Satellite freeze forecast system. Operating/troubleshooting manual

NASA Technical Reports Server (NTRS)

Martsolf, J. D. (Principal Investigator)

1983-01-01

Examples of operational procedures are given to assist users of the satellites freeze forecasting system (SFFS) in logging in on to the computer, executing the programs in the menu, logging off the computer, and setting up the automatic system. Directions are also given for displaying, acquiring, and listing satellite maps; for communicating via terminal and monitor displays; and for what to do when the SFFS doesn't work. Administrative procedures are included.

Satellite based Ocean Forecasting, the SOFT project

NASA Astrophysics Data System (ADS)

Stemmann, L.; Tintoré, J.; Moneris, S.

2003-04-01

The knowledge of future oceanic conditions would have enormous impact on human marine related areas. For such reasons, a number of international efforts are being carried out to obtain reliable and manageable ocean forecasting systems. Among the possible techniques that can be used to estimate the near future states of the ocean, an ocean forecasting system based on satellite imagery is developped through the Satelitte based Ocean ForecasTing project (SOFT). SOFT, established by the European Commission, considers the development of a forecasting system of the ocean space-time variability based on satellite data by using Artificial Intelligence techniques. This system will be merged with numerical simulation approaches, via assimilation techniques, to get a hybrid SOFT-numerical forecasting system of improved performance. The results of the project will provide efficient forecasting of sea-surface temperature structures, currents, dynamic height, and biological activity associated to chlorophyll fields. All these quantities could give valuable information on the planning and management of human activities in marine environments such as navigation, fisheries, pollution control, or coastal management. A detailed identification of present or new needs and potential end-users concerned by such an operational tool is being performed. The project would study solutions adapted to these specific needs.

The value of information as applied to the Landsat Follow-on benefit-cost analysis

NASA Technical Reports Server (NTRS)

Wood, D. B.

1978-01-01

An econometric model was run to compare the current forecasting system with a hypothetical (Landsat Follow-on) space-based system. The baseline current system was a hybrid of USDA SRS domestic forecasts and the best known foreign data. The space-based system improved upon the present Landsat by the higher spatial resolution capability of the thematic mapper. This satellite system is a major improvement for foreign forecasts but no better than SRS for domestic forecasts. The benefit analysis was concentrated on the use of Landsat Follow-on to forecast world wheat production. Results showed that it was possible to quantify the value of satellite information and that there are significant benefits in more timely and accurate crop condition information.

Weather Forecasting Systems and Methods

NASA Technical Reports Server (NTRS)

Mecikalski, John (Inventor); MacKenzie, Wayne M., Jr. (Inventor); Walker, John Robert (Inventor)

2014-01-01

A weather forecasting system has weather forecasting logic that receives raw image data from a satellite. The raw image data has values indicative of light and radiance data from the Earth as measured by the satellite, and the weather forecasting logic processes such data to identify cumulus clouds within the satellite images. For each identified cumulus cloud, the weather forecasting logic applies interest field tests to determine a score indicating the likelihood of the cumulus cloud forming precipitation and/or lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus clouds will produce precipitation and/or lighting within during the time period. Such predictions may then be used to provide a weather map thereby providing users with a graphical illustration of the areas predicted to be affected by precipitation within the time period.

Demand for satellite-provided domestic communications services up to the year 2000

NASA Technical Reports Server (NTRS)

Stevenson, S.; Poley, W.; Lekan, J.; Salzman, J. A.

1984-01-01

Three fixed service telecommunications demand assessment studies were completed for NASA by The Western Union Telegraph Company and the U.S. Telephone and Telegraph Corporation. They provided forecasts of the total U.S. domestic demand, from 1980 to the year 2000, for voice, data, and video services. That portion that is technically and economically suitable for transmission by satellite systems, both large trunking systems and customer premises services (CPS) systems was also estimated. In order to provide a single set of forecasts a NASA synthesis of the above studies was conducted. The services, associated forecast techniques, and data bases employed by both contractors were examined, those elements of each judged to be the most appropriate were selected, and new forecasts were made. The demand for voice, data, and video services was first forecast in fundamental units of call-seconds, bits/year, and channels, respectively. Transmission technology characteristics and capabilities were then forecast, and the fundamental demand converted to an equivalent transmission capacity. The potential demand for satellite-provided services was found to grow by a factor of 6, from 400 to 2400 equivalent 36 MHz satellite transponders over the 20-year period. About 80 percent of this was found to be more appropriate for trunking systems and 20 percent CPS.

Demand for satellite-provided domestic communications services up to the year 2000

NASA Astrophysics Data System (ADS)

Stevenson, S.; Poley, W.; Lekan, J.; Salzman, J. A.

1984-11-01

Three fixed service telecommunications demand assessment studies were completed for NASA by The Western Union Telegraph Company and the U.S. Telephone and Telegraph Corporation. They provided forecasts of the total U.S. domestic demand, from 1980 to the year 2000, for voice, data, and video services. That portion that is technically and economically suitable for transmission by satellite systems, both large trunking systems and customer premises services (CPS) systems was also estimated. In order to provide a single set of forecasts a NASA synthesis of the above studies was conducted. The services, associated forecast techniques, and data bases employed by both contractors were examined, those elements of each judged to be the most appropriate were selected, and new forecasts were made. The demand for voice, data, and video services was first forecast in fundamental units of call-seconds, bits/year, and channels, respectively. Transmission technology characteristics and capabilities were then forecast, and the fundamental demand converted to an equivalent transmission capacity. The potential demand for satellite-provided services was found to grow by a factor of 6, from 400 to 2400 equivalent 36 MHz satellite transponders over the 20-year period. About 80 percent of this was found to be more appropriate for trunking systems and 20 percent CPS.

JPSS application in a near real time regional numerical forecast system at CIMSS

NASA Astrophysics Data System (ADS)

Li, J.; Wang, P.; Han, H.; Zhu, F.; Schmit, T. J.; Goldberg, M.

2015-12-01

Observations from next generation of environmental sensors onboard the Suomi National Polar-Orbiting Parnership (S-NPP) and its successor, the Joint Polar Satellite System (JPSS), provide us the critical information for numerical weather forecast (NWP). How to better represent these satellite observations and how to get value added information into NWP system still need more studies. Recently scientists from Cooperative Institute of Meteorological Satellite Studies (CIMSS) at University of Wisconsin-Madison have developed a near realtime regional Satellite Data Assimilation system for Tropical storm forecasts (SDAT) (http://cimss.ssec.wisc.edu/sdat). The system is built with the community Gridpoint Statistical Interpolation (GSI) assimilation and advanced Weather Research Forecast (WRF) model. With GSI, SDAT can assimilate all operational available satellite data including GOES, AMSUA/AMSUB, HIRS, MHS, ATMS, AIRS and IASI radiances and some satellite derived products. In addition, some research products, such as hyperspectral IR retrieved temperature and moisture profiles, GOES imager atmospheric motion vector (AMV) and GOES sounder layer precipitable water (LPW), are also added into the system. Using SDAT as a research testbed, studies have been conducted to show how to improve high impact weather forecast by better handling cloud information in satellite data. Previously by collocating high spatial resolution MODIS data with hyperspectral resolution AIRS data, precise clear pixels of AIRS can be identified and some partially or thin cloud contamination from pixels can be removed by taking advantage of high spatial resolution and high accurate MODIS cloud information. The results have demonstrated that both of these strategies have greatly improved the hurricane track and intensity forecast. We recently have extended these methodologies into processing CrIS/VIIRS data. We also tested similar ideas in microwave sounders by the collocation of AMSU/MODIS and ATMS/VIIRS data. The experiments along with other SDAT progresses will be presented in the meeting.

Application of Satellite Frost Forecast Technology to Other Parts of the United States Phase II: Introduction

NASA Technical Reports Server (NTRS)

1981-01-01

The history and status of University of Michigan and University of Pennsylvania involvement in determining if P-model for front prediction used in Florida is applicable to those geographic locations is reviewed. The possibility of using the S-model to develop a satellite front forecast system that can recall the distribution of temperatures during previous freezes from a particular area and bring that cold climate climatology to bear on present forecasts is discussed as well as a proposed GOES satellite downlink system to sectionalize the data used in Florida.

Satellite fixed communications service: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

NASA Technical Reports Server (NTRS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-01-01

Voice applications, data applications, video applications, impacted baseline forecasts, market distribution model, net long haul forecasts, trunking earth station definition and costs, trunking space segment cost, trunking entrance/exit links, trunking network costs and crossover distances with terrestrial tariffs, net addressable forecasts, capacity requirements, improving spectrum utilization, satellite system market development, and the 30/20 net accessible market are considered.

Satellite fixed communications service: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

NASA Astrophysics Data System (ADS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-09-01

Voice applications, data applications, video applications, impacted baseline forecasts, market distribution model, net long haul forecasts, trunking earth station definition and costs, trunking space segment cost, trunking entrance/exit links, trunking network costs and crossover distances with terrestrial tariffs, net addressable forecasts, capacity requirements, improving spectrum utilization, satellite system market development, and the 30/20 net accessible market are considered.

Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation

NASA Astrophysics Data System (ADS)

Benedetti, A.; Morcrette, J.-J.; Boucher, O.; Dethof, A.; Engelen, R. J.; Fisher, M.; Flentje, H.; Huneeus, N.; Jones, L.; Kaiser, J. W.; Kinne, S.; Mangold, A.; Razinger, M.; Simmons, A. J.; Suttie, M.

2009-07-01

This study presents the new aerosol assimilation system, developed at the European Centre for Medium-Range Weather Forecasts, for the Global and regional Earth-system Monitoring using Satellite and in-situ data (GEMS) project. The aerosol modeling and analysis system is fully integrated in the operational four-dimensional assimilation apparatus. Its purpose is to produce aerosol forecasts and reanalyses of aerosol fields using optical depth data from satellite sensors. This paper is the second of a series which describes the GEMS aerosol effort. It focuses on the theoretical architecture and practical implementation of the aerosol assimilation system. It also provides a discussion of the background errors and observations errors for the aerosol fields, and presents a subset of results from the 2-year reanalysis which has been run for 2003 and 2004 using data from the Moderate Resolution Imaging Spectroradiometer on the Aqua and Terra satellites. Independent data sets are used to show that despite some compromises that have been made for feasibility reasons in regards to the choice of control variable and error characteristics, the analysis is very skillful in drawing to the observations and in improving the forecasts of aerosol optical depth.

A study of the economic benefits of meteorological satellite data

NASA Technical Reports Server (NTRS)

Suchman, D.; Auvine, B. A.; Hinton, B. H.

1980-01-01

Satellite data, while most useful in data poor areas, serves to fine tune forecasts in data rich areas. It consequently has a resulting significant economic benefit because, as previously stated, even one improved forecast per client per year can save each client thousands of dollars. Multiply this by several hundred clients and the dollar savings are sizeable. The great educational value which experience with satellite data gives undoubtedly leads to improved forecasts. Any type of future satellite data delivery system should take into account the needs and facilities of the user community to make it most useful.

Projected Applications of a ``Climate in a Box'' Computing System at the NASA Short-term Prediction Research and Transition (SPoRT) Center

NASA Astrophysics Data System (ADS)

Jedlovec, G.; Molthan, A.; Zavodsky, B.; Case, J.; Lafontaine, F.

2010-12-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique observations and research capabilities to the operational weather community, with a goal of improving short-term forecasts on a regional scale. Advances in research computing have lead to “Climate in a Box” systems, with hardware configurations capable of producing high resolution, near real-time weather forecasts, but with footprints, power, and cooling requirements that are comparable to desktop systems. The SPoRT Center has developed several capabilities for incorporating unique NASA research capabilities and observations with real-time weather forecasts. Planned utilization includes the development of a fully-cycled data assimilation system used to drive 36-48 hour forecasts produced by the NASA Unified version of the Weather Research and Forecasting (WRF) model (NU-WRF). The horsepower provided by the “Climate in a Box” system is expected to facilitate the assimilation of vertical profiles of temperature and moisture provided by the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua satellite. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA’s Aqua and Terra satellites provide high-resolution sea surface temperatures and vegetation characteristics. The development of MODIS normalized difference vegetation index (NVDI) composites for use within the NASA Land Information System (LIS) will assist in the characterization of vegetation, and subsequently the surface albedo and processes related to soil moisture. Through application of satellite simulators, NASA satellite instruments can be used to examine forecast model errors in cloud cover and other characteristics. Through the aforementioned application of the “Climate in a Box” system and NU-WRF capabilities, an end goal is the establishment of a real-time forecast system that fully integrates modeling and analysis capabilities developed within the NASA SPoRT Center, with benefits provided to the operational forecasting community.

Projected Applications of a "Climate in a Box" Computing System at the NASA Short-Term Prediction Research and Transition (SPoRT) Center

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Molthan, Andrew L.; Zavodsky, Bradley; Case, Jonathan L.; LaFontaine, Frank J.

2010-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique observations and research capabilities to the operational weather community, with a goal of improving short-term forecasts on a regional scale. Advances in research computing have lead to "Climate in a Box" systems, with hardware configurations capable of producing high resolution, near real-time weather forecasts, but with footprints, power, and cooling requirements that are comparable to desktop systems. The SPoRT Center has developed several capabilities for incorporating unique NASA research capabilities and observations with real-time weather forecasts. Planned utilization includes the development of a fully-cycled data assimilation system used to drive 36-48 hour forecasts produced by the NASA Unified version of the Weather Research and Forecasting (WRF) model (NU-WRF). The horsepower provided by the "Climate in a Box" system is expected to facilitate the assimilation of vertical profiles of temperature and moisture provided by the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua satellite. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA s Aqua and Terra satellites provide high-resolution sea surface temperatures and vegetation characteristics. The development of MODIS normalized difference vegetation index (NVDI) composites for use within the NASA Land Information System (LIS) will assist in the characterization of vegetation, and subsequently the surface albedo and processes related to soil moisture. Through application of satellite simulators, NASA satellite instruments can be used to examine forecast model errors in cloud cover and other characteristics. Through the aforementioned application of the "Climate in a Box" system and NU-WRF capabilities, an end goal is the establishment of a real-time forecast system that fully integrates modeling and analysis capabilities developed within the NASA SPoRT Center, with benefits provided to the operational forecasting community.

Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Limaye, Ashutosh S.; Srikishen, Jayanthi

2011-01-01

Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula s "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA s National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA s SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT s experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula.

Oregon Washington Coastal Ocean Forecast System: Real-time Modeling and Data Assimilation

NASA Astrophysics Data System (ADS)

Erofeeva, S.; Kurapov, A. L.; Pasmans, I.

2016-02-01

Three-day forecasts of ocean currents, temperature and salinity along the Oregon and Washington coasts are produced daily by a numerical ROMS-based ocean circulation model. NAM is used to derive atmospheric forcing for the model. Fresh water discharge from Columbia River, Fraser River, and small rivers in Puget Sound are included. The forecast is constrained by open boundary conditions derived from the global Navy HYCOM model and once in 3 days assimilation of recent data, including HF radar surface currents, sea surface temperature from the GOES satellite, and SSH from several satellite altimetry missions. 4-dimensional variational data assimilation is implemented in 3-day time windows using the tangent linear and adjoint codes developed at OSU. The system is semi-autonomous - all the data, including NAM and HYCOM fields are automatically updated, and daily operational forecast is automatically initiated. The pre-assimilation data quality control and post-assimilation forecast quality control require the operator's involvement. The daily forecast and 60 days of hindcast fields are available for public on opendap. As part of the system model validation plots to various satellites and SEAGLIDER are also automatically updated and available on the web (http://ingria.coas.oregonstate.edu/rtdavow/). Lessons learned in this pilot real-time coastal ocean forecasting project help develop and test metrics for forecast skill assessment for the West Coast Operational Forecast System (WCOFS), currently at testing and development phase at the National Oceanic and Atmospheric Administration (NOAA).

Comparison of Observation Impacts in Two Forecast Systems using Adjoint Methods

NASA Technical Reports Server (NTRS)

Gelaro, Ronald; Langland, Rolf; Todling, Ricardo

2009-01-01

An experiment is being conducted to compare directly the impact of all assimilated observations on short-range forecast errors in different operational forecast systems. We use the adjoint-based method developed by Langland and Baker (2004), which allows these impacts to be efficiently calculated. This presentation describes preliminary results for a "baseline" set of observations, including both satellite radiances and conventional observations, used by the Navy/NOGAPS and NASA/GEOS-5 forecast systems for the month of January 2007. In each system, about 65% of the total reduction in 24-h forecast error is provided by satellite observations, although the impact of rawinsonde, aircraft, land, and ship-based observations remains significant. Only a small majority (50- 55%) of all observations assimilated improves the forecast, while the rest degrade it. It is found that most of the total forecast error reduction comes from observations with moderate-size innovations providing small to moderate impacts, not from outliers with very large positive or negative innovations. In a global context, the relative impacts of the major observation types are fairly similar in each system, although regional differences in observation impact can be significant. Of particular interest is the fact that while satellite radiances have a large positive impact overall, they degrade the forecast in certain locations common to both systems, especially over land and ice surfaces. Ongoing comparisons of this type, with results expected from other operational centers, should lead to more robust conclusions about the impacts of the various components of the observing system as well as about the strengths and weaknesses of the methodologies used to assimilate them.

Integrating Satellite Measurements from Polar-orbiting instruments into Smoke Disperson Forecasts

NASA Astrophysics Data System (ADS)

Smith, N.; Pierce, R. B.; Barnet, C.; Gambacorta, A.; Davies, J. E.; Strabala, K.

2015-12-01

The IDEA-I (Infusion of Satellite Data into Environmental Applications-International) is a real-time system that currently generates trajectory-based forecasts of aerosol dispersion and stratospheric intrusions. Here we demonstrate new capabilities that use satellite measurements from the Joint Polar Satellite System (JPSS) Suomi-NPP (S-NPP) instruments (operational since 2012) in the generation of trajectory-based predictions of smoke dispersion from North American wildfires. Two such data products are used, namely the Visible Infrared Imaging Radiometer Suite (VIIRS) Aerosol Optical Depth (AOD) and the combined Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) NOAA-Unique CrIS-ATMS Processing System (NUCAPS) carbon monoxide (CO) retrievals. The latter is a new data product made possible by the release of full spectral-resolution CrIS measurements since December 2014. Once NUCAPS CO becomes operationally available it will be used in real-time applications such as IDEA-I along with VIIRS AOD and meteorological forecast fields to support National Weather Service (NWS) Incident Meteorologist (IMET) and air quality management decision making. By combining different measurements, the information content of the IDEA-I transport and dispersion forecast is improved within the complex terrain features that dominate the Western US and Alaska. The primary user community of smoke forecasts is the Western regions of the National Weather Service (NWS) and US Environmental Protection Agency (EPA) due to the significant impacts of wildfires in these regions. With this we demonstrate the quality of the smoke dispersion forecasts that can be achieved by integrating polar-orbiting satellite measurements with forecast models to enable on-site decision support services for fire incident management teams and other real-time air quality agencies.

JPSS Preparations at the Satellite Proving Ground for Marine, Precipitation, and Satellite Analysis

NASA Astrophysics Data System (ADS)

Folmer, M. J.; Berndt, E.; Clark, J.; Orrison, A.; Kibler, J.; Sienkiewicz, J. M.; Nelson, J. A., Jr.; Goldberg, M.

2016-12-01

The National Oceanic and Atmospheric Administration (NOAA) Satellite Proving Ground (PG) for Marine, Precipitation, and Satellite Analysis (MPS) has been demonstrating and evaluating Suomi National Polar-orbiting Partnership (S-NPP) products along with other polar-orbiting satellite platforms in preparation for the Joint Polar Satellite System - 1 (JPSS-1) launch in March 2017. The first S-NPP imagery was made available to the MPS PG during the evolution of Hurricane Sandy in October 2012 and has since been popular in operations. Since this event the MPS PG Satellite Liaison has been working with forecasters on ways to integrate single-channel and multispectral imagery from the Visible Infrared Imaging Radiometer Suite (VIIRS), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Advanced Very High Resolution Radiometer (AVHRR)into operations to complement numerical weather prediction and geostationary satellite savvy National Weather Service (NWS) National Centers. Additional unique products have been introduced to operations to address specific forecast challenges, including the Cooperative Institute for Research in the Atmosphere (CIRA) Layered Precipitable Water, the National Environmental Satellite, Data, and Information Service (NESDIS) Snowfall Rate product, NOAA Unique Combined Atmospheric Processing System (NUCAPS) Soundings, ozone products from the Atmospheric Infrared Sounder (AIRS), Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS), and Infrared Atmospheric Sounding Interferometer (IASI). In addition, new satellite domains have been created to provide forecasters at the NWS Ocean Prediction Center and Weather Prediction Center with better quality imagery at high latitudes. This has led to research projects that are addressing forecast challenges such as tropical to extratropical transition and explosive cyclogenesis. This presentation will provide examples of how the MPS PG has been introducing and integrating these products into operations to help solve these forecast challenges.

Using Satellite Data and Land Surface Models to Monitor and Forecast Drought Conditions in Africa and Middle East

NASA Astrophysics Data System (ADS)

Arsenault, K. R.; Shukla, S.; Getirana, A.; Peters-Lidard, C. D.; Kumar, S.; McNally, A.; Zaitchik, B. F.; Badr, H. S.; Funk, C. C.; Koster, R. D.; Narapusetty, B.; Jung, H. C.; Roningen, J. M.

2017-12-01

Drought and water scarcity are among the important issues facing several regions within Africa and the Middle East. In addition, these regions typically have sparse ground-based data networks, where sometimes remotely sensed observations may be the only data available. Long-term satellite records can help with determining historic and current drought conditions. In recent years, several new satellites have come on-line that monitor different hydrological variables, including soil moisture and terrestrial water storage. Though these recent data records may be considered too short for the use in identifying major droughts, they do provide additional information that can better characterize where water deficits may occur. We utilize recent satellite data records of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and the European Space Agency's Advanced Scatterometer (ASCAT) soil moisture retrievals. Combining these records with land surface models (LSMs), NASA's Catchment and the Noah Multi-Physics (MP), is aimed at improving the land model states and initialization for seasonal drought forecasts. The LSMs' total runoff is routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics, which can provide an additional means of validation against in situ streamflow data. The NASA Land Information System (LIS) software framework drives the LSMs and HyMAP and also supports the capability to assimilate these satellite retrievals, such as soil moisture and TWS. The LSMs are driven for 30+ years with NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS/UCSB Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall dataset. The seasonal water deficit forecasts are generated using downscaled and bias-corrected versions of NASA's Goddard Earth Observing System Model (GEOS-5), and NOAA's Climate Forecast System (CFSv2) forecasts. These combined satellite and model records and forecasts are intended for use in different decision support tools, like the Famine Early Warning Systems Network (FEWS NET) and the Middle East-North Africa (MENA) Regional Drought Management System, for aiding and forecasting in water and food insecure regions.

Improving Societal Benefit Areas from Applications Enhanced by the Joint Polar Satellite System

NASA Astrophysics Data System (ADS)

Goldberg, M.

2016-12-01

Applications of satellite data are paramount to transform science and technology to product and services which are used in critical decision making for societal benefits. For the satellite community, good representations of technology are the satellite sensors, while science provides the instrument calibration and derived geophysical parameters. Weather forecasting is an application of the science and technology provided by remote sensing satellites. The Joint Polar Satellite System, which includes the Suomi National Polar-orbiting Partnership (S-NPP) provides formidable science and technology to support many applications and includes support to 1) weather forecasting - data from the JPSS Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) are used to forecast weather events out to 7 days - nearly 85% of all data used in weather forecasting are from polar orbiting satellites; 2) environmental monitoring -data from the JPSS Visible Infrared Imager Radiometer Suite (VIIRS) are used to monitor the environment including the health of coastal ecosystems, drought conditions, fire, smoke, dust, snow and ice, and the state of oceans, including sea surface temperature and ocean color; and 3) climate monitoring - data from JPSS instruments, including OMPS and CERES will provide continuity to climate data records established using NOAA POES and NASA Earth Observing System (EOS) satellite observations. To bridge the gap between products and applications, the JPSS Program has established a proving ground program to optimize the use of JPSS data with other data sources to improve key products and services. A number of operational and research applications will be presented along with how the data and applications support a large number of societal benefit areas of the Global Earth Observation Systems of Systems (GEOSS).

A near real time regional JPSS and GOES-R data assimilation system for high impact weather research and applications

NASA Astrophysics Data System (ADS)

Li, J.; Wang, P.; Han, H.; Schmit, T. J.

2014-12-01

JPSS and GOES-R observations play important role in numerical weather prediction (NWP). However, how to best represent the information from satellite observations and how to get value added information from these satellite data into regional NWP models, including both radiance and derived products, still need investigations. In order to enhance the applications of JPSS and GOES-R data in regional NWP for high impact weather forecasts, scientists from Cooperative Institute of Meteorological Satellite Studies (CIMSS) at University of Wisconsin-Madison have recently developed a near realtime regional Satellite Data Assimilation system for Tropical storm forecasts (SDAT) (http://cimss.ssec.wisc.edu/sdat). The system consists of the community Gridpoint Statistical Interpolation (GSI) assimilation system and the advanced Weather Research Forecast (WRF) model. In addition to assimilate GOES, AMSUA/AMSUB, HIRS, MHS, ATMS (Suomi-NPP), AIRS and IASI radiances, the SDAT is also able to assimilate satellite-derived products such as hyperspectral IR retrieved temperature and moisture profiles, total precipitable water (TPW), GOES Sounder (and future GOES-R) layer precipitable water (LPW) and GOES Imager atmospheric motion vector (AMV) products into the system. Real time forecasted GOES infrared (IR) images simulated from SDAT output have also been part of the SDAT system for applications and forecast evaluations. To set up the system parameters, a series of experiments have been carried out to test the impacts of different initialization schemes, including different background error matrix, different NCEP global model date sets, and different WRF model horizontal resolutions. Using SDAT as a research testbed, researches have been conducted for different satellite data impacts study, as well as different techniques for handling clouds in radiance assimilation. Since the fall of 2013, the SDAT system has been running in near real time. The results from historical cases and 2014 hurricane season cases will be compared with the operational GFS and HWRF, and presented at the meeting.

Telecommunication service markets through the year 2000 in relation to millimeter wave satellite systems

NASA Technical Reports Server (NTRS)

Stevenson, S. M.

1979-01-01

NASA is currently conducting a series of millimeter wave satellite system market studies to develop 30/20 GHz satellite system concepts that have commercial potential. Four contractual efforts were undertaken: two parallel and independent system studies and two parallel and independent market studies. The marketing efforts are focused on forecasting the total domestic demand for long haul telecommunications services for the 1980-2000 period. Work completed to date and reported in this paper include projections of: geographical distribution of traffic; traffic volume as a function of urban area size; and user identification and forecasted demand.

Sentinels in the Sky: Weather Satellites.

ERIC Educational Resources Information Center

Haynes, Robert

This publication describes forecasting weather activity using satellites. Information is included on the development of weather satellites, the National Oceanic and Atmospheric Administration (NOAA) Satellite System (including the polar-orbiting satellites), and the Geostationary Operational Environmental Satellite (GOES). The publication…

Some economic benefits of a synchronous earth observatory satellite

NASA Technical Reports Server (NTRS)

Battacharyya, R. K.; Greenberg, J. S.; Lowe, D. S.; Sattinger, I. J.

1974-01-01

An analysis was made of the economic benefits which might be derived from reduced forecasting errors made possible by data obtained from a synchronous satellite system which can collect earth observation and meteorological data continuously and on demand. User costs directly associated with achieving benefits are included. In the analysis, benefits were evaluated which might be obtained as a result of improved thunderstorm forecasting, frost warning, and grain harvest forecasting capabilities. The anticipated system capabilities were used to arrive at realistic estimates of system performance on which to base the benefit analysis. Emphasis was placed on the benefits which result from system forecasting accuracies. Benefits from improved thunderstorm forecasts are indicated for the construction, air transportation, and agricultural industries. The effects of improved frost warning capability on the citrus crop are determined. The benefits from improved grain forecasting capability are evaluated in terms of both U.S. benefits resulting from domestic grain distribution and U.S. benefits from international grain distribution.

Weather Prediction Improvement Using Advanced Satellite Technology

NASA Technical Reports Server (NTRS)

Einaudi, Franco; Uccellini, L.; Purdom, J.; Rogers, D.; Gelaro, R.; Dodge, J.; Atlas, R.; Lord, S.

2001-01-01

We discuss in this paper some of the problems that exist today in the fall utilization of satellite data to improve weather forecasts and we propose specific recommendations to solve them. This discussion can be viewed as an aspect of the general debate on how best to organize the transition from research to operational satellites and how to evaluate the impact of a research instrument on numerical weather predictions. A method for providing this transition is offered by the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP). This mission will bridge the time between the present NOAA and Department of Defense (DOD) polar orbiting missions and the initiation of the converged NPOESS series and will evaluate some of the Earth Observing System (EOS) instruments as appropriate for operational missions. Thus, this mission can be viewed as an effort to meet the operational requirements of NOAA and DOD and the research requirements of NASA. More generally, however, it can be said that the process of going from the conception of new, more advanced instruments to their operational implementation and full utilization by the weather forecast communities is not optimal. Instruments developed for research purposes may have insufficient funding to explore their potential operational capabilities. Furthermore, instrument development programs designed for operational satellites typically have insufficient funding for assimilation algorithms needed to transform the satellite observations into data that can be used by sophisticated global weather forecast models. As a result, years often go by before satellite data are efficiently used for operational forecasts. NASA and NOAA each have unique expertise in the design of satellite instruments, their use for basic and applied research and their utilization in weather and climate research. At a time of limited resources, the two agencies must combine their efforts to work toward common goals of full utilization of satellite data. This is a challenge that requires the assimilation of myriad new data into increasingly sophisticated numerical forecast models that run on increasingly sophisticated computer systems. In section II, we briefly outline the impact of satellite data on the quality of the National Centers for Environmental Prediction (NCEP) forecasts. In section III, we describe the present status of the utilization of satellite data in NCEP models and the challenges that lie ahead. In section IV, we propose solutions whose goals are summarized in section V.

Short-Term Prediction Research and Transition (SPoRT) Center: Transitioning Satellite Data to Operations

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley

2012-01-01

The Short-term Prediction Research and Transition (SPoRT) Center located at NASA Marshall Space Flight Center has been conducting testbed activities aimed at transitioning satellite products to National Weather Service operational end users for the last 10 years. SPoRT is a NASA/NOAA funded project that has set the bar for transition of products to operational end users through a paradigm of understanding forecast challenges and forecaster needs, displaying products in end users decision support systems, actively assessing the operational impact of these products, and improving products based on forecaster feedback. Aiming for quality partnerships rather than a large quantity of data users, SPoRT has become a community leader in training operational forecasters on the use of up-and-coming satellite data through the use of legacy instruments and proxy data. Traditionally, SPoRT has supplied satellite imagery and products from NASA instruments such as the Moderate-resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS). However, recently, SPoRT has been funded by the GOES-R and Joint Polar Satellite System (JPSS) Proving Grounds to accelerate the transition of selected imagery and products to help improve forecaster awareness of upcoming operational data from the Visible Infrared Imager Radiometer Suite (VIIRS), Cross-track Infrared Sounder (CrIS), Advanced Baseline Imager (ABI), and Geostationary Lightning Mapper (GLM). This presentation provides background on the SPoRT Center, the SPoRT paradigm, and some example products that SPoRT is excited to work with forecasters to evaluate.

Current and future data assimilation development in the Copernicus Atmosphere Monitoring Service

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Ades, M.; Agusti-panareda, A.; Flemming, J.; Inness, A.; Kipling, Z.; Parrington, M.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The system assimilates observations from more than 60 satellite sensors to constrain both the meteorology and the atmospheric composition species. While an operational forecasting system needs to be robust and reliable, it also needs to stay state-of-the-art to provide the best possible forecasts. Continuous development is therefore an important component of the CAMS systems. We will present on-going efforts on improving the 4D-Var data assimilation system, such as using ensemble data assimilation to improve the background error covariances and more accurate use of satellite observations. We will also outline plans for including emissions in the daily CAMS analyses, which is an area where research activities have a large potential to feed into operational applications.

On the reliable use of satellite-derived surface water products for global flood monitoring

NASA Astrophysics Data System (ADS)

Hirpa, F. A.; Revilla-Romero, B.; Thielen, J.; Salamon, P.; Brakenridge, R.; Pappenberger, F.; de Groeve, T.

2015-12-01

Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response management. To this end, real-time flood forecasting and satellite-based detection systems have been developed at global scale. However, due to the limited availability of up-to-date ground observations, the reliability of these systems for real-time applications have not been assessed in large parts of the globe. In this study, we performed comparative evaluations of the commonly used satellite-based global flood detections and operational flood forecasting system using 10 major flood cases reported over three years (2012-2014). Specially, we assessed the flood detection capabilities of the near real-time global flood maps from the Global Flood Detection System (GFDS), and from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the operational forecasts from the Global Flood Awareness System (GloFAS) for the major flood events recorded in global flood databases. We present the evaluation results of the global flood detection and forecasting systems in terms of correctly indicating the reported flood events and highlight the exiting limitations of each system. Finally, we propose possible ways forward to improve the reliability of large scale flood monitoring tools.

Impact of Lidar Wind Sounding on Mesoscale Forecast

NASA Technical Reports Server (NTRS)

Miller, Timothy L.; Chou, Shih-Hung; Goodman, H. Michael (Technical Monitor)

2001-01-01

An Observing System Simulation Experiment (OSSE) was conducted to study the impact of airborne lidar wind sounding on mesoscale weather forecast. A wind retrieval scheme, which interpolates wind data from a grid data system, simulates the retrieval of wind profile from a satellite lidar system. A mesoscale forecast system based on the PSU/NCAR MM5 model is developed and incorporated the assimilation of the retrieved line-of-sight wind. To avoid the "identical twin" problem, the NCEP reanalysis data is used as our reference "nature" atmosphere. The simulated space-based lidar wind observations were retrieved by interpolating the NCEP values to the observation locations. A modified dataset obtained by smoothing the NCEP dataset was used as the initial state whose forecast was sought to be improved by assimilating the retrieved lidar observations. Forecasts using wind profiles with various lidar instrument parameters has been conducted. The results show that to significantly improve the mesoscale forecast the satellite should fly near the storm center with large scanning radius. Increasing lidar firing rate also improves the forecast. Cloud cover and lack of aerosol degrade the quality of the lidar wind data and, subsequently, the forecast.

Satellites, tweets, forecasts: the future of flood disaster management?

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Kalas, Milan; Lorini, Valerio; Wania, Annett; Pappenberger, Florian; Salamon, Peter; Ramos, Maria Helena; Cloke, Hannah; Castillo, Carlos

2017-04-01

Floods have devastating effects on lives and livelihoods around the world. Structural flood defence measures such as dikes and dams can help protect people. However, it is the emerging science and technologies for flood disaster management and preparedness, such as increasingly accurate flood forecasting systems, high-resolution satellite monitoring, rapid risk mapping, and the unique strength of social media information and crowdsourcing, that are most promising for reducing the impacts of flooding. Here, we describe an innovative framework which integrates in real-time two components of the Copernicus Emergency mapping services, namely the European Flood Awareness System and the satellite-based Rapid Mapping, with new procedures for rapid risk assessment and social media and news monitoring. The integrated framework enables improved flood impact forecast, thanks to the real-time integration of forecasting and monitoring components, and increases the timeliness and efficiency of satellite mapping, with the aim of capturing flood peaks and following the evolution of flooding processes. Thanks to the proposed framework, emergency responders will have access to a broad range of timely and accurate information for more effective and robust planning, decision-making, and resource allocation.

Application of polar orbiter products in weather forecasting using open source tools and open standards

NASA Astrophysics Data System (ADS)

Plieger, Maarten; de Vreede, Ernst

2015-04-01

EUMETSAT disseminates data for a number of polar satellites. At KNMI these data are not fully used for operational weather forecasting mainly because of the irregular coverage and lack of tools for handling these different types of data and products. For weather forecasting there is a lot of interest in the application of products from these polar orbiters. One of the key aspects is the high-resolution of these products, which can complement the information provided by numerical weather forecasts. Another advantage over geostationary satellites is the high coverage at higher latitudes and lack of parallax. Products like the VIIRS day-night band offer many possibilities for this application. This presentation will describe a project that aims to make available a number of products from polar satellites to the forecasting operation. The goal of the project is to enable easy and timely access to polar orbiter products and enable combined presentations of satellite imagery with model data. The system will be able to generate RGB composites (“false colour images”) for operational use. The system will be built using open source components and open standards. Pytroll components are used for data handling, reprojection and derived product generation. For interactive presentation of imagery the browser based ADAGUC WMS viewer component is used. Image generation is done by ADAGUC server components, which provide OGC WMS services. Polar satellite products are stored as true color RGBA data in the NetCDF file format, the satellite swaths are stored as regular grids with their own custom geographical projection. The ADAGUC WMS system is able to reproject, render and combine these data in a webbrowser interactively. Results and lessons learned will be presented at the conference.

Forecasting Tools Point to Fishing Hotspots

NASA Technical Reports Server (NTRS)

2009-01-01

Private weather forecaster WorldWinds Inc. of Slidell, Louisiana has employed satellite-gathered oceanic data from Marshall Space Flight Center to create a service that is every fishing enthusiast s dream. The company's FishBytes system uses information about sea surface temperature and chlorophyll levels to forecast favorable conditions for certain fish populations. Transmitting the data to satellite radio subscribers, FishBytes provides maps that guide anglers to the areas they are most likely to make their favorite catch.

Forecasting Global Horizontal Irradiance Using the LETKF and a Combination of Advected Satellite Images and Sparse Ground Sensors

NASA Astrophysics Data System (ADS)

Harty, T. M.; Lorenzo, A.; Holmgren, W.; Morzfeld, M.

2017-12-01

The irradiance incident on a solar panel is the main factor in determining the power output of that panel. For this reason, accurate global horizontal irradiance (GHI) estimates and forecasts are critical when determining the optimal location for a solar power plant, forecasting utility scale solar power production, or forecasting distributed, behind the meter rooftop solar power production. Satellite images provide a basis for producing the GHI estimates needed to undertake these objectives. The focus of this work is to combine satellite derived GHI estimates with ground sensor measurements and an advection model. The idea is to use accurate but sparsely distributed ground sensors to improve satellite derived GHI estimates which can cover large areas (the size of a city or a region of the United States). We use a Bayesian framework to perform the data assimilation, which enables us to produce irradiance forecasts and associated uncertainties which incorporate both satellite and ground sensor data. Within this framework, we utilize satellite images taken from the GOES-15 geostationary satellite (available every 15-30 minutes) as well as ground data taken from irradiance sensors and rooftop solar arrays (available every 5 minutes). The advection model, driven by wind forecasts from a numerical weather model, simulates cloud motion between measurements. We use the Local Ensemble Transform Kalman Filter (LETKF) to perform the data assimilation. We present preliminary results towards making such a system useful in an operational context. We explain how localization and inflation in the LETKF, perturbations of wind-fields, and random perturbations of the advection model, affect the accuracy of our estimates and forecasts. We present experiments showing the accuracy of our forecasted GHI over forecast-horizons of 15 mins to 1 hr. The limitations of our approach and future improvements are also discussed.

Satellite provided customer premise services: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

NASA Technical Reports Server (NTRS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-01-01

Voice applications, data applications, video applications, impacted baseline forecasts, market distribution, potential CPS (customers premises services) user classes, net long haul forecasts, CPS cost analysis, overall satellite forecast, CPS satellite market, Ka-band CPS satellite forecast, nationwide traffic distribution model, and intra-urban topology are discussed.

Satellite provided customer premise services: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

NASA Astrophysics Data System (ADS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-08-01

Voice applications, data applications, video applications, impacted baseline forecasts, market distribution, potential CPS (customers premises services) user classes, net long haul forecasts, CPS cost analysis, overall satellite forecast, CPS satellite market, Ka-band CPS satellite forecast, nationwide traffic distribution model, and intra-urban topology are discussed.

Improving the Transition of Earth Satellite Observations from Research to Operations

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Lapenta, William M.; Jedlovec, Gary J.

2004-01-01

There are significant gaps between the observations, models, and decision support tools that make use of new data. These challenges include: 1) Decreasing the time to incorporate new satellite data into operational forecast assimilation systems, 2) Blending in-situ and satellite observing systems to produce the most accurate and comprehensive data products and assessments, 3) Accelerating the transition from research to applications through national test beds, field campaigns, and pilot demonstrations, and 4) Developing the partnerships and organizational structures to effectively transition new technology into operations. At the Short-term Prediction Research and Transition (SPORT) Center in Huntsville, Alabama, a NASA-NOAA-University collaboration has been developed to accelerate the infusion of NASA Earth science observations, data assimilation and modeling research into NWS forecast operations and decision-making. The SPoRT Center research focus is to improve forecasts through new observation capability and the regional prediction objectives of the US Weather Research Program dealing with 0-1 day forecast issues such as convective initiation and 24-hr quantitative precipitation forecasting. The near real-time availability of high-resolution experimental products of the atmosphere, land, and ocean from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Infrared Spectroradiometer (AIRS), and lightning mapping systems provide an opportunity for science and algorithm risk reduction, and for application assessment prior to planned observations from the next generation of operational low Earth orbiting and geostationary Earth orbiting satellites. This paper describes the process for the transition of experimental products into forecast operations, current products undergoing assessment by forecasters, and plans for the future. The SPoRT Web page is at (http://www.ghcc.msfc.nasa.gov/sport).

NASA Launches NOAA Weather Satellite to Improve Forecasts

NASA Image and Video Library

2017-11-18

Early on the morning of Saturday, Nov. 18, NASA successfully launched for the National Oceanic and Atmospheric Administration (NOAA) the first in a series of four advanced polar-orbiting satellites, equipped with next-generation technology and designed to improve the accuracy of U.S. weather forecasts out to seven days. The Joint Polar Satellite System-1 (JPSS-1) lifted off on a United Launch Alliance Delta II rocket from Vandenberg Air Force Base on California’s central coast. JPSS-1 data will improve weather forecasting and help agencies involved with post-storm recovery by visualizing storm damage and the geographic extent of power outages.

Initialization of a mesoscale model for April 10, 1979, using alternative data sources

NASA Technical Reports Server (NTRS)

Kalb, M. W.

1984-01-01

A 35 km grid limited area mesoscale model was initialized with high density SESAME radiosonde data and high density TIROS-N satellite temperature profiles for April 10, 1979. These data sources were used individually and with low level wind fields constructed from surface wind observations. The primary objective was to examine the use of satellite temperature data for initializing a mesoscale model by comparing the forecast results with similar experiments employing radiosonde data. The impact of observed low level winds on the model forecasts was also investigated with experiments varying the method of insertion. All forecasts were compared with each other and with mesoscale observations for precipitation, mass and wind structure. Several forecasts produced convective precipitation systems with characteristics satisfying criteria for a mesoscale convective complex. High density satellite temperature data and balanced winds can be used in a mesoscale model to produce forecasts which verify favorably with observations.

NOAAPORT

Science.gov Websites

satellite communications utilizing the C-band. It's primary purpose is for providing internal communications services. The NOAAPORT satellite communications system is operated by GTE Corp., under contract to the NWS . The system uses satellite transmitting (i.e. "uplink") equipment at NWS forecast offices

A prospective approach to coastal geography from satellite. [technological forecasting

NASA Technical Reports Server (NTRS)

Munday, J. C., Jr.

1981-01-01

A forecasting protocol termed the "prospective approach' was used to examine probable futures relative to coastal applications of satellite data. Significant variables include the energy situation, the national economy, national Earth satellite programs, and coastal zone research, commercial activity, and regulatory activity. Alternative scenarios for the period until 1986 are presented. Possible response by state/local remote sensing centers include operational applications for users, input to geo-base information systems (GIS), development of decision-making algorithms using GIS data, and long term research programs for coastal management using merged satellite and traditional data.

Improved Use of Satellite Imagery to Forecast Hurricanes

NASA Technical Reports Server (NTRS)

Louis, Jean-Francois

2001-01-01

This project tested a novel method that uses satellite imagery to correct phase errors in the initial state for numerical weather prediction, applied to hurricane forecasts. The system was tested on hurricanes Guillermo (1997), Felicia (1997) and Iniki (1992). We compared the performance of the system with and without phase correction to a procedure that uses bogus data in the initial state, similar to current operational procedures. The phase correction keeps the hurricane on track in the analysis and is far superior to a system without phase correction. Compared to operational procedure, phase correction generates somewhat worse 3-day forecast of the hurricane track, but better forecast of intensity. It is believed that the phase correction module would work best in the context of 4-dimensional variational data assimilation. Very little modification to 4DVar would be required.

Integrating Polar-Orbiting Products into the Forecast Routine for Explosive Cyclogenesis and Extratropical Transition

NASA Astrophysics Data System (ADS)

Folmer, M. J.; Berndt, E.; Malloy, K.; Mazur, K.; Sienkiewicz, J. M.; Phillips, J.; Goldberg, M.

2017-12-01

The Joint Polar Satellite System (JPSS) was added to the Satellite Proving Ground for Marine, Precipitation, and Satellite Analysis in late 2012, just in time to introduce forecasters to the very high-resolution imagery available from the Suomi-National Polar Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) instrument when observing and forecasting Hurricane Sandy (2012). Since that time, more polar products have been introduced to the forecast routines at the National Weather Service (NWS) Ocean Prediction Center (OPC), Weather Prediction Center (WPC), Tropical Analysis and Forecast Branch (TAFB) of the National Hurricane Center (NHC), and the Satellite Analysis Branch (SAB) of the National Environmental Satellite, Data, and Information Service (NESDIS). These new data sets have led to research projects at the OPC and TAFB that have specifically been looking into the early identification of stratospheric intrusions that lead to explosive cyclogenesis or extratropical transition of tropical cyclones. Currently NOAA Unique CrIS/ATMS Processing System (NUCAPS) temperature and moisture soundings are available in AWIPS-II as a point-based display. Traditionally soundings are used to anticipate and forecast severe convection, however unique and valuable information can be gained from soundings for other forecasting applications, such as extratropical transition, especially in data sparse regions. Additional research has been conducted to look at how JPSS CrIS/ATMS NUCAPS soundings might help forecasters identify the pre-extratropical transition or pre-explosive cyclogenesis environments, leading to earlier diagnosis and better public advisories. CrIS/ATMS NUCAPS soundings, IASI and NUCAPS ozone products, NOAA G-IV GPS dropwindsondes, the Air Mass RGB, and single water vapor channels have been analyzed to look for the precursors to these high impact events. This presentation seeks to show some early analysis and potential uses of the polar-orbiting datasets to compliment the geostationary imagery and therefore lead to earlier identification and possible warnings.

Dissemination and Use of NPOESS Data in AWIPS II

NASA Technical Reports Server (NTRS)

Jedlovec, Gary; Burks, Jason

2008-01-01

Real-time satellite information provides one of many data sources used by NWS forecast offices to diagnose current weather conditions and to assist in short-term forecast preparation. While GOES satellite data provides relatively coarse spatial resolution coverage of the continental U.S. on a 10-15 minute repeat cycle, polar orbiting data has the potential to provide snapshots of weather conditions at high-resolution in many spectral channels. The multispectral polar orbiting satellite capabilities allow for the derivation of image and sounding products not available from geostationary orbit. The utility of these polar orbiting measurements to forecasters has been demonstrated with NASA EOS observations as part of the Short-term Prediction and Research Transition (SPORT) program at Marshall Space Flight Center. SPORT scientists have been providing real-time MODIS data to NWS forecasters on an experimental basis to address a variety of short-term weather forecasting problems since 2003. The launch of the NPOESS Preparatory Project (NPP) satellite in 2009 will extend the continuity of high-resolution data provided by the NASA EOS satellites into future operational weather systems. The NPP data will be available in a timeframe consistent with the early installation of the next generation Advanced Weather Information Processing System (AWIPS) under development by Raytheon for the NWS. The AWIPS II system will be a JAVA-based decision support system which preserves the functionality of the existing systems and offers unique development opportunities for new data sources and applications in the Service Orientated Architecture (SOA) environment. The poster will highlight some of the advanced observing and display- capabilities of these new systems such as plug-ins for NASA and NPP datasets, and the development of local applications which are not well handled in the current AWIPS (e.g., 3D displays of LMA data, generation and display of 3-channel color composites, etc.).

Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center

NASA Astrophysics Data System (ADS)

Molthan, A.; Limaye, A. S.

2011-12-01

Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula's "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA's National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA's SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT's experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula. This presentation will provide an overview of these activities from a scientific and cloud computing applications perspective, identifying the strengths and weaknesses for deploying each project within an IaaS environment, and ways to collaborate with the Nebula or other cloud-user communities to collaborate on projects as they go forward.

Satellite freeze forecast system

NASA Technical Reports Server (NTRS)

Martsolf, J. D. (Principal Investigator)

1983-01-01

Provisions for back-up operations for the satellite freeze forecast system are discussed including software and hardware maintenance and DS/1000-1V linkage; troubleshooting; and digitized radar usage. The documentation developed; dissemination of data products via television and the IFAS computer network; data base management; predictive models; the installation of and progress towards the operational status of key stations; and digital data acquisition are also considered. The d addition of dew point temperature into the P-model is outlined.

Forecasts for NOAA Marine Sanctuaries

Science.gov Websites

/Forecast The Gray's Reef Sea Turtle Satellite Tagging Project utilizes satellite transmitter tags to Synopsis/Forecast(0-20nm) Synopsis/Forecast(20-60nm) The Gray's Reef Sea Turtle Satellite Tagging Project utilizes satellite transmitter tags to monitor adult and juvenile loggerhead sea click image for more

Applications systems verification and transfer project. Volume 4: Operational applications of satellite snow cover observations. Colorado Field Test Center

NASA Technical Reports Server (NTRS)

Shafer, B. A.; Leaf, C. F.; Danielson, J. A.; Moravec, G. F.

1981-01-01

The study was conducted on six watersheds ranging in size from 277 km to 3460 km in the Rio Grande and Arkansas River basins of southwestern Colorado. Six years of satellite data in the period 1973-78 were analyzed and snowcover maps prepared for all available image dates. Seven snowmapping techniques were explored; the photointerpretative method was selected as the most accurate. Three schemes to forecast snowmelt runoff employing satellite snowcover observations were investigated. They included a conceptual hydrologic model, a statistical model, and a graphical method. A reduction of 10% in the current average forecast error is estimated when snowcover data in snowmelt runoff forecasting is shown to be extremely promising. Inability to obtain repetitive coverage due to the 18 day cycle of LANDSAT, the occurrence of cloud cover and slow image delivery are obstacles to the immediate implementation of satellite derived snowcover in operational streamflow forecasting programs.

Efforts in assimilating Indian satellite data in the NGFS and monitoring of their quality

NASA Astrophysics Data System (ADS)

Prasad, V. S.; Singh, Sanjeev Kumar

2016-05-01

Megha-Tropiques (MT) is an Indo-French Joint Satellite Mission, launched on 12 October 2011. MT-SAPHIR is a sounding instrument with 6 channels near the absorption band of water vapor at 183 GHz, for studying the water cycle and energy exchanges in the tropics. The main objective of this mission is to understand the life cycle of convective systems that influence the tropical weather and climate and their role in associated energy and moisture budget of the atmosphere in tropical regions. India also has a prestigious space programme and has launched the INSAT-3D satellite on 26 July 2013 which has an atmospheric sounder for the first time along with improved VHRR imager. NCMRWF (National Centre for Medium Range Weather Forecasting) is regularly receiving these new datasets and also making changes to its Global Data Assimilation Forecasting (GDAF) system from time-to-time to assimilate these new datasets. A well planned strategy involving various steps such as monitoring of data quality, development of observation operator and quality control procedures, and finally then studying its impact on forecasts is developed to include new observations in global data analysis system. By employing this strategy observations having positive impact on forecast quality such as MT-SAPHIR, and INSAT-3D Clear Sky Radiance (CSR) products are identified and being assimilated in the Global Data Assimilation and Forecasting (GDAF) system.

Remote Sensing and River Discharge Forecasting for Major Rivers in South Asia (Invited)

NASA Astrophysics Data System (ADS)

Webster, P. J.; Hopson, T. M.; Hirpa, F. A.; Brakenridge, G. R.; De-Groeve, T.; Shrestha, K.; Gebremichael, M.; Restrepo, P. J.

2013-12-01

The South Asia is a flashpoint for natural disasters particularly flooding of the Indus, Ganges, and Brahmaputra has profound societal impacts for the region and globally. The 2007 Brahmaputra floods affecting India and Bangladesh, the 2008 avulsion of the Kosi River in India, the 2010 flooding of the Indus River in Pakistan and the 2013 Uttarakhand exemplify disasters on scales almost inconceivable elsewhere. Their frequent occurrence of floods combined with large and rapidly growing populations, high levels of poverty and low resilience, exacerbate the impact of the hazards. Mitigation of these devastating hazards are compounded by limited flood forecast capability, lack of rain/gauge measuring stations and forecast use within and outside the country, and transboundary data sharing on natural hazards. Here, we demonstrate the utility of remotely-derived hydrologic and weather products in producing skillful flood forecasting information without reliance on vulnerable in situ data sources. Over the last decade a forecast system has been providing operational probabilistic forecasts of severe flooding of the Brahmaputra and Ganges Rivers in Bangldesh was developed (Hopson and Webster 2010). The system utilizes ECMWF weather forecast uncertainty information and ensemble weather forecasts, rain gauge and satellite-derived precipitation estimates, together with the limited near-real-time river stage observations from Bangladesh. This system has been expanded to Pakistan and has successfully forecast the 2010-2012 flooding (Shrestha and Webster 2013). To overcome the in situ hydrological data problem, recent efforts in parallel with the numerical modeling have utilized microwave satellite remote sensing of river widths to generate operational discharge advective-based forecasts for the Ganges and Brahmaputra. More than twenty remotely locations upstream of Bangldesh were used to produce stand-alone river flow nowcasts and forecasts at 1-15 days lead time. showing that satellite-based flow estimates are a useful source of dynamical surface water information in data-scarce regions and that they could be used for model calibration and data assimilation purposes in near-time hydrologic forecast applications (Hirpa et al. 2013). More recent efforts during this year's monsoon season are optimally combining these different independent sources of river forecast information along with archived flood inundation imagery of the Dartmouth Flood Observatory to improve the visualization and overall skill of the ongoing CFAB ensemble weather forecast-based flood forecasting system within the unique context of the ongoing flood forecasting efforts for Bangladesh.

Improved Satellite Techniques for Monitoring and Forecasting the Transition of Hurricanes to Extratropical Storms

NASA Technical Reports Server (NTRS)

Folmer, Michael; Halverson, Jeffrey; Berndt, Emily; Dunion, Jason; Goodman, Steve; Goldberg, Mitch

2014-01-01

The Geostationary Operational Environmental Satellites R-Series (GOES-R) and Joint Polar Satellite System (JPSS) Satellite Proving Grounds have introduced multiple proxy and operational products into operations over the last few years. Some of these products have proven to be useful in current operations at various National Weather Service (NWS) offices and national centers as a first look at future satellite capabilities. Forecasters at the National Hurricane Center (NHC), Ocean Prediction Center (OPC), NESDIS Satellite Analysis Branch (SAB) and the NASA Hurricane and Severe Storms Sentinel (HS3) field campaign have had access to a few of these products to assist in monitoring extratropical transitions of hurricanes. The red, green, blue (RGB) Air Mass product provides forecasters with an enhanced view of various air masses in one complete image to help differentiate between possible stratospheric/tropospheric interactions, moist tropical air masses, and cool, continental/maritime air masses. As a compliment to this product, a new Atmospheric Infrared Sounder (AIRS) and Cross-track Infrared Sounder (CrIS) Ozone product was introduced in the past year to assist in diagnosing the dry air intrusions seen in the RGB Air Mass product. Finally, a lightning density product was introduced to forecasters as a precursor to the new Geostationary Lightning Mapper (GLM) that will be housed on GOES-R, to monitor the most active regions of convection, which might indicate a disruption in the tropical environment and even signal the onset of extratropical transition. This presentation will focus on a few case studies that exhibit extratropical transition and point out the usefulness of these new satellite techniques in aiding forecasters forecast these challenging events.

Atmospheric and oceanographic research review, 1978. [global weather, ocean/air interactions, and climate

NASA Technical Reports Server (NTRS)

1978-01-01

Research activities related to global weather, ocean/air interactions, and climate are reported. The global weather research is aimed at improving the assimilation of satellite-derived data in weather forecast models, developing analysis/forecast models that can more fully utilize satellite data, and developing new measures of forecast skill to properly assess the impact of satellite data on weather forecasting. The oceanographic research goal is to understand and model the processes that determine the general circulation of the oceans, focusing on those processes that affect sea surface temperature and oceanic heat storage, which are the oceanographic variables with the greatest influence on climate. The climate research objective is to support the development and effective utilization of space-acquired data systems in climate forecast models and to conduct sensitivity studies to determine the affect of lower boundary conditions on climate and predictability studies to determine which global climate features can be modeled either deterministically or statistically.

Satellite freeze forecast system: Executive summary

NASA Technical Reports Server (NTRS)

Martsolf, J. D. (Principal Investigator)

1983-01-01

A satellite-based temperature monitoring and prediction system consisting of a computer controlled acquisition, processing, and display system and the ten automated weather stations called by that computer was developed and transferred to the national weather service. This satellite freeze forecasting system (SFFS) acquires satellite data from either one of two sources, surface data from 10 sites, displays the observed data in the form of color-coded thermal maps and in tables of automated weather station temperatures, computes predicted thermal maps when requested and displays such maps either automatically or manually, archives the data acquired, and makes comparisons with historical data. Except for the last function, SFFS handles these tasks in a highly automated fashion if the user so directs. The predicted thermal maps are the result of two models, one a physical energy budget of the soil and atmosphere interface and the other a statistical relationship between the sites at which the physical model predicts temperatures and each of the pixels of the satellite thermal map.

Short-range precipitation forecasts using assimilation of simulated satellite water vapor profiles and column cloud liquid water amounts

NASA Technical Reports Server (NTRS)

Wu, Xiaohua; Diak, George R.; Hayden, Cristopher M.; Young, John A.

1995-01-01

These observing system simulation experiments investigate the assimilation of satellite-observed water vapor and cloud liquid water data in the initialization of a limited-area primitive equations model with the goal of improving short-range precipitation forecasts. The assimilation procedure presented includes two aspects: specification of an initial cloud liquid water vertical distribution and diabatic initialization. The satellite data is simulated for the next generation of polar-orbiting satellite instruments, the Advanced Microwave Sounding Unit (AMSU) and the High-Resolution Infrared Sounder (HIRS), which are scheduled to be launched on the NOAA-K satellite in the mid-1990s. Based on cloud-top height and total column cloud liquid water amounts simulated for satellite data a diagnostic method is used to specify an initial cloud water vertical distribution and to modify the initial moisture distribution in cloudy areas. Using a diabatic initialization procedure, the associated latent heating profiles are directly assimilated into the numerical model. The initial heating is estimated by time averaging the latent heat release from convective and large-scale condensation during the early forecast stage after insertion of satellite-observed temperature, water vapor, and cloud water formation. The assimilation of satellite-observed moisture and cloud water, together withy three-mode diabatic initialization, significantly alleviates the model precipitation spinup problem, especially in the first 3 h of the forecast. Experimental forecasts indicate that the impact of satellite-observed temperature and water vapor profiles and cloud water alone in the initialization procedure shortens the spinup time for precipitation rates by 1-2 h and for regeneration of the areal coverage by 3 h. The diabatic initialization further reduces the precipitation spinup time (compared to adiabatic initialization) by 1 h.

Market capture by 30/20 GHz satellite systems, volume 2

NASA Technical Reports Server (NTRS)

Gamble, R. B.; Saporta, L.

1981-01-01

Results of a telecommunications demand study are presented. Forecasts of demand for 30/20 GHz satellite systems, and the expected build up of traffic on these systems are given as a function of time for each of several operational scenarios.

Market capture by 30/20 GHz satellite systems, volume 2

NASA Astrophysics Data System (ADS)

Gamble, R. B.; Saporta, L.

1981-04-01

Results of a telecommunications demand study are presented. Forecasts of demand for 30/20 GHz satellite systems, and the expected build up of traffic on these systems are given as a function of time for each of several operational scenarios.

Technology requirements for communication satellites in the 1980's

NASA Technical Reports Server (NTRS)

Burtt, J. E.; Moe, C. R.; Elms, R. V.; Delateur, L. A.; Sedlacek, W. C.; Younger, G. G.

1973-01-01

The key technology requirements are defined for meeting the forecasted demands for communication satellite services in the 1985 to 1995 time frame. Evaluation is made of needs for services and technical and functional requirements for providing services. The future growth capabilities of the terrestrial telephone network, cable television, and satellite networks are forecasted. The impact of spacecraft technology and booster performance and costs upon communication satellite costs are analyzed. Systems analysis techniques are used to determine functional requirements and the sensitivities of technology improvements for reducing the costs of meeting requirements. Recommended development plans and funding levels are presented, as well as the possible cost saving for communications satellites in the post 1985 era.

Using Satellite Data in Weather Forecasting: I

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Suggs, Ronnie J.; Lecue, Juan M.

2006-01-01

The GOES Product Generation System (GPGS) is a set of computer codes and scripts that enable the assimilation of real-time Geostationary Operational Environmental Satellite (GOES) data into regional-weather-forecasting mathematical models. The GPGS can be used to derive such geophysical parameters as land surface temperature, the amount of precipitable water, the degree of cloud cover, the surface albedo, and the amount of insolation from satellite measurements of radiant energy emitted by the Earth and its atmosphere. GPGS incorporates a priori information (initial guesses of thermodynamic parameters of the atmosphere) and radiometric measurements from the geostationary operational environmental satellites along with mathematical models of physical principles that govern the transfer of energy in the atmosphere. GPGS solves the radiative-transfer equation and provides the resulting data products in formats suitable for use by weather-forecasting computer programs. The data-assimilation capability afforded by GPGS offers the potential to improve local weather forecasts ranging from 3 hours to 2 days - especially with respect to temperature, humidity, cloud cover, and the probability of precipitation. The improvements afforded by GPGS could be of interest to news media, utility companies, and other organizations that utilize regional weather forecasts.

Pilot Tests of Satellite Snowcover/Runoff Forecasting Systems. [Arizona, Sierra Nevada Mountains (Ca), Colorado, Rocky Mountains (North America)

NASA Technical Reports Server (NTRS)

Rango, A.

1978-01-01

Major snow zones of the western U.S. were selected to test the capability of satellite systems for mapping snowcover in various snow, cloud, climatic, and vegetation regimes. Different satellite snowcover analysis methods used in each area are described along with results.

Development of a satellite-based nowcasting system for surface solar radiation

NASA Astrophysics Data System (ADS)

Limbach, Sebastian; Hungershoefer, Katja; Müller, Richard; Trentmann, Jörg; Asmus, Jörg; Schömer, Elmar; Groß, André

2014-05-01

The goal of the RadNowCast project was the development of a tool-chain for a satellite-based nowcasting of the all sky global and direct surface solar radiation. One important application of such short-term forecasts is the computation of the expected energy yield of photovoltaic systems. This information is of great importance for an efficient balancing of power generation and consumption in large, decentralized power grids. Our nowcasting approach is based on an optical-flow analysis of a series of Meteosat SEVIRI satellite images. For this, we extended and combined several existing software tools and set up a series of benchmarks for determining the optimal forecasting parameters. The first step in our processing-chain is the determination of the cloud albedo from the HRV (High Resolution Visible)-satellite images using a Heliosat-type method. The actual nowcasting is then performed by a commercial software system in two steps: First, vector fields characterizing the movement of the clouds are derived from the cloud albedo data from the previous 15 min to 2 hours. Next, these vector fields are combined with the most recent cloud albedo data in order to extrapolate the cloud albedo in the near future. In the last step of the processing, the Gnu-Magic software is used to calculate the global and direct solar radiation based on the forecasted cloud albedo data. For an evaluation of the strengths and weaknesses of our nowcastig system, we analyzed four different benchmarks, each of which covered different weather conditions. We compared the forecasted data with radiation data derived from the real satellite images of the corresponding time steps. The impact of different parameters on the cloud albedo nowcasting and the surface radiation computation has been analysed. Additionally, we could show that our cloud-albedo-based forecasts outperform forecasts based on the original HRV images. Possible future extension are the incorporation of additional data sources, for example NWC-SAF high resolution wind fields, in order to improve the quality of the atmospheric motion fields, and experiments with custom, optimized software components for the optical-flow estimation and the nowcasting.

Using Deep Learning for Targeted Data Selection, Improving Satellite Observation Utilization for Model Initialization

NASA Astrophysics Data System (ADS)

Lee, Y. J.; Bonfanti, C. E.; Trailovic, L.; Etherton, B.; Govett, M.; Stewart, J.

2017-12-01

At present, a fraction of all satellite observations are ultimately used for model assimilation. The satellite data assimilation process is computationally expensive and data are often reduced in resolution to allow timely incorporation into the forecast. This problem is only exacerbated by the recent launch of Geostationary Operational Environmental Satellite (GOES)-16 satellite and future satellites providing several order of magnitude increase in data volume. At the NOAA Earth System Research Laboratory (ESRL) we are researching the use of machine learning the improve the initial selection of satellite data to be used in the model assimilation process. In particular, we are investigating the use of deep learning. Deep learning is being applied to many image processing and computer vision problems with great success. Through our research, we are using convolutional neural network to find and mark regions of interest (ROI) to lead to intelligent extraction of observations from satellite observation systems. These targeted observations will be used to improve the quality of data selected for model assimilation and ultimately improve the impact of satellite data on weather forecasts. Our preliminary efforts to identify the ROI's are focused in two areas: applying and comparing state-of-art convolutional neural network models using the analysis data from the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS) weather model, and using these results as a starting point to optimize convolution neural network model for pattern recognition on the higher resolution water vapor data from GOES-WEST and other satellite. This presentation will provide an introduction to our convolutional neural network model to identify and process these ROI's, along with the challenges of data preparation, training the model, and parameter optimization.

Operational specification and forecasting advances for Dst, LEO thermospheric densities, and aviation radiation dose and dose rate

NASA Astrophysics Data System (ADS)

Tobiska, W.; Knipp, D. J.; Burke, W. J.; Bouwer, D.; Bailey, J. J.; Hagan, M. P.; Didkovsky, L. V.; Garrett, H. B.; Bowman, B. R.; Gannon, J. L.; Atwell, W.; Blake, J. B.; Crain, W.; Rice, D.; Schunk, R. W.; Fulgham, J.; Bell, D.; Gersey, B.; Wilkins, R.; Fuschino, R.; Flynn, C.; Cecil, K.; Mertens, C. J.; Xu, X.; Crowley, G.; Reynolds, A.; Azeem, S. I.; Wiley, S.; Holland, M.; Malone, K.

2013-12-01

Space weather's effects upon the near-Earth environment are due to dynamic changes in the energy transfer processes from the Sun's photons, particles, and fields. Of the space environment domains that are affected by space weather, the magnetosphere, thermosphere, and even troposphere are key regions that are affected. Space Environment Technologies (SET) has developed and is producing innovative space weather applications. Key operational systems for providing timely information about the effects of space weather on these domains are SET's Magnetosphere Alert and Prediction System (MAPS), LEO Alert and Prediction System (LAPS), and Automated Radiation Measurements for Aviation Safety (ARMAS) system. MAPS provides a forecast Dst index out to 6 days through the data-driven, redundant data stream Anemomilos algorithm. Anemomilos uses observational proxies for the magnitude, location, and velocity of solar ejecta events. This forecast index is used by satellite operations to characterize upcoming geomagnetic storms, for example. LAPS is the SET fully redundant operational system providing recent history, current epoch, and forecast solar and geomagnetic indices for use in operational versions of the JB2008 thermospheric density model. The thermospheric densities produced by that system, driven by the LAPS data, are forecast to 72-hours to provide the global mass densities for satellite operators. ARMAS is a project that has successfully demonstrated the operation of a micro dosimeter on aircraft to capture the real-time radiation environment due to Galactic Cosmic Rays and Solar Energetic Particles. The dose and dose-rates are captured on aircraft, downlinked in real-time via the Iridium satellites, processed on the ground, incorporated into the most recent NAIRAS global radiation climatology data runs, and made available to end users via the web and smart phone apps. ARMAS provides the 'weather' of the radiation environment to improve air-crew and passenger safety. Many of the data products from MAPS, LAPS, and ARMAS are available on the SpaceWx smartphone app for iPhone, iPad, iPod, and Android professional users and public space weather education. We describe recent forecasting advances for moving the space weather information from these automated systems into operational, derivative products for communications, aviation, and satellite operations uses.

Forecasting the ocean optical environment in support of Navy mine warfare operations

NASA Astrophysics Data System (ADS)

Ladner, S. D.; Arnone, R.; Jolliff, J.; Casey, B.; Matulewski, K.

2012-06-01

A 3D ocean optical forecast system called TODS (Tactical Ocean Data System) has been developed to determine the performance of underwater LIDAR detection/identification systems. TODS fuses optical measurements from gliders, surface satellite optical properties, and 3D ocean forecast circulation models to extend the 2-dimensional surface satellite optics into a 3-dimensional optical volume including subsurface optical layers of beam attenuation coefficient (c) and diver visibility. Optical 3D nowcast and forecasts are combined with electro-optical identification (EOID) models to determine the underwater LIDAR imaging performance field used to identify subsurface mine threats in rapidly changing coastal regions. TODS was validated during a recent mine warfare exercise with Helicopter Mine Countermeasures Squadron (HM-14). Results include the uncertainties in the optical forecast and lidar performance and sensor tow height predictions that are based on visual detection and identification metrics using actual mine target images from the EOID system. TODS is a new capability of coupling the 3D optical environment and EOID system performance and is proving important for the MIW community as both a tactical decision aid and for use in operational planning, improving timeliness and efficiency in clearance operations.

Using Enabling Technologies to Facilitate the Comparison of Satellite Observations with the Model Forecasts for Hurricane Study

NASA Astrophysics Data System (ADS)

Li, P.; Knosp, B.; Hristova-Veleva, S. M.; Niamsuwan, N.; Johnson, M. P.; Shen, T. P. J.; Tanelli, S.; Turk, J.; Vu, Q. A.

2014-12-01

Due to their complexity and volume, the satellite data are underutilized in today's hurricane research and operations. To better utilize these data, we developed the JPL Tropical Cyclone Information System (TCIS) - an Interactive Data Portal providing fusion between Near-Real-Time satellite observations and model forecasts to facilitate model evaluation and improvement. We have collected satellite observations and model forecasts in the Atlantic Basin and the East Pacific for the hurricane seasons since 2010 and supported the NASA Airborne Campaigns for Hurricane Study such as the Genesis and Rapid Intensification Processes (GRIP) in 2010 and the Hurricane and Severe Storm Sentinel (HS3) from 2012 to 2014. To enable the direct inter-comparisons of the satellite observations and the model forecasts, the TCIS was integrated with the NASA Earth Observing System Simulator Suite (NEOS3) to produce synthetic observations (e.g. simulated passive microwave brightness temperatures) from a number of operational hurricane forecast models (HWRF and GFS). An automated process was developed to trigger NEOS3 simulations via web services given the location and time of satellite observations, monitor the progress of the NEOS3 simulations, display the synthetic observation and ingest them into the TCIS database when they are done. In addition, three analysis tools, the joint PDF analysis of the brightness temperatures, ARCHER for finding the storm-center and the storm organization and the Wave Number Analysis tool for storm asymmetry and morphology analysis were integrated into TCIS to provide statistical and structural analysis on both observed and synthetic data. Interactive tools were built in the TCIS visualization system to allow the spatial and temporal selections of the datasets, the invocation of the tools with user specified parameters, and the display and the delivery of the results. In this presentation, we will describe the key enabling technologies behind the design of the TCIS interactive data portal and analysis tools, including the spatial database technology for the representation and query of the level 2 satellite data, the automatic process flow using web services, the interactive user interface using the Google Earth API, and a common and expandable Python wrapper to invoke the analysis tools.

Analysis and forecast experiments incorporating satellite soundings and cloud and water vapor drift wind information

NASA Technical Reports Server (NTRS)

Goodman, Brian M.; Diak, George R.; Mills, Graham A.

1986-01-01

A system for assimilating conventional meteorological data and satellite-derived data in order to produce four-dimensional gridded data sets of the primary atmospheric variables used for updating limited area forecast models is described. The basic principles of a data assimilation scheme as proposed by Lorenc (1984) are discussed. The design of the system and its incremental assimilation cycles are schematically presented. The assimilation system was tested using radiosonde, buoy, VAS temperature, dew point, gradient wind data, cloud drift, and water vapor motion data. The rms vector errors for the data are analyzed.

Simulating surface oil transport during the Deepwater Horizon oil spill: Experiments with the BioCast system

NASA Astrophysics Data System (ADS)

Jolliff, Jason Keith; Smith, Travis A.; Ladner, Sherwin; Arnone, Robert A.

2014-03-01

The U.S. Naval Research Laboratory (NRL) is developing nowcast/forecast software systems designed to combine satellite ocean color data streams with physical circulation models in order to produce prognostic fields of ocean surface materials. The Deepwater Horizon oil spill in the Gulf of Mexico provided a test case for the Bio-Optical Forecasting (BioCast) system to rapidly combine the latest satellite imagery of the oil slick distribution with surface circulation fields in order to produce oil slick transport scenarios and forecasts. In one such sequence of experiments, MODIS satellite true color images were combined with high-resolution ocean circulation forecasts from the Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS®) to produce 96-h oil transport simulations. These oil forecasts predicted a major oil slick landfall at Grand Isle, Louisiana, USA that was subsequently observed. A key driver of the landfall scenario was the development of a coastal buoyancy current associated with Mississippi River Delta freshwater outflow. In another series of experiments, longer-term regional circulation model results were combined with oil slick source/sink scenarios to simulate the observed containment of surface oil within the Gulf of Mexico. Both sets of experiments underscore the importance of identifying and simulating potential hydrodynamic conduits of surface oil transport. The addition of explicit sources and sinks of surface oil concentrations provides a framework for increasingly complex oil spill modeling efforts that extend beyond horizontal trajectory analysis.

Advances in air quality prediction with the use of integrated systems

NASA Astrophysics Data System (ADS)

Dragani, R.; Benedetti, A.; Engelen, R. J.; Peuch, V. H.

2017-12-01

Recent years have seen the rise of global operational atmospheric composition forecasting systems for several applications including climate monitoring, provision of boundary conditions for regional air quality forecasting, energy sector applications, to mention a few. Typically, global forecasts are provided in the medium-range up to five days ahead and are initialized with an analysis based on satellite data. In this work we present the latest advances in data assimilation using the ECMWF's 4D-Var system extended to atmospheric composition which is currently operational under the Copernicus Atmosphere Monitoring Service of the European Commission. The service is based on acquisition of all relevant data available in near-real-time, the processing of these datasets in the assimilation and the subsequent dissemination of global forecasts at ECMWF. The global forecasts are used by the CAMS regional models as boundary conditions for the European forecasts based on a multi-model ensemble. The global forecasts are also used to provide boundary conditions for other parts of the world (e.g., China) and are freely available to all interested entities. Some of the regional models also perform assimilation of satellite and ground-based observations. All products are assessed, validated and made publicly available on https://atmosphere.copernicus.eu/.

JPSS Operational and Research Applications: The Pathway from Observations to Applications to Information.

NASA Astrophysics Data System (ADS)

Goldberg, M.; Sjoberg, W.; Layns, A. L.

2017-12-01

Applications of satellite data are paramount to transform science and technology to product and services which are used in critical decision making. For the satellite community, good representations of technology are the satellite sensors, while science provides the instrument calibration and derived geophysical parameters. Weather forecasting is an application of the science and technology provided by remote sensing satellites. The Joint Polar Satellite System, which includes the Suomi National Polar-orbiting Partnership (S-NPP) provides formidable science and technology to support many applications and includes support to 1) weather forecasting - data from the JPSS Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) are used to forecast weather events out to 7 days - nearly 85% of all data used in weather forecasting are from polar orbiting satellites; 2) environmental monitoring -data from the JPSS Visible Infrared Imager Radiometer Suite (VIIRS) are used to monitor the environment including the health of coastal ecosystems, drought conditions, fire, smoke, dust, snow and ice, and the state of oceans, including sea surface temperature and ocean color; and 3) climate monitoring - data from JPSS instruments, including OMPS and CERES will provide continuity to climate data records established using NOAA POES and NASA Earth Observing System (EOS) satellite observations. To bridge the gap between products and applications, the JPSS Program has established the Proving Ground and Risk Reduction (PGRR) Program to identify opportunities to maximize the operational application of current JPSS capabilities. The PGRR Program also helps identify and evaluate the use of JPSS capabilities for new operational missions. New PGRR initiatives focus on hydrological, Arctic, data assimilation, atmospheric chemistry, ocean ecosystem applications. At the conference, the benefits of JPSS data on societal benefits will be presented along with results from the PGRR initiatives.

The impact of satellite temperature soundings on the forecasts of a small national meteorological service

NASA Technical Reports Server (NTRS)

Wolfson, N.; Thomasell, A.; Alperson, Z.; Brodrick, H.; Chang, J. T.; Gruber, A.; Ohring, G.

1984-01-01

The impact of introducing satellite temperature sounding data on a numerical weather prediction model of a national weather service is evaluated. A dry five level, primitive equation model which covers most of the Northern Hemisphere, is used for these experiments. Series of parallel forecast runs out to 48 hours are made with three different sets of initial conditions: (1) NOSAT runs, only conventional surface and upper air observations are used; (2) SAT runs, satellite soundings are added to the conventional data over oceanic regions and North Africa; and (3) ALLSAT runs, the conventional upper air observations are replaced by satellite soundings over the entire model domain. The impact on the forecasts is evaluated by three verification methods: the RMS errors in sea level pressure forecasts, systematic errors in sea level pressure forecasts, and errors in subjective forecasts of significant weather elements for a selected portion of the model domain. For the relatively short range of the present forecasts, the major beneficial impacts on the sea level pressure forecasts are found precisely in those areas where the satellite sounding are inserted and where conventional upper air observations are sparse. The RMS and systematic errors are reduced in these regions. The subjective forecasts of significant weather elements are improved with the use of the satellite data. It is found that the ALLSAT forecasts are of a quality comparable to the SAR forecasts.

An Integrated Urban Flood Analysis System in South Korea

NASA Astrophysics Data System (ADS)

Moon, Young-Il; Kim, Min-Seok; Yoon, Tae-Hyung; Choi, Ji-Hyeok

2017-04-01

Due to climate change and the rapid growth of urbanization, the frequency of concentrated heavy rainfall has caused urban floods. As a result, we studied climate change in Korea and developed an integrated flood analysis system that systematized technology to quantify flood risk and flood forecasting in urban areas. This system supports synthetic decision-making through real-time monitoring and prediction on flash rain or short-term rainfall by using radar and satellite information. As part of the measures to deal with the increase of inland flood damage, we have found it necessary to build a systematic city flood prevention system that systematizes technology to quantify flood risk as well as flood forecast, taking into consideration both inland and river water. This combined inland-river flood analysis system conducts prediction on flash rain or short-term rainfall by using radar and satellite information and performs prompt and accurate prediction on the inland flooded area. In addition, flood forecasts should be accurate and immediate. Accurate flood forecasts signify that the prediction of the watch, warning time and water level is precise. Immediate flood forecasts represent the forecasts lead time which is the time needed to evacuate. Therefore, in this study, in order to apply rainfall-runoff method to medium and small urban stream for flood forecasts, short-term rainfall forecasting using radar is applied to improve immediacy. Finally, it supports synthetic decision-making for prevention of flood disaster through real-time monitoring. Keywords: Urban Flood, Integrated flood analysis system, Rainfall forecasting, Korea Acknowledgments This research was supported by a grant (16AWMP-B066744-04) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport of Korean government.

Applications of Satellite Observations to Aerosol Analyses and Forecasting using the NAAPS Model and the DataFed Distributed Data System

NASA Astrophysics Data System (ADS)

Husar, R. B.; Hoijarvi, K.; Westphal, D. L.; Scheffe, R.; Keating, T.; Frank, N.; Poirot, R.; DuBois, D. W.; Bleiweiss, M. P.; Eberhard, W. L.; Menon, R.; Sethi, V.; Deshpande, A.

2012-12-01

Near-real-time (NRT) aerosol characterization, forecasting and decision support is now possible through the availability of (1) surface-based monitoring of regional PM concentrations, (2) global-scale columnar aerosol observations through satellites; (3) an aerosol model (NAAPS) that is capable of assimilating NRT satellite observations; and (4) an emerging cyber infrastructure for processing and distribution of data and model results (DataFed) for a wide range of users. This report describes the evolving NRT aerosol analysis and forecasting system and its applications at Federal and State and other AQ Agencies and groups. Through use cases and persistent real-world applications in the US and abroad, the report will show how satellite observations along with surface data and models are combined to aid decision support for AQ management, science and informing the public. NAAPS is the U.S. Navy's global aerosol and visibility forecast model that generates operational six-day global-scale forecasts for sulfate, dust, sea salt, and smoke aerosol. Through NAVDAS-AOD, NAAPS operationally assimilates filtered and corrected MODIS MOD04 aerosol optical depths and uses satellite-derived FLAMBÉ smoke emissions. Washington University's federated data system, DataFed, consist of a (1) data server which mediates the access to AQ datasets from distributed providers (NASA, NOAA, EPA, etc.,); (2) an AQ Data Catalog for finding and accessing data; and (3) a set of application programs/tools for browsing, exploring, comparing, aggregating, fusing data, evaluating models and delivering outputs through interactive visualization. NAAPS and DataFed are components of the Global Earth Observation System of Systems (GEOSS). Satellite data support the detection of long-range transported wind-blown dust and biomass smoke aerosols on hemispheric scales. The AQ management and analyst communities use the satellite/model data through DataFed and other channels as evidence for Exceptional Events (EE) as defined by EPA; i.e., Sahara dust impact on Texas and Florida, local dusts events in the Southwestern U.S. and Canadian smoke events over the Northeastern U.S. Recent applications include the impact analysis of a major Saudi Arabian dust event on Mumbai, India air quality. The NAAPS model and the DataFed tools can visualize the dynamic AQ events as they are manifested through the different sensors. Satellite-derived aerosol observations assimilated into NAAPS provide estimates of daily emission rates for dust and biomass fire sources. Tuning and reconciliation of the observations, emissions and models constitutes a key and novel contribution yielding a convergence toward the true five-dimensional (X, Y, Z, T, Composition) characterization of the atmospheric aerosol data space. This observation-emission-model reconciliation effort is aided by model evaluation tools and supports the international HTAP program. The report will also discuss some of the challenges facing multi-disciplinary, multi-agency, multi-national applications of integrated observation-modeling system of systems that impede the incorporation of satellite observations into AQ management decision support systems.

Testing an innovative framework for flood forecasting, monitoring and mapping in Europe

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Kalas, Milan; Lorini, Valerio; Wania, Annett; Pappenberger, Florian; Salamon, Peter; Ramos, Maria Helena; Cloke, Hannah; Castillo, Carlos

2017-04-01

Between May and June 2016, France was hit by severe floods, particularly in the Loire and Seine river basins. In this work, we use this case study to test an innovative framework for flood forecasting, mapping and monitoring. More in detail, the system integrates in real-time two components of the Copernicus Emergency mapping services, namely the European Flood Awareness System and the satellite-based Rapid Mapping, with new procedures for rapid risk assessment and social media and news monitoring. We explore in detail the performance of each component of the system, demonstrating the improvements in respect to stand-alone flood forecasting and monitoring systems. We show how the performances of the forecasting component can be refined using the real-time feedback from social media monitoring to identify which areas were flooded, to evaluate the flood intensity, and therefore to correct impact estimations. Moreover, we show how the integration with impact forecast and social media monitoring can improve the timeliness and efficiency of satellite based emergency mapping, and reduce the chances of missing areas where flooding is already happening. These results illustrate how the new integrated approach leads to a better and earlier decision making and a timely evaluation of impacts.

CALIPSO Satellite Lidar Identification Of Elevated Dust Over Australia Compared With Air Quality Model PM60 Forecasts

NASA Technical Reports Server (NTRS)

Young, Stuart A.; Vaughan, Mark; Omar, Ali; Liu, Zhaoyan; Lee, Sunhee; Hu, Youngxiang; Cope, Martin

2008-01-01

Global measurements of the vertical distribution of clouds and aerosols have been recorded by the lidar on board the CALIPSO (Cloud Aerosol Lidar Infrared Pathfinder Satellite Observations) satellite since June 2006. Such extensive, height-resolved measurements provide a rare and valuable opportunity for developing, testing and validating various atmospheric models, including global climate, numerical weather prediction, chemical transport and air quality models. Here we report on the initial results of an investigation into the performance of the Australian Air Quality Forecast System (AAQFS) model in forecasting the distribution of elevated dust over the Australian region. The model forecasts of PM60 dust distribution are compared with the CALIPSO lidar Vertical Feature Mask (VFM) data product. The VFM classifies contiguous atmospheric regions of enhanced backscatter as either cloud or aerosols. Aerosols are further classified into six subtypes. By comparing forecast PM60 concentration profiles to the spatial distribution of dust reported in the CALIPSO VFM, we can assess the model s ability to predict the occurrence and the vertical and horizontal extents of dust events within the study area.

The 30/20 GHz fixed communications systems service demand assessment. Volume 3: Annex

NASA Technical Reports Server (NTRS)

Gamble, R. B.; Seltzer, H. R.; Speter, K. M.; Westheimer, M.

1979-01-01

A review of studies forecasting the communication market in the United States is given. The applicability of these forecasts to assessment of demand for the 30/20 GHz fixed communications system is analyzed. Costs for the 30/20 satellite trunking systems are presented and compared with the cost of terrestrial communications.

Satellite radiance data assimilation for binary tropical cyclone cases over the western North Pacific

NASA Astrophysics Data System (ADS)

Choi, Yonghan; Cha, Dong-Hyun; Lee, Myong-In; Kim, Joowan; Jin, Chun-Sil; Park, Sang-Hun; Joh, Min-Su

2017-06-01

A total of three binary tropical cyclone (TC) cases over the Western North Pacific are selected to investigate the effects of satellite radiance data assimilation on analyses and forecasts of binary TCs. Two parallel cycling experiments with a 6 h interval are performed for each binary TC case, and the difference between the two experiments is whether satellite radiance observations are assimilated. Satellite radiance observations are assimilated using the Weather Research and Forecasting Data Assimilation (WRFDA)'s three-dimensional variational (3D-Var) system, which includes the observation operator, quality control procedures, and bias correction algorithm for radiance observations. On average, radiance assimilation results in slight improvements of environmental fields and track forecasts of binary TC cases, but the detailed effects vary with the case. When there is no direct interaction between binary TCs, radiance assimilation leads to better depictions of environmental fields, and finally it results in improved track forecasts. However, positive effects of radiance assimilation on track forecasts can be reduced when there exists a direct interaction between binary TCs and intensities/structures of binary TCs are not represented well. An initialization method (e.g., dynamic initialization) combined with radiance assimilation and/or more advanced DA techniques (e.g., hybrid method) can be considered to overcome these limitations.

Satellites see major winter storm marching toward the U.S. East Coast

NASA Image and Video Library

2017-12-08

NASA and NOAA satellites are providing various views of the major winter storm marching toward the U.S. East coast on March 13. The storm is forecast to merge with another system and is expected to bring large snowfall totals from the Mid-Atlantic to New England. NASA's Aqua satellite gathered infrared data from the storm system and the area ahead of the storm for cloud and ground temperatures. NOAA's GOES-East satellite provided visible and infrared imagery that showed the extent and the movement of the system. Forecasters at the National Weather Service's Weather Prediction Center (WPC) noted that the low pressure system crossing the Midwest states and Ohio Valley is expected to merge with another low off the southeast U.S. coast. WPC stated "This will allow for a strong nor'easter to develop near the coast and cause a late-season snowstorm from the central Appalachians to New England, including many of the big cities in the Northeast U.S." Credits: NASA/NOAA GOES Project

Assimilation of GNSS radio occultation observations in GRAPES

NASA Astrophysics Data System (ADS)

Liu, Y.; Xue, J.

2014-07-01

This paper reviews the development of the global navigation satellite system (GNSS) radio occultation (RO) observations assimilation in the Global/Regional Assimilation and PrEdiction System (GRAPES) of China Meteorological Administration, including the choice of data to assimilate, the data quality control, the observation operator, the tuning of observation error, and the results of the observation impact experiments. The results indicate that RO data have a significantly positive effect on analysis and forecast at all ranges in GRAPES not only in the Southern Hemisphere where conventional observations are lacking but also in the Northern Hemisphere where data are rich. It is noted that a relatively simple assimilation and forecast system in which only the conventional and RO observation are assimilated still has analysis and forecast skill even after nine months integration, and the analysis difference between both hemispheres is gradually reduced with height when compared with NCEP (National Centers for Enviromental Prediction) analysis. Finally, as a result of the new onboard payload of the Chinese FengYun-3 (FY-3) satellites, the research status of the RO of FY-3 satellites is also presented.

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

NASA Technical Reports Server (NTRS)

Duda, David P.; Minnis, Patrick

2009-01-01

Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.

A seamless global hydrological monitoring and forecasting system for water resources assessment and hydrological hazard early warning

NASA Astrophysics Data System (ADS)

Sheffield, Justin; He, Xiaogang; Wood, Eric; Pan, Ming; Wanders, Niko; Zhan, Wang; Peng, Liqing

2017-04-01

Sustainable management of water resources and mitigation of the impacts of hydrological hazards are becoming ever more important at large scales because of inter-basin, inter-country and inter-continental connections in water dependent sectors. These include water resources management, food production, and energy production, whose needs must be weighed against the water needs of ecosystems and preservation of water resources for future generations. The strains on these connections are likely to increase with climate change and increasing demand from burgeoning populations and rapid development, with potential for conflict over water. At the same time, network connections may provide opportunities to alleviate pressures on water availability through more efficient use of resources such as trade in water dependent goods. A key constraint on understanding, monitoring and identifying solutions to increasing competition for water resources and hazard risk is the availability of hydrological data for monitoring and forecasting water resources and hazards. We present a global online system that provides continuous and consistent water products across time scales, from the historic instrumental period, to real-time monitoring, short-term and seasonal forecasts, and climate change projections. The system is intended to provide data and tools for analysis of historic hydrological variability and trends, water resources assessment, monitoring of evolving hazards and forecasts for early warning, and climate change scale projections of changes in water availability and extreme events. The system is particular useful for scientists and stakeholders interested in regions with less available in-situ data, and where forecasts have the potential to help decision making. The system is built on a database of high-resolution climate data from 1950 to present that merges available observational records with bias-corrected reanalysis and satellite data, which then drives a coupled land surface model-flood inundation model to produce hydrological variables and indices at daily, 0.25-degree resolution, globally. The system is updated in near real-time (< 2 days) using satellite precipitation and weather model data, and produces forecasts at short-term (out to 7 days) based on the Global Forecast System (GFS) and seasonal (up to 6 months) based on U.S. National Multi-Model Ensemble (NMME) seasonal forecasts. Climate change projections are based on bias-corrected and downscaled CMIP5 climate data that is used to force the hydrological model. Example products from the system include real-time and forecast drought indices for precipitation, soil moisture, and streamflow, and flood magnitude and extent indices. The model outputs are complemented by satellite based products and indices based on satellite data for vegetation health (MODIS NDVI) and soil moisture (SMAP). We show examples of the validation of the system at regional scales, including how local information can significantly improve predictions, and examples of how the system can be used to understand large-scale water resource issues, and in real-world contexts for early warning, decision making and planning.

Effects of the Forecasting Methods, Precipitation Character, and Satellite Resolution on the Predictability of Short-Term Quantitative Precipitation Nowcasting (QPN) from a Geostationary Satellite.

PubMed

Liu, Yu; Xi, Du-Gang; Li, Zhao-Liang; Ji, Wei

2015-01-01

The prediction of the short-term quantitative precipitation nowcasting (QPN) from consecutive gestational satellite images has important implications for hydro-meteorological modeling and forecasting. However, the systematic analysis of the predictability of QPN is limited. The objective of this study is to evaluate effects of the forecasting model, precipitation character, and satellite resolution on the predictability of QPN using images of a Chinese geostationary meteorological satellite Fengyun-2F (FY-2F) which covered all intensive observation since its launch despite of only a total of approximately 10 days. In the first step, three methods were compared to evaluate the performance of the QPN methods: a pixel-based QPN using the maximum correlation method (PMC); the Horn-Schunck optical-flow scheme (PHS); and the Pyramid Lucas-Kanade Optical Flow method (PPLK), which is newly proposed here. Subsequently, the effect of the precipitation systems was indicated by 2338 imageries of 8 precipitation periods. Then, the resolution dependence was demonstrated by analyzing the QPN with six spatial resolutions (0.1atial, 0.3a, 0.4atial rand 0.6). The results show that the PPLK improves the predictability of QPN with better performance than the other comparison methods. The predictability of the QPN is significantly determined by the precipitation system, and a coarse spatial resolution of the satellite reduces the predictability of QPN.

Effects of the Forecasting Methods, Precipitation Character, and Satellite Resolution on the Predictability of Short-Term Quantitative Precipitation Nowcasting (QPN) from a Geostationary Satellite

PubMed Central

Liu, Yu; Xi, Du-Gang; Li, Zhao-Liang; Ji, Wei

2015-01-01

The prediction of the short-term quantitative precipitation nowcasting (QPN) from consecutive gestational satellite images has important implications for hydro-meteorological modeling and forecasting. However, the systematic analysis of the predictability of QPN is limited. The objective of this study is to evaluate effects of the forecasting model, precipitation character, and satellite resolution on the predictability of QPN usingimages of a Chinese geostationary meteorological satellite Fengyun-2F (FY-2F) which covered all intensive observation since its launch despite of only a total of approximately 10 days. In the first step, three methods were compared to evaluate the performance of the QPN methods: a pixel-based QPN using the maximum correlation method (PMC); the Horn-Schunck optical-flow scheme (PHS); and the Pyramid Lucas-Kanade Optical Flow method (PPLK), which is newly proposed here. Subsequently, the effect of the precipitation systems was indicated by 2338 imageries of 8 precipitation periods. Then, the resolution dependence was demonstrated by analyzing the QPN with six spatial resolutions (0.1atial, 0.3a, 0.4atial rand 0.6). The results show that the PPLK improves the predictability of QPN with better performance than the other comparison methods. The predictability of the QPN is significantly determined by the precipitation system, and a coarse spatial resolution of the satellite reduces the predictability of QPN. PMID:26447470

Assimilation of GMS-5 satellite winds using nudging method with MM5

NASA Astrophysics Data System (ADS)

Gao, Shanhong; Wu, Zengmao; Yang, Bo

2006-09-01

With the aid of Meteorological Information Composite and Processing System (MICAPS), satellite wind vectors derived from the Geostationary Meteorological Statellite-5 (GMS-5) and retrieved by National Satellite Meteorology Center of China (NSMC) can be obtained. Based on the nudging method built in the fifth-generation Mesoscale Model (MM5) of Pennsylvania State University and National Center for Atmospheric Research, a data preprocessor is developed to convert these satellite wind vectors to those with specified format required in MM5. To examine the data preprocessor and evaluate the impact of satellite winds from GMS-5 on MM5 simulations, a series of numerical experimental forecasts consisting of four typhoon cases in 2002 are designed and implemented. The results show that the preprocessor can process satellite winds smoothly and MM5 model runs successfully with a little extra computational load during ingesting these winds, and that assimilation of satellite winds by MM5 nudging method can obviously improve typhoon track forecast but contributes a little to typhoon intensity forecast. The impact of the satellite winds depends heavily upon whether the typhoon bogussing scheme in MM5 was turned on or not. The data preprocessor developed in this paper not only can treat GMS-5 satellite winds but also has capability with little modification to process derived winds from other geostationary satellites.

Global Turbulence Decision Support for Aviation

NASA Astrophysics Data System (ADS)

Williams, J.; Sharman, R.; Kessinger, C.; Feltz, W.; Wimmers, A.

2009-09-01

Turbulence is widely recognized as the leading cause of injuries to flight attendants and passengers on commercial air carriers, yet legacy decision support products such as SIGMETs and SIGWX charts provide relatively low spatial- and temporal-resolution assessments and forecasts of turbulence, with limited usefulness for strategic planning and tactical turbulence avoidance. A new effort is underway to develop an automated, rapid-update, gridded global turbulence diagnosis and forecast system that addresses upper-level clear-air turbulence, mountain-wave turbulence, and convectively-induced turbulence. This NASA-funded effort, modeled on the U.S. Federal Aviation Administration's Graphical Turbulence Guidance (GTG) and GTG Nowcast systems, employs NCEP Global Forecast System (GFS) model output and data from NASA and operational satellites to produce quantitative turbulence nowcasts and forecasts. A convective nowcast element based on GFS forecasts and satellite data provides a basis for diagnosing convective turbulence. An operational prototype "Global GTG” system has been running in real-time at the U.S. National Center for Atmospheric Research since the spring of 2009. Initial verification based on data from TRMM, Cloudsat and MODIS (for the convection nowcasting) and AIREPs and AMDAR data (for turbulence) are presented. This product aims to provide the "single authoritative source” for global turbulence information for the U.S. Next Generation Air Transportation System.

Global Drought Monitoring and Forecasting based on Satellite Data and Land Surface Modeling

NASA Astrophysics Data System (ADS)

Sheffield, J.; Lobell, D. B.; Wood, E. F.

2010-12-01

Monitoring drought globally is challenging because of the lack of dense in-situ hydrologic data in many regions. In particular, soil moisture measurements are absent in many regions and in real time. This is especially problematic for developing regions such as Africa where water information is arguably most needed, but virtually non-existent on the ground. With the emergence of remote sensing estimates of all components of the water cycle there is now the potential to monitor the full terrestrial water cycle from space to give global coverage and provide the basis for drought monitoring. These estimates include microwave-infrared merged precipitation retrievals, evapotranspiration based on satellite radiation, temperature and vegetation data, gravity recovery measurements of changes in water storage, microwave based retrievals of soil moisture and altimetry based estimates of lake levels and river flows. However, many challenges remain in using these data, especially due to biases in individual satellite retrieved components, their incomplete sampling in time and space, and their failure to provide budget closure in concert. A potential way forward is to use modeling to provide a framework to merge these disparate sources of information to give physically consistent and spatially and temporally continuous estimates of the water cycle and drought. Here we present results from our experimental global water cycle monitor and its African drought monitor counterpart (http://hydrology.princeton.edu/monitor). The system relies heavily on satellite data to drive the Variable Infiltration Capacity (VIC) land surface model to provide near real-time estimates of precipitation, evapotranspiraiton, soil moisture, snow pack and streamflow. Drought is defined in terms of anomalies of soil moisture and other hydrologic variables relative to a long-term (1950-2000) climatology. We present some examples of recent droughts and how they are identified by the system, including objective quantification and tracking of their spatial-temporal characteristics. Further we present strategies for merging various sources of information, including bias correction of satellite precipitation and assimilation of remotely sensed soil moisture, which can augment the monitoring in regions where satellite precipitation is most uncertain. Ongoing work is adding a drought forecast component based on a successful implementation over the U.S. and agricultural productivity estimates based on output from crop yield models. The forecast component uses seasonal global climate forecasts from the NCEP Climate Forecast System (CFS). These are merged with observed climatology in a Bayesian framework to produce ensemble atmospheric forcings that better capture the uncertainties. At the same time, the system bias corrects and downscales the monthly CFS data. We show some initial seasonal (up to 6-month lead) hydrologic forecast results for the African system. Agricultural monitoring is based on the precipitation, temperature and soil moisture from the system to force statistical and process based crop yield models. We demonstrate the feasibility of monitoring major crop types across the world and show a strategy for providing predictions of yields within our drought forecast mode.

Research Review, 1984

NASA Technical Reports Server (NTRS)

1986-01-01

A variety of topics relevant to global modeling and simulation are presented. Areas of interest include: (1) analysis and forecast studies; (2) satellite observing systems; (3) analysis and forecast model development; (4) atmospheric dynamics and diagnostic studies; (5) climate/ocean-air interactions; and notes from lectures.

Telecommunications forecast for ITU Region 2 to the year 1995

NASA Technical Reports Server (NTRS)

Hollansworth, J. E.; Salzman, J. A.; Ramler, J. R.

1985-01-01

Telecommunications activity was studied. The primary objective was to forecast the need for fixed service satellites (FSS) by countries within ITU Region 2 excluding the United States and Greenland. Forecasts of telecommunications equipment needs were developed as a yardstick of the relative level of telecommunications activity among developing countries within the region. A likely scenario for the implementation of domestic and regional communications satellites is forecasted to provide services to and among countries in ITU Region 2. By 1995, it is forecast that 15 fixed service satellites will be implemented. A forecast of the countries requirements indicates that, with the possible exception of Canada, this constellation of satellites will meet these countries' needs to beyond the year 2000.

Real Time Volcanic Cloud Products and Predictions for Aviation Alerts

NASA Technical Reports Server (NTRS)

Krotkov, Nickolay A.; Habib, Shahid; da Silva, Arlindo; Hughes, Eric; Yang, Kai; Brentzel, Kelvin; Seftor, Colin; Li, Jason Y.; Schneider, David; Guffanti, Marianne;

Improving River Flow Predictions from the NOAA NCRFC Forecasting Model by Incorporating Satellite Observations

NASA Astrophysics Data System (ADS)

Tuttle, S. E.; Jacobs, J. M.; Restrepo, P. J.; Deweese, M. M.; Connelly, B.; Buan, S.

2016-12-01

The NOAA National Weather Service North Central River Forecast Center (NCRFC) is responsible for issuing river flow forecasts for parts of the Upper Mississippi, Great Lakes, and Hudson Bay drainages, including the Red River of the North basin (RRB). The NCRFC uses an operational hydrologic modeling infrastructure called the Community Hydrologic Prediction System (CHPS) for its operational forecasts, which currently links the SNOW-17 snow accumulation and ablation model, to the Sacramento-Soil Moisture Accounting (SAC-SMA) rainfall-runoff model, to a number of hydrologic and hydraulic flow routing models. The operational model is lumped and requires only area-averaged precipitation and air temperature as inputs. NCRFC forecasters use observational data of hydrological state variables as a source of supplemental information during forecasting, and can use professional judgment to modify the model states in real time. In a few recent years (e.g. 2009, 2013), the RRB exhibited unexpected anomalous hydrologic behavior, resulting in overestimation of peak flood discharge by up to 70% and highlighting the need for observations with high temporal and spatial coverage. Unfortunately, observations of hydrological states (e.g. soil moisture, snow water equivalent (SWE)) are relatively scarce in the RRB. Satellite remote sensing can fill this need. We use Minnesota's Buffalo River watershed within the RRB as a test case and update the operational CHPS model using modifications based on satellite observations, including AMSR-E SWE and SMOS soil moisture estimates. We evaluate the added forecasting skill of the satellite-enhanced model compared to measured streamflow using hindcasts from 2010-2013.

Assimilation of Real-Time Satellite And Human Sensor Networks for Modeling Natural Disasters

NASA Astrophysics Data System (ADS)

Aulov, O.; Halem, M.; Lary, D. J.

2011-12-01

We describe the development of underlying technologies needed to address the merging of a web of real time satellite sensor Web (SSW) and Human Sensor Web (HSW) needed to augment the US response to extreme events. As an initial prototyping step and use case scenario, we consider the development of two major system tools that can be transitioned from research to the responding operational agency for mitigating coastal oil spills. These tools consist of the capture of Situation Aware (SA) Social Media (SM) Data, and assimilation of the processed information into forecasting models to provide incident decision managers with interactive virtual spatial temporal animations superimposed with probabilistic data estimates. The system methodologies are equally applicable to the wider class of extreme events such as plume dispersions from volcanoes or massive fires, major floods, hurricane impacts, radioactive isotope dispersions from nuclear accidents, etc. A successful feasibility demonstration of this technology has been shown in the case of the Deepwater Horizon Oil Spill where Human Sensor Networks have been combined with a geophysical model to perform parameter assessments. Flickr images of beached oil were mined from the spill area, geolocated and timestamped and converted into geophysical data. This data was incorporated into General NOAA Operational Modeling Environment (GNOME), a Lagrangian forecast model that uses near real-time surface winds, ocean currents, and satellite shape profiles of oil to generate a forecast of plume movement. As a result, improved estimates of diffusive coefficients and rates of oil spill were determined. Current approaches for providing satellite derived oil distributions are collected from a satellite sensor web of operational and research sensors from many countries, and a manual analysis is performed by NESDIS. A real time SA HSW processing system based on geolocated SM data from sources such as Twitter, Flickr, YouTube etc., greatly supplements the current operational practice of sending out teams of humans to gather samples of tarballs reaching coastal locations. We show that ensemble Kalman filter assimilation of the combination of SM data with model forecast background data fields can minimize the false positive cases of satellite observations alone. Our future framework consists of two parts, a real time SA HSW processing system and an on-demand SSW processing system. HSW processing system uses a geolocated SM data to provide observations of coastal oil contact. SSW system is composed of selected instruments from NASA EOS, NPP and available Decadal Survey mission satellites along with other in situ data to form a real time regional oil spill observing system. We will automate the NESDIS manual process of providing oil spill maps by using Self Organizing Feature Map (SOFM) algorithm. We use the LETKF scheme for assimilating the satellite sensor web and HSW observations into the GNOME model to reduce the uncertainty of the observations. We intend to infuse these developments in an SOA implementation for execution of event driven model forecast assimilation cycles in a dedicated HPC cloud.

Ecological Forecasting in the Applied Sciences Program and Input to the Decadal Survey

NASA Technical Reports Server (NTRS)

Skiles, Joseph

2015-01-01

Ecological forecasting uses knowledge of physics, ecology and physiology to predict how ecosystems will change in the future in response to environmental factors. Further, Ecological Forecasting employs observations and models to predict the effects of environmental change on ecosystems. In doing so, it applies information from the physical, biological, and social sciences and promotes a scientific synthesis across the domains of physics, geology, chemistry, biology, and psychology. The goal is reliable forecasts that allow decision makers access to science-based tools in order to project changes in living systems. The next decadal survey will direct the development Earth Observation sensors and satellites for the next ten years. It is important that these new sensors and satellites address the requirements for ecosystem models, imagery, and other data for resource management. This presentation will give examples of these model inputs and some resources needed for NASA to continue effective Ecological Forecasting.

Operational Applications of Satellite Snowcover Observations

NASA Technical Reports Server (NTRS)

Rango, A. (Editor); Peterson, R. (Editor)

1980-01-01

The history of remote sensing of snow cover is reviewed and the following topics are covered: various techniques for interpreting LANDSAT and NOAA satellite data; the status of future systems for continuing snow hydrology applications; the use of snow cover observations in streamflow forecasts by Applications Systems Verification and Transfer participants and selected foreign investigators; and the benefits of using satellite snow cover data in runoff prediction.

Operational specification and forecasting advances for Dst, LEO thermospheric densities, and aviation radiation dose and dose rate

NASA Astrophysics Data System (ADS)

Tobiska, W. Kent

Space weather’s effects upon the near-Earth environment are due to dynamic changes in the energy transfer processes from the Sun’s photons, particles, and fields. Of the space environment domains that are affected by space weather, the magnetosphere, thermosphere, and even troposphere are key regions that are affected. Space Environment Technologies (SET) has developed and is producing innovative space weather applications. Key operational systems for providing timely information about the effects of space weather on these domains are SET’s Magnetosphere Alert and Prediction System (MAPS), LEO Alert and Prediction System (LAPS), and Automated Radiation Measurements for Aviation Safety (ARMAS) system. MAPS provides a forecast Dst index out to 6 days through the data-driven, redundant data stream Anemomilos algorithm. Anemomilos uses observational proxies for the magnitude, location, and velocity of solar ejecta events. This forecast index is used by satellite operations to characterize upcoming geomagnetic storms, for example. In addition, an ENLIL/Rice Dst prediction out to several days has also been developed and will be described. LAPS is the SET fully redundant operational system providing recent history, current epoch, and forecast solar and geomagnetic indices for use in operational versions of the JB2008 thermospheric density model. The thermospheric densities produced by that system, driven by the LAPS data, are forecast to 72-hours to provide the global mass densities for satellite operators. ARMAS is a project that has successfully demonstrated the operation of a micro dosimeter on aircraft to capture the real-time radiation environment due to Galactic Cosmic Rays and Solar Energetic Particles. The dose and dose-rates are captured on aircraft, downlinked in real-time via the Iridium satellites, processed on the ground, incorporated into the most recent NAIRAS global radiation climatology data runs, and made available to end users via the web and smart phone apps. ARMAS provides the “weather” of the radiation environment to improve air-crew and passenger safety. Many of the data products from MAPS, LAPS, and ARMAS are available on the SpaceWx smartphone app for iPhone, iPad, iPod, and Android professional users and public space weather education. We describe recent forecasting advances for moving the space weather information from these automated systems into operational, derivative products for communications, aviation, and satellite operations uses.

The impact of different background errors in the assimilation of satellite radiances and in-situ observational data using WRFDA for three rainfall events over Iran

NASA Astrophysics Data System (ADS)

Zakeri, Zeinab; Azadi, Majid; Ghader, Sarmad

2018-01-01

Satellite radiances and in-situ observations are assimilated through Weather Research and Forecasting Data Assimilation (WRFDA) system into Advanced Research WRF (ARW) model over Iran and its neighboring area. Domain specific background error based on x and y components of wind speed (UV) control variables is calculated for WRFDA system and some sensitivity experiments are carried out to compare the impact of global background error and the domain specific background errors, both on the precipitation and 2-m temperature forecasts over Iran. Three precipitation events that occurred over the country during January, September and October 2014 are simulated in three different experiments and the results for precipitation and 2-m temperature are verified against the verifying surface observations. Results show that using domain specific background error improves 2-m temperature and 24-h accumulated precipitation forecasts consistently, while global background error may even degrade the forecasts compared to the experiments without data assimilation. The improvement in 2-m temperature is more evident during the first forecast hours and decreases significantly as the forecast length increases.

Assessing the Impact of Advanced Satellite Observations in the NASA GEOS-5 Forecast System Using the Adjoint Method

NASA Technical Reports Server (NTRS)

Gelaro, Ron; Liu, Emily; Sienkiewicz, Meta

2011-01-01

The adjoint of a data assimilation system provides a flexible and efficient tool for estimating observation impacts on short-range weather forecasts. The impacts of any or all observations can be estimated simultaneously based on a single execution of the adjoint system. The results can be easily aggregated according to data type, location, channel, etc., making this technique especially attractive for examining the impacts of new hyper-spectral satellite instruments and for conducting regular, even near-real time, monitoring of the entire observing system. In this talk, we present results from the adjoint-based observation impact monitoring tool in NASA's GEOS-5 global atmospheric data assimilation and forecast system. The tool has been running in various off-line configurations for some time, and is scheduled to run as a regular part of the real-time forecast suite beginning in autumn 20 I O. We focus on the impacts of the newest components of the satellite observing system, including AIRS, IASI and GPS. For AIRS and IASI, it is shown that the vast majority of the channels assimilated have systematic positive impacts (of varying magnitudes), although some channels degrade the forecast. Of the latter, most are moisture-sensitive or near-surface channels. The impact of GPS observations in the southern hemisphere is found to be a considerable overall benefit to the system. In addition, the spatial variability of observation impacts reveals coherent patterns of positive and negative impacts that may point to deficiencies in the use of certain observations over, for example, specific surface types. When performed in conjunction with selected observing system experiments (OSEs), the adjoint results reveal both redundancies and dependencies between observing system impacts as observations are added or removed from the assimilation system. Understanding these dependencies appears to pose a major challenge for optimizing the use of the current observational network and defining requirements for future observing systems.

Study of the impact of satellite data in the analysis and forecasting of a SACZ episode using G3DVar

NASA Astrophysics Data System (ADS)

de Azevedo, H. B.; Goncalves, L.

2013-05-01

Earth observations from satellite have great importance and impact, in particular for operational weather and climate forecast centers in the Southern Hemisphere such as the Center for Weather Forecast and Climate Studies (CPTEC from its Portuguese acronym), a division of the Brazilian National Institute for Space Research (INPE from its Portuguese acronym). It is well known that such data is critical, in particular over Southern Hemisphere oceans where there is a lack of other information sources e.g. radiosonde and aircraft, where satellite data provides excellent spatial coverage with relatively frequent sampling in addition to sweeping continents, deserts, woodlands and other remote areas. Hence, studies like OSEs (Observing Systems Experiments) where we are able to test whether observational input data in an assimilation system degrades or improves analyzes and forecasts, are of great value. Furthermore OSEs applied to satellite observations are expected to show large impact due to its large amount of information compared to conventional observations. OSE also provides useful information on the efficiency of the system and this information can be used to improve the use of one or another observation system in the data assimilation process and to determine its relative importance compared to other observation systems. This is a technique where one or more observation systems are retained in the data assimilation process in order to assess the impact of the inclusion or exclusion of a particular observation on the quality of numerical weather prediction (NWP). One of the difficulties within the NWP nevertheless, is to predict the correct intensity of severe weather systems, which has great impact on the population. Over South America, an important weather system is the South Atlantic Convergence Zone (SACZ), which every year during summer yelds large amounts of rainfall over a band oriented northwest-southeast, extending from the Amazon to the Brazilian Southeast, with persistence of at least four days. It often is associated with the occurrence of landslides on slopes in heavily populated areas of Brazil such as Rio de Janeiro, killing and displacing hundreds to thousands of people. This work aims to study the impact of satellite data assimilated in the recently implemented Global 3DVar (G3DVar) assimilation system based on the Gridpoint Statistical Interpolation (GSI) in a situation of extreme event, such as the SACZ from January 8th 2013 to January 15th 2013. This system runs using a GCM from CPTEC/INPE (T299L64) corresponding to a horizontal resolution of approximately 44 km. A OSE was performed by removing the satellite data from the assimilation cycle and compared against a control experiment, were all available information (radiosondes, satellite, conventional observations, etc) was assimilated into the system. The OSE results presented in this work show the evaluation of the analyzes and up to 120 hours forecasts.

An investigation of the role of current and future remote sensing data systems in numerical meteorology

NASA Technical Reports Server (NTRS)

Diak, George R.; Smith, William L.

1993-01-01

The goals of this research endeavor have been to develop a flexible and relatively complete framework for the investigation of current and future satellite data sources in numerical meteorology. In order to realistically model how satellite information might be used for these purposes, it is necessary that Observing System Simulation Experiments (OSSEs) be as complete as possible. It is therefore desirable that these experiments simulate in entirety the sequence of steps involved in bringing satellite information from the radiance level through product retrieval to a realistic analysis and forecast sequence. In this project we have worked to make this sequence realistic by synthesizing raw satellite data from surrogate atmospheres, deriving satellite products from these data and subsequently producing analyses and forecasts using the retrieved products. The accomplishments made in 1991 are presented. The emphasis was on examining atmospheric soundings and microphysical products which we expect to produce with the launch of the Advanced Microwave Sounding Unit (AMSU), slated for flight in mid 1994.

The Properties of Convective Clouds Over the Western Pacific and Their Relationship to the Environment of Tropical Cyclones

DTIC Science & Technology

2010-09-30

oceans from radar , aircraft and satellite data; 2) Derive an accurate mesoscale environment of convective systems through the assimilation of satellite... radar , lidar and in-situ data; 3) Evaluate the quality of the global forecast system (e.g., Navy Operational Global Atmospheric Prediction System or...from Aqua and NASA Tropical Rainfall Measuring Mission (TRMM), 2) developing mesoscale data assimilation techniques to assimilate satellite, radar

4-D Cloud Water Content Fields Derived from Operational Satellite Data

NASA Technical Reports Server (NTRS)

Smith, William L., Jr.; Minnis, Patrick

2010-01-01

In order to improve operational safety and efficiency, the transportation industry, including aviation, has an urgent need for accurate diagnoses and predictions of clouds and associated weather conditions. Adverse weather accounts for 70% of all air traffic delays within the U.S. National Airspace System. The Federal Aviation Administration has determined that as much as two thirds of weather-related delays are potentially avoidable with better weather information and roughly 20% of all aviation accidents are weather related. Thus, it is recognized that an important factor in meeting the goals of the Next Generation Transportation System (NexGen) vision is the improved integration of weather information. The concept of a 4-D weather cube is being developed to address that need by integrating observed and forecasted weather information into a shared 4-D database, providing an integrated and nationally consistent weather picture for a variety of users and to support operational decision support systems. Weather analyses and forecasts derived using Numerical Weather Prediction (NWP) models are a critical tool that forecasters rely on for guidance and also an important element in current and future decision support systems. For example, the Rapid Update Cycle (RUC) and the recently implemented Rapid Refresh (RR) Weather Research and Forecast (WRF) models provide high frequency forecasts and are key elements of the FAA Aviation Weather Research Program. Because clouds play a crucial role in the dynamics and thermodynamics of the atmosphere, they must be adequately accounted for in NWP models. The RUC, for example, cycles at full resolution five cloud microphysical species (cloud water, cloud ice, rain, snow, and graupel) and has the capability of updating these fields from observations. In order to improve the models initial state and subsequent forecasts, cloud top altitude (or temperature, T(sub c)) derived from operational satellite data, surface observations of cloud base altitude, radar reflectivity, and lightning data are used to help build and remove clouds in the models assimilation system. Despite this advance and the many recent advances made in our understanding of cloud physical processes and radiative effects, many problems remain in adequately representing clouds in models. While the assimilation of cloud top information derived from operational satellite data has merit, other information is available that has not yet been exploited. For example, the vertically integrated cloud water content (CWC) or cloud water path (CWP) and cloud geometric thickness (delta Z) are standard products being derived routinely from operational satellite data. These and other cloud products have been validated under a variety of conditions. Since the uncertainties have generally been found to be less than those found in model analyses and forecasts, the satellite products should be suitable for data assimilation, provided an appropriate strategy can be developed that links the satellite-derived cloud parameters with cloud parameters specified in the model. In this paper, we briefly outline such a strategy and describe a methodology to retrieve cloud water content profiles from operational satellite data. Initial results and future plans are presented. It is expected that the direct assimilation of this new product will provide the most accurate depiction of the vertical distribution of cloud water ever produced at the high spatial and temporal resolution needed for short term weather analyses and forecasts.

Bias correction of satellite precipitation products for flood forecasting application at the Upper Mahanadi River Basin in Eastern India

NASA Astrophysics Data System (ADS)

Beria, H.; Nanda, T., Sr.; Chatterjee, C.

2015-12-01

High resolution satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF), etc., offer a promising alternative to flood forecasting in data scarce regions. At the current state-of-art, these products cannot be used in the raw form for flood forecasting, even at smaller lead times. In the current study, these precipitation products are bias corrected using statistical techniques, such as additive and multiplicative bias corrections, and wavelet multi-resolution analysis (MRA) with India Meteorological Department (IMD) gridded precipitation product,obtained from gauge-based rainfall estimates. Neural network based rainfall-runoff modeling using these bias corrected products provide encouraging results for flood forecasting upto 48 hours lead time. We will present various statistical and graphical interpretations of catchment response to high rainfall events using both the raw and bias corrected precipitation products at different lead times.

Tropical Pacific moisture variability: Its detection, synoptic structure and consequences in the general circulation

NASA Technical Reports Server (NTRS)

Mcguirk, James P.

1990-01-01

Satellite data analysis tools are developed and implemented for the diagnosis of atmospheric circulation systems over the tropical Pacific Ocean. The tools include statistical multi-variate procedures, a multi-spectral radiative transfer model, and the global spectral forecast model at NMC. Data include in-situ observations; satellite observations from VAS (moisture, infrared and visible) NOAA polar orbiters (including Tiros Operational Satellite System (TOVS) multi-channel sounding data and OLR grids) and scanning multichannel microwave radiometer (SMMR); and European Centre for Medium Weather Forecasts (ECHMWF) analyses. A primary goal is a better understanding of the relation between synoptic structures of the area, particularly tropical plumes, and the general circulation, especially the Hadley circulation. A second goal is the definition of the quantitative structure and behavior of all Pacific tropical synoptic systems. Finally, strategies are examined for extracting new and additional information from existing satellite observations. Although moisture structure is emphasized, thermal patterns are also analyzed. Both horizontal and vertical structures are studied and objective quantitative results are emphasized.

Projected Applications of a "Weather in a Box" Computing System at the NASA Short-Term Prediction Research and Transition (SPoRT) Center

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Molthan, Andrew; Zavodsky, Bradley T.; Case, Jonathan L.; LaFontaine, Frank J.; Srikishen, Jayanthi

2010-01-01

The NASA Short-term Prediction Research and Transition Center (SPoRT)'s new "Weather in a Box" resources will provide weather research and forecast modeling capabilities for real-time application. Model output will provide additional forecast guidance and research into the impacts of new NASA satellite data sets and software capabilities. By combining several research tools and satellite products, SPoRT can generate model guidance that is strongly influenced by unique NASA contributions.

The suitability of remotely sensed soil moisture for improving operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

2013-11-01

We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model for flood predictions with lead times up to 10 days. For this study, satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF, are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more data is assimilated into the system and the best performance is achieved with the assimilation of both discharge and satellite observations. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

The suitability of remotely sensed soil moisture for improving operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

2014-06-01

We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

Advanced solar irradiances applied to satellite and ionospheric operational systems

NASA Astrophysics Data System (ADS)

Tobiska, W. Kent; Schunk, Robert; Eccles, Vince; Bouwer, Dave

Satellite and ionospheric operational systems require solar irradiances in a variety of time scales and spectral formats. We describe the development of a system using operational grade solar irradiances that are applied to empirical thermospheric density models and physics-based ionospheric models used by operational systems that require a space weather characterization. The SOLAR2000 (S2K) and SOLARFLARE (SFLR) models developed by Space Environment Technologies (SET) provide solar irradiances from the soft X-rays (XUV) through the Far Ultraviolet (FUV) spectrum. The irradiances are provided as integrated indices for the JB2006 empirical atmosphere density models and as line/band spectral irradiances for the physics-based Ionosphere Forecast Model (IFM) developed by the Space Environment Corporation (SEC). We describe the integration of these irradiances in historical, current epoch, and forecast modes through the Communication Alert and Prediction System (CAPS). CAPS provides real-time and forecast HF radio availability for global and regional users and global total electron content (TEC) conditions.

Proposed Use of the NASA Ames Nebula Cloud Computing Platform for Numerical Weather Prediction and the Distribution of High Resolution Satellite Imagery

NASA Technical Reports Server (NTRS)

Limaye, Ashutosh S.; Molthan, Andrew L.; Srikishen, Jayanthi

2010-01-01

The development of the Nebula Cloud Computing Platform at NASA Ames Research Center provides an open-source solution for the deployment of scalable computing and storage capabilities relevant to the execution of real-time weather forecasts and the distribution of high resolution satellite data to the operational weather community. Two projects at Marshall Space Flight Center may benefit from use of the Nebula system. The NASA Short-term Prediction Research and Transition (SPoRT) Center facilitates the use of unique NASA satellite data and research capabilities in the operational weather community by providing datasets relevant to numerical weather prediction, and satellite data sets useful in weather analysis. SERVIR provides satellite data products for decision support, emphasizing environmental threats such as wildfires, floods, landslides, and other hazards, with interests in numerical weather prediction in support of disaster response. The Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS) has been configured for Nebula cloud computing use via the creation of a disk image and deployment of repeated instances. Given the available infrastructure within Nebula and the "infrastructure as a service" concept, the system appears well-suited for the rapid deployment of additional forecast models over different domains, in response to real-time research applications or disaster response. Future investigations into Nebula capabilities will focus on the development of a web mapping server and load balancing configuration to support the distribution of high resolution satellite data sets to users within the National Weather Service and international partners of SERVIR.

The potential of remotely sensed soil moisture for operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S.; Bierkens, M. F.

2013-12-01

Nowadays, remotely sensed soil moisture is readily available from multiple space born sensors. The high temporal resolution and global coverage make these products very suitable for large-scale land-surface applications. The potential to use these products in operational flood forecasting has thus far not been extensively studied. In this study, we evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the timing and height of the flood peak and low flows. EFAS is used for operational flood forecasting in Europe and uses a distributed hydrological model for flood predictions for lead times up to 10 days. Satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of only discharge observations. Discharge observations are available at the outlet and at six additional locations throughout the catchment. To assimilate soil moisture data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, derived from a detailed model-satellite soil moisture comparison study, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are used in that the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 10-15% on average, compared to assimilation of discharge only. The rank histograms show that the forecast is not biased. The timing errors in the flood predictions are decreased when soil moisture data is used and imminent floods can be forecasted with skill one day earlier. In conclusion, our study shows that assimilation of satellite soil moisture increases the performance of flood forecasting systems for large catchments, like the Upper Danube. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of future soil moisture missions with a higher spatial resolution like SMAP to improve near-real time flood forecasting in large catchments.

The 2017 Hurricane Season: A Revolution in Geostationary Weather Satellite Imaging and Data Processing

NASA Astrophysics Data System (ADS)

Weiner, A. M.; Gundy, J.; Brown-Bertold, B.; Yates, H.; Dobler, J. T.

2017-12-01

Since their introduction, geostationary weather satellites have enabled us to track hurricane life-cycle movement from development to dissipation. During the 2017 hurricane season, the new GOES-16 geostationary satellite demonstrated just how far we have progressed technologically in geostationary satellite imaging, with hurricane imagery showing never-before-seen detail of the hurricane eye and eyewall structure and life cycle. In addition, new ground system technology, leveraging high-performance computing, delivered imagery and data to forecasters with unprecedented speed—and with updates as often as every 30 seconds. As additional satellites and new products become operational, forecasters will be able to track hurricanes with even greater accuracy and assist in aftermath evaluations. This presentation will present glimpses into the past, a look at the present, and a prediction for the future utilization of geostationary satellites with respect to all facets of hurricane support.

Performance and Quality Assessment of the Forthcoming Copernicus Marine Service Global Ocean Monitoring and Forecasting Real-Time System

NASA Astrophysics Data System (ADS)

Lellouche, J. M.; Le Galloudec, O.; Greiner, E.; Garric, G.; Regnier, C.; Drillet, Y.

2016-02-01

Mercator Ocean currently delivers in real-time daily services (weekly analyses and daily forecast) with a global 1/12° high resolution system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. Along track altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.Since May 2015, Mercator Ocean opened the Copernicus Marine Service (CMS) and is in charge of the global ocean analyses and forecast, at eddy resolving resolution. In this context, R&D activities have been conducted at Mercator Ocean these last years in order to improve the real-time 1/12° global system for the next CMS version in 2016. The ocean/sea-ice model and the assimilation scheme benefit among others from the following improvements: large-scale and objective correction of atmospheric quantities with satellite data, new Mean Dynamic Topography taking into account the last version of GOCE geoid, new adaptive tuning of some observational errors, new Quality Control on the assimilated temperature and salinity vertical profiles based on dynamic height criteria, assimilation of satellite sea-ice concentration, new freshwater runoff from ice sheets melting …This presentation doesn't focus on the impact of each update, but rather on the overall behavior of the system integrating all updates. This assessment reports on the products quality improvements, highlighting the level of performance and the reliability of the new system.

GOES-S NASA Social

NASA Image and Video Library

2018-02-28

Mic Woltman, chief of the Fleet Systems Integration Branch of NASA's Launch Services Program, left, and Gabriel Rodriguez-Mena, a United Launch Alliance systems test engineer, speak to members of social media in the Kennedy Space Center’s Press Site auditorium. The briefing focused on the National Oceanic and Atmospheric Administration's, or NOAA's, Geostationary Operational Environmental Satellite, or GOES-S. The spacecraft is the second satellite in a series of next-generation NOAA weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting. GOES-S is slated to lift off at 5:02 p.m. EST on March 1, 2018 aboard a United Launch Alliance Atlas V rocket.

Applications of a shadow camera system for energy meteorology

NASA Astrophysics Data System (ADS)

Kuhn, Pascal; Wilbert, Stefan; Prahl, Christoph; Garsche, Dominik; Schüler, David; Haase, Thomas; Ramirez, Lourdes; Zarzalejo, Luis; Meyer, Angela; Blanc, Philippe; Pitz-Paal, Robert

2018-02-01

Downward-facing shadow cameras might play a major role in future energy meteorology. Shadow cameras directly image shadows on the ground from an elevated position. They are used to validate other systems (e.g. all-sky imager based nowcasting systems, cloud speed sensors or satellite forecasts) and can potentially provide short term forecasts for solar power plants. Such forecasts are needed for electricity grids with high penetrations of renewable energy and can help to optimize plant operations. In this publication, two key applications of shadow cameras are briefly presented.

Introducing Multisensor Satellite Radiance-Based Evaluation for Regional Earth System Modeling

NASA Technical Reports Server (NTRS)

Matsui, T.; Santanello, J.; Shi, J. J.; Tao, W.-K.; Wu, D.; Peters-Lidard, C.; Kemp, E.; Chin, M.; Starr, D.; Sekiguchi, M.;

A comparison of GLAS SAT and NMC high resolution NOSAT forecasts from 19 and 11 February 1976

NASA Technical Reports Server (NTRS)

Atlas, R.

1979-01-01

A subjective comparison of the Goddard Laboratory for Atmospheric Sciences (GLAS) and the National Meteorological Center (NMC) high resolution model forecasts is presented. Two cases where NMC's operational model in 1976 had serious difficulties in forecasting for the United States were examined. For each of the cases, the GLAS model forecasts from initial conditions which included satellite sounding data were compared directly to the NMC higher resolution model forecasts, from initial conditions which excluded the satellite data. The comparison showed that the GLAS satellite forecasts significantly improved upon the current NMC operational model's predictions in both cases.

Early Transition and Use of VIIRS and GOES-R Products by NWS Forecast Offices

NASA Technical Reports Server (NTRS)

Fuell, Kevin K.; Smith, Mathew; Jedlovec, Gary

2012-01-01

The Visible Infrared Imaging Radiometer Suite (VIIRS) on the NPOESS Preparatory Project (NPP) satellite, part of the Joint Polar Satellite System (JPSS), and the ABI and GLM sensors scheduled for the GOES-R geostationary satellite will bring advanced observing capabilities to the operational weather community. The NASA Short-term Prediction Research and Transition (SPoRT) project at Marshall Space Flight Center has been facilitating the use of real-time experimental and research satellite data by NWS Weather Forecast Offices (WFOs) for a number of years to demonstrate the planned capabilities of future sensors to address particular forecast challenges through improve situational awareness and short-term weather forecasts. For the NOAA GOES-R Proving Ground (PG) activity, SPoRT is developing and disseminating selected GOES-R proxy products to collaborating WFOs and National Centers. SPoRT developed the a pseudo-Geostationary Lightning Mapper product and helped in the transition of the Algorithm Working Group (AWG) Convective Initiation (CI) proxy product for the Hazardous Weather Testbed (HWT) Spring Experiment,. Along with its partner WFOs, SPoRT is evaluating MODIS/GOES Hybrid products, which brings ABI-like data sets from existing NASA instrumentation in front of the forecaster for everyday use. The Hybrid uses near real-time MODIS imagery to demonstrate future ABI capabilities, while utilizing standard GOES imagery to provide the temporal frequency of geostationary imagery expected by operational forecasters. In addition, SPoRT is collaborating with the GOES-R hydrology AWG to transition a baseline proxy product for rainfall rate / quantitative precipitation estimate (QPE) to the OCONUS regions. For VIIRS, SPoRT is demonstrating multispectral observing capabilities and the utility of low-light channels not previously available on operational weather satellites to address a variety of weather forecast challenges. This presentation will discuss the results of transitioning these products to collaborating WFOs throughout the country.

The Properties of Convective Clouds Over the Western Pacific and Their Relationship to the Environment of Tropical Cyclones

DTIC Science & Technology

2011-09-30

assimilating satellite, radar and in-situ observations for improved numerical simulations of major Typhoons (Jiangmi, Sinlaku, Nuri and Hagupit) during T- PARC ...oceans from radar , aircraft and satellite data; 2) Derive an accurate mesoscale environment of convective systems through the assimilation of satellite... radar , lidar and in-situ data; 3) Evaluate the quality of the global forecast system (e.g., Navy Operational Global Atmospheric Prediction System or

Assessing the impact of different satellite retrieval methods on forecast available potential energy

NASA Technical Reports Server (NTRS)

Whittaker, Linda M.; Horn, Lyle H.

1990-01-01

The effects of the inclusion of satellite temperature retrieval data, and of different satellite retrieval methods, on forecasts made with the NASA Goddard Laboratory for Atmospheres (GLA) fourth-order model were investigated using, as the parameter, the available potential energy (APE) in its isentropic form. Calculation of the APE were used to study the differences in the forecast sets both globally and in the Northern Hemisphere during 72-h forecast period. The analysis data sets used for the forecasts included one containing the NESDIS TIROS-N retrievals, the GLA retrievals using the physical inversion method, and a third, which did not contain satellite data, used as a control; two data sets, with and without satellite data, were used for verification. For all three data sets, the Northern Hemisphere values for the total APE showed an increase throughout the forecast period, mostly due to an increase in the zonal component, in contrast to the verification sets, which showed a steady level of total APE.

Regional Air Quality forecAST (RAQAST) Over the U.S

NASA Astrophysics Data System (ADS)

Yoshida, Y.; Choi, Y.; Zeng, T.; Wang, Y.

2005-12-01

A regional chemistry and transport modeling system is used to provide 48-hour forecast of the concentrations of ozone and its precursors over the United States. Meteorological forecast is conducted using the NCAR/Penn State MM5 model. The regional chemistry and transport model simulates the sources, transport, chemistry, and deposition of 24 chemical tracers. The lateral and upper boundary conditions of trace gas concentrations are specified using the monthly mean output from the global GEOS-CHEM model. The initial and boundary conditions for meteorological fields are taken from the NOAA AVN forecast. The forecast has been operational since August, 2003. Model simulations are evaluated using surface, aircraft, and satellite measurements in the A'hindcast' mode. The next step is an automated forecast evaluation system.

Assimilating InSAR Maps of Water Vapor to Improve Heavy Rainfall Forecasts: A Case Study With Two Successive Storms

NASA Astrophysics Data System (ADS)

Mateus, Pedro; Miranda, Pedro M. A.; Nico, Giovanni; Catalão, João.; Pinto, Paulo; Tomé, Ricardo

2018-04-01

Very high resolution precipitable water vapor maps obtained by the Sentinel-1 A synthetic aperture radar (SAR), using the SAR interferometry (InSAR) technique, are here shown to have a positive impact on the performance of severe weather forecasts. A case study of deep convection which affected the city of Adra, Spain, on 6-7 September 2015, is successfully forecasted by the Weather Research and Forecasting model initialized with InSAR data assimilated by the three-dimensional variational technique, with improved space and time distributions of precipitation, as observed by the local weather radar and rain gauge. This case study is exceptional because it consisted of two severe events 12 hr apart, with a timing that allows for the assimilation of both the ascending and descending satellite images, each for the initialization of each event. The same methodology applied to the network of Global Navigation Satellite System observations in Iberia, at the same times, failed to reproduce observed precipitation, although it also improved, in a more modest way, the forecast skill. The impact of precipitable water vapor data is shown to result from a direct increment of convective available potential energy, associated with important adjustments in the low-level wind field, favoring its release in deep convection. It is suggested that InSAR images, complemented by dense Global Navigation Satellite System data, may provide a new source of water vapor data for weather forecasting, since their sampling frequency could reach the subdaily scale by merging different SAR platforms, or when future geosynchronous radar missions become operational.

Development of On-line Wildfire Emissions for the Operational Canadian Air Quality Forecast System

NASA Astrophysics Data System (ADS)

Pavlovic, R.; Menard, S.; Chen, J.; Anselmo, D.; Paul-Andre, B.; Gravel, S.; Moran, M. D.; Davignon, D.

2013-12-01

An emissions processing system has been developed to incorporate near-real-time emissions from wildfires and large prescribed burns into Environment Canada's real-time GEM-MACH air quality (AQ) forecast system. Since the GEM-MACH forecast domain covers Canada and most of the USA, including Alaska, fire location information is needed for both of these large countries. Near-real-time satellite data are obtained and processed separately for the two countries for organizational reasons. Fire location and fuel consumption data for Canada are provided by the Canadian Forest Service's Canadian Wild Fire Information System (CWFIS) while fire location and emissions data for the U.S. are provided by the SMARTFIRE (Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation) system via the on-line BlueSky Gateway. During AQ model runs, emissions from individual fire sources are injected into elevated model layers based on plume-rise calculations and then transport and chemistry calculations are performed. This 'on the fly' approach to the insertion of emissions provides greater flexibility since on-line meteorology is used and reduces computational overhead in emission pre-processing. An experimental wildfire version of GEM-MACH was run in real-time mode for the summers of 2012 and 2013. 48-hour forecasts were generated every 12 hours (at 00 and 12 UTC). Noticeable improvements in the AQ forecasts for PM2.5 were seen in numerous regions where fire activity was high. Case studies evaluating model performance for specific regions, computed objective scores, and subjective evaluations by AQ forecasters will be included in this presentation. Using the lessons learned from the last two summers, Environment Canada will continue to work towards the goal of incorporating near-real-time intermittent wildfire emissions within the operational air quality forecast system.

INMARSAT-C SafetyNET

Science.gov Websites

Tsunamis 406 EPIRB's National Weather Service Marine Forecasts INMARSAT-C SafetyNET Marine Forecast Offices greater danger near shore or any shallow waters? NATIONAL WEATHER SERVICE PRODUCTS VIA INMARSAT-C SafetyNET Inmarsat-C SafetyNET is an internationally adopted, automated satellite system for promulgating

Improved Orbit Determination and Forecasts with an Assimilative Tool for Atmospheric Density and Satellite Drag Specification

NASA Astrophysics Data System (ADS)

Crowley, G.; Pilinski, M.; Sutton, E. K.; Codrescu, M.; Fuller-Rowell, T. J.; Matsuo, T.; Fedrizzi, M.; Solomon, S. C.; Qian, L.; Thayer, J. P.

2016-12-01

Much as aircraft are affected by the prevailing winds and weather conditions in which they fly, satellites are affected by the variability in density and motion of the near earth space environment. Drastic changes in the neutral density of the thermosphere, caused by geomagnetic storms or other phenomena, result in perturbations of LEO satellite motions through drag on the satellite surfaces. This can lead to difficulties in locating important satellites, temporarily losing track of satellites, and errors when predicting collisions in space. We describe ongoing work to build a comprehensive nowcast and forecast system for specifying the neutral atmospheric state related to orbital drag conditions. The system outputs include neutral density, winds, temperature, composition, and the satellite drag derived from these parameters. This modeling tool is based on several state-of-the-art coupled models of the thermosphere-ionosphere as well as several empirical models running in real-time and uses assimilative techniques to produce a thermospheric nowcast. This software will also produce 72 hour predictions of the global thermosphere-ionosphere system using the nowcast as the initial condition and using near real-time and predicted space weather data and indices as the inputs. Features of this technique include: • Satellite drag specifications with errors lower than current models • Altitude coverage up to 1000km • Background state representation using both first principles and empirical models • Assimilation of satellite drag and other datatypes • Real time capability • Ability to produce 72-hour forecasts of the atmospheric state In this paper, we will summarize the model design and assimilative architecture, and present preliminary validation results. Validation results will be presented in the context of satellite orbit errors and compared with several leading atmospheric models including the High Accuracy Satellite Drag Model, which is currently used operationally by the Air Force to specify neutral densities. As part of the analysis, we compare the drag observed by a variety of satellites which were not used as part of the assimilation-dataset and whose perigee altitudes span a range from 200km to 700 km.

Air Quality Forecasts Using the NASA GEOS Model

NASA Technical Reports Server (NTRS)

Keller, Christoph A.; Knowland, K. Emma; Nielsen, Jon E.; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Follette-Cook, Melanie; Liu, Junhua;

Estimation of PV energy production based on satellite data

NASA Astrophysics Data System (ADS)

Mazurek, G.

2015-09-01

Photovoltaic (PV) technology is an attractive source of power for systems without connection to power grid. Because of seasonal variations of solar radiation, design of such a power system requires careful analysis in order to provide required reliability. In this paper we present results of three-year measurements of experimental PV system located in Poland and based on polycrystalline silicon module. Irradiation values calculated from results of ground measurements have been compared with data from solar radiation databases employ calculations from of satellite observations. Good convergence level of both data sources has been shown, especially during summer. When satellite data from the same time period is available, yearly and monthly production of PV energy can be calculated with 2% and 5% accuracy, respectively. However, monthly production during winter seems to be overestimated, especially in January. Results of this work may be helpful in forecasting performance of similar PV systems in Central Europe and allow to make more precise forecasts of PV system performance than based only on tables with long time averaged values.

Assimilation of Feng-Yun-3B satellite microwave humidity sounder data over land

NASA Astrophysics Data System (ADS)

Chen, Keyi; Bormann, Niels; English, Stephen; Zhu, Jiang

2018-03-01

The ECMWF has been assimilating Feng-Yun-3B (FY-3B) satellite microwave humidity sounder (MWHS) data over ocean in an operational forecasting system since 24 September 2014. It is more difficult, however, to assimilate microwave observations over land and sea ice than over the open ocean due to higher uncertainties in land surface temperature, surface emissivity and less effective cloud screening. We compare approaches in which the emissivity is retrieved dynamically from MWHS channel 1 [150 GHz (vertical polarization)] with the use of an evolving emissivity atlas from 89 GHz observations from the MWHS onboard NOAA and EUMETSAT satellites. The assimilation of the additional data over land improves the fit of short-range forecasts to other observations, notably ATMS (Advanced Technology Microwave Sounder) humidity channels, and the forecast impacts are mainly neutral to slightly positive over the first five days. The forecast impacts are better in boreal summer and the Southern Hemisphere. These results suggest that the techniques tested allow for effective assimilation of MWHS/FY-3B data over land.

Space and ground segment performance of the FORMOSAT-3/COSMIC mission: four years in orbit

NASA Astrophysics Data System (ADS)

Fong, C.-J.; Whiteley, D.; Yang, E.; Cook, K.; Chu, V.; Schreiner, B.; Ector, D.; Wilczynski, P.; Liu, T.-Y.; Yen, N.

2011-01-01

The FORMOSAT-3/COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) mission consisting of six Low-Earth-Orbit (LEO) satellites is the world's first demonstration constellation using radio occultation signals from Global Positioning System (GPS) satellites. The radio occultation signals are retrieved in near real-time for global weather/climate monitoring, numerical weather prediction, and space weather research. The mission has processed on average 1400 to 1800 high-quality atmospheric sounding profiles per day. The atmospheric radio occultation soundings data are assimilated into operational numerical weather prediction models for global weather prediction, including typhoon/hurricane/cyclone forecasts. The radio occultation data has shown a positive impact on weather predictions at many national weather forecast centers. A proposed follow-on mission transitions the program from the current experimental research system to a significantly improved real-time operational system, which will reliably provide 8000 radio occultation soundings per day. The follow-on mission as planned will consist of 12 satellites with a data latency of 45 min, which will provide greatly enhanced opportunities for operational forecasts and scientific research. This paper will address the FORMOSAT-3/COSMIC system and mission overview, the spacecraft and ground system performance after four years in orbit, the lessons learned from the encountered technical challenges and observations, and the expected design improvements for the new spacecraft and ground system.

1/32° real-time global ocean prediction and value-added over 1/16° resolution

NASA Astrophysics Data System (ADS)

Shriver, J. F.; Hurlburt, H. E.; Smedstad, O. M.; Wallcraft, A. J.; Rhodes, R. C.

2007-03-01

A 1/32° global ocean nowcast/forecast system has been developed by the Naval Research Laboratory at the Stennis Space Center. It started running at the Naval Oceanographic Office in near real-time on 1 Nov. 2003 and has been running daily in real-time since 1 Mar. 2005. It became an operational system on 6 March 2006, replacing the existing 1/16° system which ceased operation on 12 March 2006. Both systems use the NRL Layered Ocean Model (NLOM) with assimilation of sea surface height from satellite altimeters and sea surface temperature from multi-channel satellite infrared radiometers. Real-time and archived results are available online at http://www.ocean.nrlssc.navy.mil/global_nlom. The 1/32° system has improvements over the earlier system that can be grouped into two categories: (1) better resolution and representation of dynamical processes and (2) design modifications. The design modifications are the result of accrued knowledge since the development of the earlier 1/16° system. The improved horizontal resolution of the 1/32° system has significant dynamical benefits which increase the ability of the model to accurately nowcast and skillfully forecast. At the finer resolution, current pathways and their transports become more accurate, the sea surface height (SSH) variability increases and becomes more realistic and even the global ocean circulation experiences some changes (including inter-basin exchange). These improvements make the 1/32° system a better dynamical interpolator of assimilated satellite altimeter track data, using a one-day model forecast as the first guess. The result is quantitatively more accurate nowcasts, as is illustrated by several model-data comparisons. Based on comparisons with ocean color imagery in the northwestern Arabian Sea and the Gulf of Oman, the 1/32° system has even demonstrated the ability to map small eddies, 25-75 km in diameter, with 70% reliability and a median eddy center location error of 22.5 km, a surprising and unanticipated result from assimilation of altimeter track data. For all of the eddies (50% small eddies), the reliability was 80% and the median eddy center location error was 29 km. The 1/32° system also exhibits improved forecast skill in relation to the 1/16° system. This is due to ( a) a more accurate initial condition for the forecast and ( b) better resolution and representation of critical dynamical processes (such as upper ocean - topographic coupling via mesoscale flow instabilities) which allow the model to more accurately evolve these features in time while running in forecast mode (forecast atmospheric forcing for the first 5 days, then gradually reverting toward climatology for the remainder of the 30-day forecast period). At 1/32° resolution, forecast SSH generally compares better with unassimilated observations and the anomaly correlation of the forecast SSH exceeds that from persistence by a larger amount than found in the 1/16° system.

Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI soil moisture data set

NASA Astrophysics Data System (ADS)

Drusch, M.

2007-02-01

Satellite-derived surface soil moisture data sets are readily available and have been used successfully in hydrological applications. In many operational numerical weather prediction systems the initial soil moisture conditions are analyzed from the modeled background and 2 m temperature and relative humidity. This approach has proven its efficiency to improve surface latent and sensible heat fluxes and consequently the forecast on large geographical domains. However, since soil moisture is not always related to screen level variables, model errors and uncertainties in the forcing data can accumulate in root zone soil moisture. Remotely sensed surface soil moisture is directly linked to the model's uppermost soil layer and therefore is a stronger constraint for the soil moisture analysis. For this study, three data assimilation experiments with the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been performed for the 2-month period of June and July 2002: a control run based on the operational soil moisture analysis, an open loop run with freely evolving soil moisture, and an experimental run incorporating TMI (TRMM Microwave Imager) derived soil moisture over the southern United States. In this experimental run the satellite-derived soil moisture product is introduced through a nudging scheme using 6-hourly increments. Apart from the soil moisture analysis, the system setup reflects the operational forecast configuration including the atmospheric 4D-Var analysis. Soil moisture analyzed in the nudging experiment is the most accurate estimate when compared against in situ observations from the Oklahoma Mesonet. The corresponding forecast for 2 m temperature and relative humidity is almost as accurate as in the control experiment. Furthermore, it is shown that the soil moisture analysis influences local weather parameters including the planetary boundary layer height and cloud coverage.

COSMIC Payload in NCAR-NASPO GPS Satellite System for Severe Weather Prediction

NASA Astrophysics Data System (ADS)

Lai-Chen, C.

Severe weather, such as cyclones, heavy rainfall, outburst of cold air, etc., results in great disaster all the world. It is the mission for the scientists to design a warning system, to predict the severe weather systems and to reduce the damage of the society. In Taiwan, National Satellite Project Office (NSPO) initiated ROCSAT-3 program at 1997. She scheduled the Phase I conceptual design to determine the mission for observation weather system. Cooperating with National Center of Atmospheric Research (NCAR), NSPO involved an international cooperation research and operation program to build a 32 GPS satellites system. NCAR will offer 24 GPS satellites. The total expanse will be US 100 millions. NSPO also provide US 80 millions for launching and system engineering operation. And NCAR will be responsible for Payload Control Center and Fiducial Network. The cooperative program contract has been signed by Taiwan National Science Council, Taipei Economic Cultural Office of United States and American Institute in Taiwan. One of the payload is COSMIC, Constellation Observation System for Meteorology, Ionosphere and Climate. It is a GPS meteorology instrument system. The system will observe the weather information, e. g. electron density profiles, horizontal and vertical TEC and CFT scintillation and communication outage maps. The mission is to obtain the weather data such as vertical temperature profiles, water vapor distribution and pressure distribution over the world for global weather forecasting, especially during the severe weather period. The COSMIC Conference held on November, 1998. The export license was also issued by Department of Commerce of Unites States at November, 1998. Recently, NSPO begun to train their scientists to investigate the system. Scientists simulate the observation data to combine the existing routine satellite infrared cloud maps, radar echo and synoptic weather analysis for severe weather forecasting. It is hopeful to provide more accurate weather analysis for forecasting and decreasing the damage of the disasters over the area concerned.

Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting

NASA Astrophysics Data System (ADS)

Wardah, T.; Abu Bakar, S. H.; Bardossy, A.; Maznorizan, M.

2008-07-01

SummaryFrequent flash-floods causing immense devastation in the Klang River Basin of Malaysia necessitate an improvement in the real-time forecasting systems being used. The use of meteorological satellite images in estimating rainfall has become an attractive option for improving the performance of flood forecasting-and-warning systems. In this study, a rainfall estimation algorithm using the infrared (IR) information from the Geostationary Meteorological Satellite-5 (GMS-5) is developed for potential input in a flood forecasting system. Data from the records of GMS-5 IR images have been retrieved for selected convective cells to be trained with the radar rain rate in a back-propagation neural network. The selected data as inputs to the neural network, are five parameters having a significant correlation with the radar rain rate: namely, the cloud-top brightness-temperature of the pixel of interest, the mean and the standard deviation of the temperatures of the surrounding five by five pixels, the rate of temperature change, and the sobel operator that indicates the temperature gradient. In addition, three numerical weather prediction (NWP) products, namely the precipitable water content, relative humidity, and vertical wind, are also included as inputs. The algorithm is applied for the areal rainfall estimation in the upper Klang River Basin and compared with another technique that uses power-law regression between the cloud-top brightness-temperature and radar rain rate. Results from both techniques are validated against previously recorded Thiessen areal-averaged rainfall values with coefficient correlation values of 0.77 and 0.91 for the power-law regression and the artificial neural network (ANN) technique, respectively. An extra lead time of around 2 h is gained when the satellite-based ANN rainfall estimation is coupled with a rainfall-runoff model to forecast a flash-flood event in the upper Klang River Basin.

Frontiers of Remote Sensing of the Oceans and Troposphere from Air and Space Platforms

NASA Technical Reports Server (NTRS)

1984-01-01

Several areas of remote sensing are addressed including: future satellite systems; air-sea interaction/wind; ocean waves and spectra/S.A.R.; atmospheric measurements (particulates and water vapor); synoptic and weather forecasting; topography; bathymetry; sea ice; and impact of remote sensing on synoptic analysis/forecasting.

GOES-S Mission Science Briefing

NASA Image and Video Library

2018-02-27

In the Kennedy Space Center's Press Site auditorium, Jim Roberts, a scientist with the Earth System Research Laboratory's Office of Atmospheric Research for NOAA, speaks to members of the media at a mission briefing on National Oceanic and Atmospheric Administration's, or NOAA's, Geostationary Operational Environmental Satellite, or GOES-S. The spacecraft is the second satellite in a series of next-generation NOAA weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting. GOES-S is slated to lift off at 5:02 p.m. EST on March 1, 2018 aboard a United Launch Alliance Atlas V rocket.

GOES-S NASA Social

NASA Image and Video Library

2018-02-28

Gabriel Rodriguez-Mena, a United Launch Alliance systems test engineer, speaks to members of social media in the Kennedy Space Center’s Press Site auditorium. The briefing focused on the National Oceanic and Atmospheric Administration's, or NOAA's, Geostationary Operational Environmental Satellite, or GOES-S. The spacecraft is the second satellite in a series of next-generation NOAA weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting. GOES-S is slated to lift off at 5:02 p.m. EST on March 1, 2018 aboard a United Launch Alliance Atlas V rocket.

The Properties of Convective Clouds Over the Western Pacific and Their Relationship to the Environment of Tropical Cyclones

DTIC Science & Technology

2009-09-30

from radar , aircraft and satellite data; 2) Derive an accurate mesoscale environment of convective systems through the assimilation of satellite... radar , lidar and in-situ data; 3) Evaluate the quality of the global forecast system (e.g., Navy Operational Global Atmospheric Prediction System or...ABSTRACT unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 satellite, radar , lidar and in-situ data

Lightning-generated whistler waves observed by probes on the Communication/Navigation Outage Forecast System satellite at low latitudes

NASA Astrophysics Data System (ADS)

Holzworth, R. H.; McCarthy, M. P.; Pfaff, R. F.; Jacobson, A. R.; Willcockson, W. L.; Rowland, D. E.

2011-06-01

Direct evidence is presented for a causal relationship between lightning and strong electric field transients inside equatorial ionospheric density depletions. In fact, these whistler mode plasma waves may be the dominant electric field signal within such depletions. Optical lightning data from the Communication/Navigation Outage Forecast System (C/NOFS) satellite and global lightning location information from the World Wide Lightning Location Network are presented as independent verification that these electric field transients are caused by lightning. The electric field instrument on C/NOFS routinely measures lightning-related electric field wave packets or sferics, associated with simultaneous measurements of optical flashes at all altitudes encountered by the satellite (401-867 km). Lightning-generated whistler waves have abundant access to the topside ionosphere, even close to the magnetic equator.

Lightning-Generated Whistler Waves Observed by Probes On The Communication/Navigation Outage Forecast System Satellite at Low Latitudes

NASA Technical Reports Server (NTRS)

Holzworth, R. H.; McCarthy, M. P.; Pfaff, R. F.; Jacobson, A. R.; Willcockson, W. L.; Rowland, D. E.

2011-01-01

Direct evidence is presented for a causal relationship between lightning and strong electric field transients inside equatorial ionospheric density depletions. In fact, these whistler mode plasma waves may be the dominant electric field signal within such depletions. Optical lightning data from the Communication/Navigation Outage Forecast System (C/NOFS) satellite and global lightning location information from the World Wide Lightning Location Network are presented as independent verification that these electric field transients are caused by lightning. The electric field instrument on C/NOFS routinely measures lightning ]related electric field wave packets or sferics, associated with simultaneous measurements of optical flashes at all altitudes encountered by the satellite (401.867 km). Lightning ]generated whistler waves have abundant access to the topside ionosphere, even close to the magnetic equator.

Forecasting E > 50-MeV Proton Events with the Proton Prediction System (PPS)

NASA Astrophysics Data System (ADS)

Kahler, S. W.; White, S. M.; Ling, A. G.

2017-12-01

Forecasting solar energetic (E > 10 MeV) particle (SEP) events is an important element of space weather. While several models have been developed for use in forecasting such events, satellite operations are particularly vulnerable to higher-energy (> 50 MeV) SEP events. Here we validate one model, the proton prediction system (PPS), which extends to that energy range. We first develop a data base of E > 50-MeV proton events > 1.0 proton flux units (pfu) events observed on the GOES satellite over the period 1986 to 2016. We modify the PPS to forecast proton events at the reduced level of 1 pfu and run PPS for four different solar input parameters: (1) all > M5 solar X-ray flares; (2) all > 200 sfu 8800-MHz bursts with associated > M5 flares; (3) all > 500 sfu 8800-MHz bursts; and (4) all > 5000 sfu 8800-MHz bursts. For X-ray flare inputs the forecasted event peak intensities and fluences are compared with observed values. The validation contingency tables and skill scores are calculated for all groups and used as a guide to use of the PPS. We plot the false alarms and missed events as functions of solar source longitude.

Spatio-temporal pattern clustering for skill assessment of the Korea Operational Oceanographic System

NASA Astrophysics Data System (ADS)

Kim, J.; Park, K.

2016-12-01

In order to evaluate the performance of operational forecast models in the Korea operational oceanographic system (KOOS) which has been developed by Korea Institute of Ocean Science and Technology (KIOST), a skill assessment (SA) tool has developed and provided multiple skill metrics including not only correlation and error skills by comparing predictions and observation but also pattern clustering with numerical models, satellite, and observation. The KOOS has produced 72 hours forecast information on atmospheric and hydrodynamic forecast variables of wind, pressure, current, tide, wave, temperature, and salinity at every 12 hours per day produced by operating numerical models such as WRF, ROMS, MOM5, WW-III, and SWAN and the SA has conducted to evaluate the forecasts. We have been operationally operated several kinds of numerical models such as WRF, ROMS, MOM5, MOHID, WW-III. Quantitative assessment of operational ocean forecast model is very important to provide accurate ocean forecast information not only to general public but also to support ocean-related problems. In this work, we propose a method of pattern clustering using machine learning method and GIS-based spatial analytics to evaluate spatial distribution of numerical models and spatial observation data such as satellite and HF radar. For the clustering, we use 10 or 15 years-long reanalysis data which was computed by the KOOS, ECMWF, and HYCOM to make best matching clusters which are classified physical meaning with time variation and then we compare it with forecast data. Moreover, for evaluating current, we develop extraction method of dominant flow and apply it to hydrodynamic models and HF radar's sea surface current data. By applying pattern clustering method, it allows more accurate and effective assessment of ocean forecast models' performance by comparing not only specific observation positions which are determined by observation stations but also spatio-temporal distribution of whole model areas. We believe that our proposed method will be very useful to examine and evaluate large amount of numerical modeling data as well as satellite data.

Regional and Coastal Prediction with the Relocatable Ocean Nowcast/Forecast System

DTIC Science & Technology

2014-09-01

and those that may be resolved with a suite of satellite altimeters when several are present and operational (~ 100 km). The altimeter data provide...September 2014 47 The observational data used for assimilation include satellite sea surface temperature (SST), satellite altimeter sea surface height...anomaly (SSHA), satellite microwave-derived sea ice concentration, and in situ surface and profile data from sensors on ships; drifters; fixed buoys

Comparison of three different methods of perturbing the potential vorticity field in mesoscale forecasts of Mediterranean heavy precipitation events: PV-gradient, PV-adjoint and PV-satellite

NASA Astrophysics Data System (ADS)

Vich, M.; Romero, R.; Richard, E.; Arbogast, P.; Maynard, K.

2010-09-01

Heavy precipitation events occur regularly in the western Mediterranean region. These events often have a high impact on the society due to economic and personal losses. The improvement of the mesoscale numerical forecasts of these events can be used to prevent or minimize their impact on the society. In previous studies, two ensemble prediction systems (EPSs) based on perturbing the model initial and boundary conditions were developed and tested for a collection of high-impact MEDEX cyclonic episodes. These EPSs perturb the initial and boundary potential vorticity (PV) field through a PV inversion algorithm. This technique ensures modifications of all the meteorological fields without compromising the mass-wind balance. One EPS introduces the perturbations along the zones of the three-dimensional PV structure presenting the local most intense values and gradients of the field (a semi-objective choice, PV-gradient), while the other perturbs the PV field over the MM5 adjoint model calculated sensitivity zones (an objective method, PV-adjoint). The PV perturbations are set from a PV error climatology (PVEC) that characterizes typical PV errors in the ECMWF forecasts, both in intensity and displacement. This intensity and displacement perturbation of the PV field is chosen randomly, while its location is given by the perturbation zones defined in each ensemble generation method. Encouraged by the good results obtained by these two EPSs that perturb the PV field, a new approach based on a manual perturbation of the PV field has been tested and compared with the previous results. This technique uses the satellite water vapor (WV) observations to guide the correction of initial PV structures. The correction of the PV field intents to improve the match between the PV distribution and the WV image, taking advantage of the relation between dark and bright features of WV images and PV anomalies, under some assumptions. Afterwards, the PV inversion algorithm is applied to run a forecast with the corresponding perturbed initial state (PV-satellite). The non hydrostatic MM5 mesoscale model has been used to run all forecasts. The simulations are performed for a two-day period with a 22.5 km resolution domain (Domain 1 in http://mm5forecasts.uib.es) nested in the ECMWF large-scale forecast fields. The MEDEX cyclone of 10 June 2000, also known as the Montserrat Case, is a suitable testbed to compare the performance of each ensemble and the PV-satellite method. This case is characterized by an Atlantic upper-level trough and low-level cold front which generated a stationary mesoscale cyclone over the Spanish Mediterranean coast, advecting warm and moist air toward Catalonia from the Mediterranean Sea. The consequences of the resulting mesoscale convective system were 6-h accumulated rainfall amounts of 180 mm with estimated material losses to exceed 65 million euros by media. The performace of both ensemble forecasting systems and PV-satellite technique for our case study is evaluated through the verification of the rainfall field. Since the EPSs are probabilistic forecasts and the PV-satellite is deterministic, their comparison is done using the individual ensemble members. Therefore the verification procedure uses deterministic scores, like the ROC curve, the Taylor diagram or the Q-Q plot. These scores cover the different quality attributes of the forecast such as reliability, resolution, uncertainty and sharpness. The results show that the PV-satellite technique performance lies within the performance range obtained by both ensembles; it is even better than the non-perturbed ensemble member. Thus, perturbing randomly using the PV error climatology and introducing the perturbations in the zones given by each EPS captures the mismatch between PV and WV fields better than manual perturbations made by an expert forecaster, at least for this case study.

Replacing climatological potential evapotranspiration estimates with dynamic satellite-based observations in operational hydrologic prediction models

NASA Astrophysics Data System (ADS)

Franz, K. J.; Bowman, A. L.; Hogue, T. S.; Kim, J.; Spies, R.

2011-12-01

In the face of a changing climate, growing populations, and increased human habitation in hydrologically risky locations, both short- and long-range planners increasingly require robust and reliable streamflow forecast information. Current operational forecasting utilizes watershed-scale, conceptual models driven by ground-based (commonly point-scale) observations of precipitation and temperature and climatological potential evapotranspiration (PET) estimates. The PET values are derived from historic pan evaporation observations and remain static from year-to-year. The need for regional dynamic PET values is vital for improved operational forecasting. With the advent of satellite remote sensing and the adoption of a more flexible operational forecast system by the National Weather Service, incorporation of advanced data products is now more feasible than in years past. In this study, we will test a previously developed satellite-derived PET product (UCLA MODIS-PET) in the National Weather Service forecast models and compare the model results to current methods. The UCLA MODIS-PET method is based on the Priestley-Taylor formulation, is driven with MODIS satellite products, and produces a daily, 250m PET estimate. The focus area is eight headwater basins in the upper Midwest U.S. There is a need to develop improved forecasting methods for this region that are able to account for climatic and landscape changes more readily and effectively than current methods. This region is highly flood prone yet sensitive to prolonged dry periods in late summer and early fall, and is characterized by a highly managed landscape, which has drastically altered the natural hydrologic cycle. Our goal is to improve model simulations, and thereby, the initial conditions prior to the start of a forecast through the use of PET values that better reflect actual watershed conditions. The forecast models are being tested in both distributed and lumped mode.

An Open-Book Modular Watershed Modeling Framework for Rapid Prototyping of GPM- based Flood Forecasting in International River Basins

NASA Astrophysics Data System (ADS)

Katiyar, N.; Hossain, F.

2006-05-01

Floods have always been disastrous for human life. It accounts for about 15 % of the total death related to natural disasters. There are around 263 transboundary river basins listed by UNESCO, wherein at least 30 countries have more than 95% of their territory locked in one or more such transboundary basins. For flood forecasting in the lower riparian nations of these International River Basins (IRBs), real-time rainfall data from upstream nations is naturally the most critical factor governing the forecasting effectiveness. However, many upstream nations fail to provide data to the lower riparian nations due to a lack of in-situ rainfall measurement infrastructure or a lack of a treaty for real-time sharing of rainfall data. A potential solution is therefore to use satellites that inherently measure rainfall across political boundaries. NASA's proposed Global Precipitation Measurement (GPM) mission appears very promising in providing this vital rainfall information under the data- limited scenario that will continue to prevail in most IRBs. However, satellite rainfall is associated with uncertainty and hence, proper characterization of the satellite rainfall error propagation in hydrologic models for flood forecasting is a critical priority that should be resolved in the coming years in anticipation of GPM. In this study, we assess an open book modular watershed modeling approach for estimating the expected error in flood forecasting related to GPM rainfall data. Our motivation stems from the critical challenge in identifying the specific IRBs that would benefit from a pre-programmed satellite-based forecasting system in anticipation of GPM. As the number of flood-prone IRBs is large, conventional data-intensive implementation of existing physically-based distributed hydrologic models on case-by-case IRBs is considered time-consuming for completing such a global assessment. A more parsimonious approach is justified at the expense of a tolerable loss of detail and accuracy. Through assessment of our proposed modular modeling framework, we present our initial understanding in resolving the fundamental question - Can a parsimonious open-book watershed modeling framework be a physically consistent proxy for rapid and global identification of IRBs in greater need of a GPM-based flood forecasting system?

Improved track forecasting of a typhoon reaching landfall from four-dimensional variational data assimilation of AMSU-A retrieved data

NASA Astrophysics Data System (ADS)

Zhao, Ying; Wang, Bin; Ji, Zhongzhen; Liang, Xudong; Deng, Guo; Zhang, Xin

2005-07-01

In this study, an attempt to improve typhoon forecasts is made by incorporating three-dimensional Advanced Microwave Sounding Unit-A (AMSU-A) retrieved wind and temperature and the central sea level pressure of cyclones from typhoon reports or bogus surface low data into initial conditions, on the basis of the Fifth-Generation National Center for Atmospheric Research/Pennsylvania State University Mesoscale Model (MM5) four-dimensional variational data assimilation (4DVar) system with a full-physics adjoint model. All the above-mentioned data are found to be useful for improvement of typhoon forecasts in this mesoscale data assimilation experiment. The comparison tests showed the following results: (1) The assimilation of the satellite-retrieved data was found to have a positive impact on the typhoon track forecast, but the landing position error is ˜150 km. (2) The assimilation of both the satellite-retrieved data and moving information of the typhoon center dramatically improved the track forecast and captured the recurvature and landfall. The mean track error during the 72-hour forecast is 69 km. The predicted typhoon intensity, however, is much weaker than that from observations. (3) The assimilation of both the satellite-retrieved data and the bogus surface low data improved the intensity and track forecasts more significantly than the assimilation of only bogus surface low data (bogus data assimilation) did. The mean errors during the 72-hour forecast are 2.6 hPa for the minimum sea level pressure and 87 km for track position. However, the forecasted landing time is ˜6 hours earlier than the observed one.

Research Review, 1983

NASA Technical Reports Server (NTRS)

1984-01-01

The Global Modeling and Simulation Branch (GMSB) of the Laboratory for Atmospheric Sciences (GLAS) is engaged in general circulation modeling studies related to global atmospheric and oceanographic research. The research activities discussed are organized into two disciplines: Global Weather/Observing Systems and Climate/Ocean-Air Interactions. The Global Weather activities are grouped in four areas: (1) Analysis and Forecast Studies, (2) Satellite Observing Systems, (3) Analysis and Model Development, (4) Atmospheric Dynamics and Diagnostic Studies. The GLAS Analysis/Forecast/Retrieval System was applied to both FGGE and post FGGE periods. The resulting analyses have already been used in a large number of theoretical studies of atmospheric dynamics, forecast impact studies and development of new or improved algorithms for the utilization of satellite data. Ocean studies have focused on the analysis of long-term global sea surface temperature data, for use in the study of the response of the atmosphere to sea surface temperature anomalies. Climate research has concentrated on the simulation of global cloudiness, and on the sensitivities of the climate to sea surface temperature and ground wetness anomalies.

Integrating observation and statistical forecasts over sub-Saharan Africa to support Famine Early Warning

USGS Publications Warehouse

Funk, Chris; Verdin, James P.; Husak, Gregory

2007-01-01

Famine early warning in Africa presents unique challenges and rewards. Hydrologic extremes must be tracked and anticipated over complex and changing climate regimes. The successful anticipation and interpretation of hydrologic shocks can initiate effective government response, saving lives and softening the impacts of droughts and floods. While both monitoring and forecast technologies continue to advance, discontinuities between monitoring and forecast systems inhibit effective decision making. Monitoring systems typically rely on high resolution satellite remote-sensed normalized difference vegetation index (NDVI) and rainfall imagery. Forecast systems provide information on a variety of scales and formats. Non-meteorologists are often unable or unwilling to connect the dots between these disparate sources of information. To mitigate these problem researchers at UCSB's Climate Hazard Group, NASA GIMMS and USGS/EROS are implementing a NASA-funded integrated decision support system that combines the monitoring of precipitation and NDVI with statistical one-to-three month forecasts. We present the monitoring/forecast system, assess its accuracy, and demonstrate its application in food insecure sub-Saharan Africa.

Weather assessment and forecasting

NASA Technical Reports Server (NTRS)

1977-01-01

Data management program activities centered around the analyses of selected far-term Office of Applications (OA) objectives, with the intent of determining if significant data-related problems would be encountered and if so what alternative solutions would be possible. Three far-term (1985 and beyond) OA objectives selected for analyses as having potential significant data problems were large-scale weather forecasting, local weather and severe storms forecasting, and global marine weather forecasting. An overview of general weather forecasting activities and their implications upon the ground based data system is provided. Selected topics were specifically oriented to the use of satellites.

Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoff, Thomas Hoff; Kankiewicz, Adam

Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP)more » forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest uncertainties. This work culminated in a GO decision being made by the California ISO to include zonal BTM forecasts into its operational load forecasting system. The California ISO’s Manager of Short Term Forecasting, Jim Blatchford, summarized the research performed in this project with the following quote: “The behind-the-meter (BTM) California ISO region forecasting research performed by Clean Power Research and sponsored by the Department of Energy’s SUNRISE program was an opportunity to verify value and demonstrate improved load forecast capability. In 2016, the California ISO will be incorporating the BTM forecast into the Hour Ahead and Day Ahead load models to look for improvements in the overall load forecast accuracy as BTM PV capacity continues to grow.”« less

On the assimilation of satellite derived soil moisture in numerical weather prediction models

NASA Astrophysics Data System (ADS)

Drusch, M.

2006-12-01

Satellite derived surface soil moisture data sets are readily available and have been used successfully in hydrological applications. In many operational numerical weather prediction systems the initial soil moisture conditions are analysed from the modelled background and 2 m temperature and relative humidity. This approach has proven its efficiency to improve surface latent and sensible heat fluxes and consequently the forecast on large geographical domains. However, since soil moisture is not always related to screen level variables, model errors and uncertainties in the forcing data can accumulate in root zone soil moisture. Remotely sensed surface soil moisture is directly linked to the model's uppermost soil layer and therefore is a stronger constraint for the soil moisture analysis. Three data assimilation experiments with the Integrated Forecast System (IFS) of the European Centre for Medium-range Weather Forecasts (ECMWF) have been performed for the two months period of June and July 2002: A control run based on the operational soil moisture analysis, an open loop run with freely evolving soil moisture, and an experimental run incorporating bias corrected TMI (TRMM Microwave Imager) derived soil moisture over the southern United States through a nudging scheme using 6-hourly departures. Apart from the soil moisture analysis, the system setup reflects the operational forecast configuration including the atmospheric 4D-Var analysis. Soil moisture analysed in the nudging experiment is the most accurate estimate when compared against in-situ observations from the Oklahoma Mesonet. The corresponding forecast for 2 m temperature and relative humidity is almost as accurate as in the control experiment. Furthermore, it is shown that the soil moisture analysis influences local weather parameters including the planetary boundary layer height and cloud coverage. The transferability of the results to other satellite derived soil moisture data sets will be discussed.

EarthSat spring wheat yield system test 1975, appendix 4

NASA Technical Reports Server (NTRS)

1976-01-01

A computer system is presented which processes meteorological data from both ground observations and meteorologic satellites to define plant weather aspects on a four time per day basis. Plant growth stages are calculated and soil moisture profiles are defined by the system. The EarthSat system assesses plant stress and prepares forecasts of end-of-year yields. The system was used to forecast spring wheat yields in the upper Great Plains states. Hardware and software documentation is provided.

Contrail Tracking and ARM Data Product Development

NASA Technical Reports Server (NTRS)

Duda, David P.; Russell, James, III

2005-01-01

A contrail tracking system was developed to help in the assessment of the effect of commercial jet contrails on the Earth's radiative budget. The tracking system was built by combining meteorological data from the Rapid Update Cycle (RUC) numerical weather prediction model with commercial air traffic flight track data and satellite imagery. A statistical contrail-forecasting model was created a combination of surface-based contrail observations and numerical weather analyses and forecasts. This model allows predictions of widespread contrail occurrences for contrail research on either a real-time basis or for long-term time scales. Satellite-derived cirrus cloud properties in polluted and unpolluted regions were compared to determine the impact of air traffic on cirrus.

Visualizing and Integrating AFSCN Utilization into a Common Operational Picture

NASA Astrophysics Data System (ADS)

Hays, B.; Carlile, A.; Mitchell, T.

The Department of Defense (DoD) and the 50th Space Network Operations Group Studies and Analysis branch (50th SCS/SCXI), located at Schriever AFB Colorado, face the unique challenge of forecasting the expected near term and future utilization of the Air Force Satellite Control Network (AFSCN). The forecasting timeframe covers the planned load from the current date to ten years out. The various satellite missions, satellite requirements, orbital regions, and ground architecture dynamics provide the model inputs and constraints that are used in generating the forecasted load. The AFSCN is the largest network the Air Force uses to control satellites worldwide. Each day, network personnel perform over 500 scheduled events-from satellite maneuvers to critical data downloads. The Forecasting Objective is to provide leadership with the insights necessary to manage the network today and tomorrow. For both today's needs and future needs, SCXI develops AFSCN utilization forecasts to optimize the ground system's coverage and capacity to meet user satellite requirements. SCXI also performs satellite program specific studies to determine network support feasibility. STK and STK Scheduler form the core of the tools used by SCXI. To establish this tool suite, we had to evaluate, evolve, and validate both the COTS products and our own developed code and processes. This began with calibrating the network model to emulate the real life scheduling environment of the AFSCN. Multiple STK Scheduler optimizing (de-confliction) algorithms, including Multi-Pass, Sequential, Random, and Neural, were evaluated and adjusted to determine applicability to the model and the accuracy of the prediction. Additionally, the scheduling Figure of Merit (FOM), which permits custom weighting of various parameters, was analyzed and tested to achieve the most accurate real life result. With the inherent capabilities of STK and the ability to wrap and automate output, SCXI is now able to visually communicate satellite loads in a manner never seen before in AFSCN management meetings. Scenarios such as regional antenna load stress, satellite missed opportunities, and the overall network "big picture" can be visually displayed in 3D versus the textual and line graph methods used for many years. This is the first step towards an integrated space awareness picture with an operational focus. SCXI is working on taking the visual forecast concept farther and begin fusing multiple sources of data to build a 50 SW Common Operating Picture (COP). The vision is to integrate more effective orbital determination processes, resource outages, current and forecasted satellite mission requirements, and future architectural changes into a real-time visual status to enable quick and responsive decisions. This COP would be utilized in a Wing Operations Center to provide up to the minute network status on where satellites are, which ground resources are in contact with them, and what resources are down. The ability to quickly absorb and process this data will enhance decision analysis and save valuable time in both day to day operations and wartime scenarios.

Application of Satellite information (JASON-2) in improvement of Flood Forecasting and Early Warning Service in Bangladesh

NASA Astrophysics Data System (ADS)

Hossain, M. A.; Anderson, E. R.; Bhuiyan, M. A.; Hossain, F.; Shah-Newaz, S. M.

2014-12-01

Bangladesh is the lowest riparian of the huge system of the Ganges, Brahmaputra and Meghna (GBM) basins, second to that of Amazan, with 1.75 million sq-km catchment area, only 7% is inside Bangladesh. High inflow from GBM associated with the intense rainfall is the source of flood in Bangladesh. Flood Forecasting and Early Warning (FFEW) is the mandate and responsibility of Bangladesh Water Development Board (BWDB) and Flood Forecasting and Warning Center (FFWC) under BWDB has been carrying out this responsibility since 1972 and operational on 7-days a week during monsoon (May to October). FFEW system started with few hours lead time has been upgraded up to to 5-days with reasonable accuracy. At FFWC numerical Hydrodynamic model is used for generating water level (WL) forecast upto 5-days at 54 points on 29 rivers based on real-time observed WL of 83 and rainfall of 56 stations with boundary estimationa on daily basis. Main challenge of this system is the boundary estimation is the limited upstream data of the transboundary rivers, obstacle for increasing lead-time for FFEW. The satellite based upper catchment data may overcome this limitation. Recent NASA-French joint Satellite mission JASON-2 records Water Elevation (WE) and it may be used within 24 hours. Using JASON-2 recorded WE data of 4 and 3 virtual stations on the Ganges and Brahmaputra rivers , respectively (upper catchment), a new methodology has been developed for increasing lead time of forecast. Correlation between the JASON-2 recorded WE on the virtual stations at the upper catchment and WL of 2 dominating boundary stations at model boundary on the Ganges and Brahmaputra has been derived for generating WL forecast at those 2 boundary stations, which used as input in model. FFWC has started experimental 8-days lead-time WL forecast at 09 stations (5 in Brahmaputra and 4 in Ganges) using generated boundary data and regularly updating the results in the website. The trend of the forecasted WL using JASON-2 data is similar to those upto 5-days forecast generated in the existing system. This is a new approach in FFEW in Bangladesh where boundary estimation becomes possible using JASON-2 observed WE data of the Transboundary rivers. There is scope of further development of this system along with increase of lead time. Reference: www.ffwc.gov.bd

Application of satellite-based rainfall and medium range meteorological forecast in real-time flood forecasting in the Mahanadi River basin

NASA Astrophysics Data System (ADS)

Nanda, Trushnamayee; Beria, Harsh; Sahoo, Bhabagrahi; Chatterjee, Chandranath

2016-04-01

Increasing frequency of hydrologic extremes in a warming climate call for the development of reliable flood forecasting systems. The unavailability of meteorological parameters in real-time, especially in the developing parts of the world, makes it a challenging task to accurately predict flood, even at short lead times. The satellite-based Tropical Rainfall Measuring Mission (TRMM) provides an alternative to the real-time precipitation data scarcity. Moreover, rainfall forecasts by the numerical weather prediction models such as the medium term forecasts issued by the European Center for Medium range Weather Forecasts (ECMWF) are promising for multistep-ahead flow forecasts. We systematically evaluate these rainfall products over a large catchment in Eastern India (Mahanadi River basin). We found spatially coherent trends, with both the real-time TRMM rainfall and ECMWF rainfall forecast products overestimating low rainfall events and underestimating high rainfall events. However, no significant bias was found for the medium rainfall events. Another key finding was that these rainfall products captured the phase of the storms pretty well, but suffered from consistent under-prediction. The utility of the real-time TRMM and ECMWF forecast products are evaluated by rainfall-runoff modeling using different artificial neural network (ANN)-based models up to 3-days ahead. Keywords: TRMM; ECMWF; forecast; ANN; rainfall-runoff modeling

Air Quality Forecasts Using the NASA GEOS Model: A Unified Tool from Local to Global Scales

NASA Technical Reports Server (NTRS)

Knowland, E. Emma; Keller, Christoph; Nielsen, J. Eric; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Cook, Melanie; Liu, Junhua;

Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling

NASA Astrophysics Data System (ADS)

Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè

2017-04-01

Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.

Performance and quality assessment of the recent updated CMEMS global ocean monitoring and forecasting real-time system

NASA Astrophysics Data System (ADS)

Le Galloudec, Olivier; Lellouche, Jean-Michel; Greiner, Eric; Garric, Gilles; Régnier, Charly; Drévillon, Marie; Drillet, Yann

2017-04-01

Since May 2015, Mercator Ocean opened the Copernicus Marine Environment and Monitoring Service (CMEMS) and is in charge of the global eddy resolving ocean analyses and forecast. In this context, Mercator Ocean currently delivers in real-time daily services (weekly analyses and daily forecast) with a global 1/12° high resolution system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. Along track altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity. R&D activities have been conducted at Mercator Ocean these last years to improve the real-time 1/12° global system for recent updated CMEMS version in 2016. The ocean/sea-ice model and the assimilation scheme benefited of the following improvements: large-scale and objective correction of atmospheric quantities with satellite data, new Mean Dynamic Topography taking into account the last version of GOCE geoid, new adaptive tuning of some observational errors, new Quality Control on the assimilated temperature and salinity vertical profiles based on dynamic height criteria, assimilation of satellite sea-ice concentration, new freshwater runoff from ice sheets melting, … This presentation will show the impact of some updates separately, with a particular focus on adaptive tuning experiments of satellite Sea Level Anomaly (SLA) and Sea Surface Temperature (SST) observations errors. For the SLA, the a priori prescribed observation error is globally greatly reduced. The median value of the error changed from 5cm to 2.5cm in a few assimilation cycles. For the SST, we chose to maintain the median value of the error to 0.4°C. The spatial distribution of the SST error follows the model physics and atmospheric variability. Either for SLA or SST, we improve the performances of the system using this adaptive tuning. The overall behavior of the system integrating all updates reporting on the products quality improvements will be also discussed, highlighting the level of performance and the reliability of the new system.

The Fixed-bias Langmuir Probe on the Communication-navigation Outage Forecast System Satellite: Calibration and Validation

NASA Technical Reports Server (NTRS)

Klenzing, Jeffrey H.; Rowland, Douglas E.

2012-01-01

A fixed-bias spherical Langmuir probe is included as part of the Vector Electric Field Instrument (VEFI) suite on the Communication Navigation Outage Forecast System (CNOFS) satellite.CNOFS gathers data in the equatorial ionosphere between 400 and 860 km, where the primary constituent ions are H+ and O+. The ion current collected by the probe surface per unit plasmadensity is found to be a strong function of ion composition. The calibration of the collected current to an absolute density is discussed, and the performance of the spherical probe is compared to other in situ instruments on board the CNOFS satellite. The application of the calibration is discussed with respect to future xed-bias probes; in particular, it is demonstrated that some density fluctuations will be suppressed in the collected current if the plasma composition rapidly changes along with density. This is illustrated in the observation of plasma density enhancements on CNOFS.

AROME-Arctic: New operational NWP model for the Arctic region

NASA Astrophysics Data System (ADS)

Süld, Jakob; Dale, Knut S.; Myrland, Espen; Batrak, Yurii; Homleid, Mariken; Valkonen, Teresa; Seierstad, Ivar A.; Randriamampianina, Roger

2016-04-01

In the frame of the EU-funded project ACCESS (Arctic Climate Change, Economy and Society), MET Norway aimed 1) to describe the present monitoring and forecasting capabilities in the Arctic; and 2) to identify the key factors limiting the forecasting capabilities and to give recommendations on key areas to improve the forecasting capabilities in the Arctic. We have observed that the NWP forecast quality is lower in the Arctic than in the regions further south. Earlier research indicated that one of the factors behind this is the composition of the observing system in the Arctic, in particular the scarceness of conventional observations. To further assess possible strategies for alleviating the situation and propose scenarios for a future Arctic observing system, we have performed a set of experiments to gain a more detailed insight in the contribution of the components of the present observing system in a regional state-of-the-art non-hydrostatic NWP model using the AROME physics (Seity et al, 2011) at 2.5 km horizontal resolution - AROME-Arctic. Our observing system experiment studies showed that conventional observations (Synop, Buoys) can play an important role in correcting the surface state of the model, but prove that the present upper-air conventional (Radiosondes, Aircraft) observations in the area are too scarce to have a significant effect on forecasts. We demonstrate that satellite sounding data play an important role in improving forecast quality. This is the case with satellite temperature sounding data (AMSU-A, IASI), as well as with the satellite moisture sounding data (AMSU-B/MHS, IASI). With these sets of observations, the AROME-Arctic clearly performs better in forecasting extreme events, like for example polar lows. For more details see presentation by Randriamampianina et al. in this session. The encouraging performance of AROME-Arctic lead us to implement it with more observations and improved settings into daily runs with the objective to substitute our actual operational Arctic mesoscale HIRLAM (High Resolution Limited Area Model) NWP model. This presentation will discuss in detail the operational implementation of the AROME-Arctic model together with post-processing methods. Aimed services in the Arctic region covered by the model, such as online weather forecasting (yr.no) and tracking of polar lows (barentswatch.no), is also included.

Study to forecast and determine characteristics of world satellite communications market

NASA Technical Reports Server (NTRS)

Filep, R. T.; Schnapf, A.; Fordyce, S. W.

1983-01-01

The world commercial communications satellite market during the spring and summer of 1983 was examined and characteristics and forecasts of the market extending to the year 2000 were developed. Past, present and planned satellites were documented in relation to frequencies, procurement and launch dates, costs, transponders, and prime contractor. Characteristics of the market are outlined for the periods 1965 - 1985, 1986 - 1989, and 1990 - 2000. Market share forecasts, discussions of potential competitors in various world markets, and profiles of major communication satellite manufacturing and user countries are documented.

Characteristics of Operational Space Weather Forecasting: Observations and Models

NASA Astrophysics Data System (ADS)

Berger, Thomas; Viereck, Rodney; Singer, Howard; Onsager, Terry; Biesecker, Doug; Rutledge, Robert; Hill, Steven; Akmaev, Rashid; Milward, George; Fuller-Rowell, Tim

2015-04-01

In contrast to research observations, models and ground support systems, operational systems are characterized by real-time data streams and run schedules, with redundant backup systems for most elements of the system. We review the characteristics of operational space weather forecasting, concentrating on the key aspects of ground- and space-based observations that feed models of the coupled Sun-Earth system at the NOAA/Space Weather Prediction Center (SWPC). Building on the infrastructure of the National Weather Service, SWPC is working toward a fully operational system based on the GOES weather satellite system (constant real-time operation with back-up satellites), the newly launched DSCOVR satellite at L1 (constant real-time data network with AFSCN backup), and operational models of the heliosphere, magnetosphere, and ionosphere/thermosphere/mesophere systems run on the Weather and Climate Operational Super-computing System (WCOSS), one of the worlds largest and fastest operational computer systems that will be upgraded to a dual 2.5 Pflop system in 2016. We review plans for further operational space weather observing platforms being developed in the context of the Space Weather Operations Research and Mitigation (SWORM) task force in the Office of Science and Technology Policy (OSTP) at the White House. We also review the current operational model developments at SWPC, concentrating on the differences between the research codes and the modified real-time versions that must run with zero fault tolerance on the WCOSS systems. Understanding the characteristics and needs of the operational forecasting community is key to producing research into the coupled Sun-Earth system with maximal societal benefit.

An oceanographic survey for oil spill monitoring and model forecasting validation using remote sensing and in situ data in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Pisano, A.; De Dominicis, M.; Biamino, W.; Bignami, F.; Gherardi, S.; Colao, F.; Coppini, G.; Marullo, S.; Sprovieri, M.; Trivero, P.; Zambianchi, E.; Santoleri, R.

2016-11-01

A research cruise was organized on board the Italian National Research Council (CNR) R/V Urania to test the oil spill monitoring system developed during the PRogetto pilota Inquinamento Marino da Idrocarburi project (PRIMI, pilot project for marine oil pollution). For the first time, this system integrated in a modular way satellite oil spill detection (Observation Module) and oil spill displacement forecasting (Forecast Module) after detection. The Observation Module was based on both Synthetic Aperture RADAR (SAR) and optical satellite detection, namely SAR and Optical Modules, while the Forecast Module on Lagrangian numerical circulation models. The cruise (Aug. 6-Sep. 7, 2009) took place in the Mediterranean Sea, around Sicily, an area affected by heavy oil tanker traffic with frequent occurrence of oil spills resulting from illegal tank washing. The cruise plan was organized in order to have the ship within the SAR image frames selected for the cruise, at acquisition time. In this way, the ship could rapidly reach oil slicks detected in the images by the SAR Module, and/or eventually by the Optical Module, in order to carry out visual and instrumental inspection of the slicks. During the cruise, several oil spills were detected by the two Observation Modules and verified in situ, with the essential aid of the Forecasting Module which provided the slick position by the time the ship reached the area after the alert given by the SAR and/or optical imagery. Results confirm the good capability of oil spill SAR detection and indicate that also optical sensors are able to detect oil spills, ranging from thin films to slicks containing heavily polluted water. Also, results confirm the useful potential of oil spill forecasting models, but, on the other hand, that further work combining satellite, model and in situ data is necessary to refine the PRIMI system.

Fire and Smoke Monitoring at NOAA' Satellite Service; Applications to Smoke Forecasting

NASA Astrophysics Data System (ADS)

Stephens, G.; Ruminski, M.

2005-12-01

The Hazard Mapping System (HMS), developed and run operationally by NOAA's Satellite Services Division (SSD), is a multiplatform remote sensing approach to detecting fires and smoke over the US and adjacent areas of Canada and Mexico. The system utilizes sensors on 7 different NOAA and NASA satellites. Automated detection algorithms are employed for each of the satellites for the fire detects while smoke is delineated by an image analyst. Analyses are quality control by an analyst who inspects all available imagery and automated fire detects, deleting suspected false detects and adding fires that the automated routines miss. Graphical, text, and GIS compatible analyses are posted to a web site as soon as updates are performed, and a final product for a given day is posted early the following morning. All products are archived at NOAA's National Geophysical Data Center. Areal extent of detectable smoke is outlined using animated visible imagery, for input to a dispersion and transport model, the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), developed by NOAA's Air Resources Laboratory (ARL). Resulting smoke forecasts will soon be used as input to NOAA's Air Quality forecasts. The GOES Aerosol and Smoke Product (GASP) is an experimental GOES imagery based aerosol optical depth (AOD) product developed by the NESDIS Office of Research and Applications, being implemented for evaluation by the NESDIS Satellite Analysis Branch for use in smoke and volcanic ash monitoring. Currently, research is underway in NESDIS' Office of Research and Applications to objectivize smoke delineation using GASP and MODIS AOD retrievals. NOAA's Operational Significant Event Imagery (OSEI) program processes satellite imagery of environmentally significant events, including fire, smoke and volcanic ash, visible in operational satellite data. This imagery is often referred to by fire managers and air quality agencies. Future plans include the integration of high resolution global data from the European Space Agency's MetOp satellite and global geostationary satellites.

The Mediterranean Forecasting System: recent developments

NASA Astrophysics Data System (ADS)

Tonani, Marina; Oddo, Paolo; Korres, Gerasimos; Clementi, Emanuela; Dobricic, Srdjan; Drudi, Massimiliano; Pistoia, Jenny; Guarnieri, Antonio; Romaniello, Vito; Girardi, Giacomo; Grandi, Alessandro; Bonaduce, Antonio; Pinardi, Nadia

2014-05-01

Recent developments of the Mediterranean Monitoring and Forecasting Centre of the EU-Copernicus marine service, the Mediterranean Forecasting System (MFS), are presented. MFS provides forecast, analysis and reanalysis for the physical and biogeochemical parameters of the Mediterranean Sea. The different components of the system are continuously updated in order to provide to the users the best available product. This work is focus on the physical component of the system. The physical core of MFS is composed by an ocean general circulation model (NEMO) coupled with a spectral wave model (Wave Watch-III). The NEMO model provides to WW-III surface currents and SST fields, while WW-III returns back to NEMO the neutral component of the surface drag coefficient. Satellite Sea Level Anomaly observations and in-situ T & S vertical profiles are assimilated into this system using a variational assimilation scheme based on 3DVAR (Dobricic, 2008) . Sensitive experiments have been performed in order to assess the impact of the assimilation of the latest available SLA missions, Altika and Cryosat together with the long term available mission of Jason2. The results show a significant improvement of the MFS skill due to the multi-mission along track assimilation. The primitive equations module has been recently upgraded with the introduction of the atmospheric pressure term and a new, explicit, numerical scheme has been adopted to solve the barotropic component of the equations of motion. The SLA satellite observations for data assimilation have been consequently modified in order to account for the new atmospheric pressure term introduced in the equations. This new system has been evaluated using tide gauge coastal buoys and the satellite along track data. The quality of the SSH has improved significantly while a minor impact has been observed on the other state variables (temperature, salinity and currents). Experiments with a higher resolution NWP (numerical weather prediction) forcing provided by the COSMO-MED system (provided by the Italian Meteorological Office), have been performed and a pre-operational 3-day forecast production system has been developed. The comparison between this system and the official one forced by the ECMWF NWP data will be discussed.

Modeled Forecasts of Dengue Fever in San Juan, Puerto Rico Using NASA Satellite Enhanced Weather Forecasts

NASA Astrophysics Data System (ADS)

Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.

2015-12-01

Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.

Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

NASA Technical Reports Server (NTRS)

Zhu, Jiang; Stevens, E.; Zavodsky, B. T.; Zhang, X.; Heinrichs, T.; Broderson, D.

2014-01-01

Data assimilation has been demonstrated very useful in improving both global and regional numerical weather prediction. Alaska has very coarser surface observation sites. On the other hand, it gets much more satellite overpass than lower 48 states. How to utilize satellite data to improve numerical prediction is one of hot topics among weather forecast community in Alaska. The Geographic Information Network of Alaska (GINA) at University of Alaska is conducting study on satellite data assimilation for WRF model. AIRS/CRIS sounder profile data are used to assimilate the initial condition for the customized regional WRF model (GINA-WRF model). Normalized standard deviation, RMSE, and correlation statistic analysis methods are applied to analyze one case of 48 hours forecasts and one month of 24-hour forecasts in order to evaluate the improvement of regional numerical model from Data assimilation. The final goal of the research is to provide improved real-time short-time forecast for Alaska regions.

Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites

PubMed Central

Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru

2016-01-01

This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons. PMID:27092508

Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites.

PubMed

Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru

2016-04-15

This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons.

Transitioning NPOESS Data to Weather Offices: The SPoRT Paradigm with EOS Data

NASA Technical Reports Server (NTRS)

Jedlovec, Gary

2009-01-01

Real-time satellite information provides one of many data sources used by NWS weather forecast offices (WFOs) to diagnose current weather conditions and to assist in short-term forecast preparation. While GOES satellite data provides relatively coarse spatial resolution coverage of the continental U.S. on a 10-15 minute repeat cycle, polar orbiting imagery has the potential to provide snapshots of weather conditions at high-resolution in many spectral channels. Additionally, polar orbiting sounding data can provide additional information on the thermodynamic structure of the atmosphere in data sparse regions of at asynoptic observation times. The NASA Short-term Prediction Research and Transition (SPoRT) project has demonstrated the utility of polar orbiting MODIS and AIRS data on the Terra and Aqua satellites to improve weather diagnostics and short-term forecasting on the regional and local scales. SPoRT scientists work directly forecasters at selected WFOS in the Southern Region (SR) to help them ingest these unique data streams into their AWIPS system, understand how to use the data (through on-site and distance learn techniques), and demonstrate the utility of these products to address significant forecast problems. This process also prepares forecasters for the use of similar observational capabilities from NPOESS operational sensors. NPOESS environmental data records (EDRs) from the Visible 1 Infrared Imager I Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrlS) and Advanced Technology Microwave Sounder (ATMS) instruments and additional value-added products produced by NESDIS will be available in near real-time and made available to WFOs to extend their use of NASA EOS data into the NPOESS era. These new data streams will be integrated into the NWs's new AWIPS II decision support tools. The AWIPS I1 system to be unveiled in WFOs in 2009 will be a JAVA-based decision support system which preserves the functionality of the existing systems and offers unique development opportunities for new data sources and applications in the Service Orientated Architecture ISOA) environment. This paper will highlight some of the SPoRT activities leading to the integration of VllRS and CrIS/ATMS data into the display capabilities of these new systems to support short-term forecasting problems at WFOs.

Monitoring Areal Snow Cover Using NASA Satellite Imagery

NASA Technical Reports Server (NTRS)

Harshburger, Brian J.; Blandford, Troy; Moore, Brandon

2011-01-01

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data can be obtained from a digital elevation model (DEM) for the area of interest.

Summary of the Operational Applications of Satellite Snowcover Observations Working Session, 20 August 1975

NASA Technical Reports Server (NTRS)

Salomonson, V. V.; Rango, A.

1975-01-01

Various techniques for reducing the satellite data to a form usable by the operational agencies were covered in mini-presentations by the operational satellite snow interpretive personnel. Similar discussions were made by operational agency stream flow forecasters on how satellite-derived snow data could be incorporated into runoff forecasting methods.

The GISS sounding temperature impact test

NASA Technical Reports Server (NTRS)

Halem, M.; Ghil, M.; Atlas, R.; Susskind, J.; Quirk, W. J.

1978-01-01

The impact of DST 5 and DST 6 satellite sounding data on mid-range forecasting was studied. The GISS temperature sounding technique, the GISS time-continuous four-dimensional assimilation procedure based on optimal statistical analysis, the GISS forecast model, and the verification techniques developed, including impact on local precipitation forecasts are described. It is found that the impact of sounding data was substantial and beneficial for the winter test period, Jan. 29 - Feb. 21. 1976. Forecasts started from initial state obtained with the aid of satellite data showed a mean improvement of about 4 points in the 48 and 772 hours Sub 1 scores as verified over North America and Europe. This corresponds to an 8 to 12 hour forecast improvement in the forecast range at 48 hours. An automated local precipitation forecast model applied to 128 cities in the United States showed on an average 15% improvement when satellite data was used for numerical forecasts. The improvement was 75% in the midwest.

Space-based Scintillation Nowcasting with the Communications/Navigation Outage Forecast System

NASA Astrophysics Data System (ADS)

Groves, K.; Starks, M.; Beach, T.; Basu, S.

2008-12-01

The Air Force Research Laboratory's Communication/Navigation Outage Forecast System (C/NOFS) fuses ground- and space-based data in a near real-time physics-based model aimed at forecasting and nowcasting equatorial scintillations and their impacts on satellite communications and navigation. A key component of the system is the C/NOFS satellite that was launched into a low-inclination (13°) elliptical orbit (400 km x 850 km) in April 2008. The satellite contains six sensors to measure space environment parameters including electron density and temperature, ion density and drift, electric and magnetic fields and neutral wind, as well as a tri-band radio beacon transmitting at 150 MHz, 400 MHz and 1067 MHz. Scintillation nowcasts are derived from measuring the one-dimensional in situ electron density fluctuations and subsequently modeling the propagation environment for satellite-to-ground radio links. The modeling process requires a number of simplifying assumptions regarding the three-dimensional structure of the ionosphere and the results are readily validated by comparisons with ground-based measurements of the satellite's tri-band beacon signals. In mid-September 2008 a campaign to perform detailed analyses of space-based scintillation nowcasts with numerous ground observations was conducted in the vicinity of Kwajalein Atoll, Marshall Islands. To maximize the collection of ground-truth data, the ALTAIR radar was employed to obtain detailed information on the spatial structure of the ionosphere during the campaign and to aid the improvement of space-based nowcasting algorithms. A comparison of these results will be presented; it appears that detailed information on the electron density structure is a limiting factor in modeling the scintillation environment from in situ observations.

JPSS Preparations at the Satellite Proving Ground for Marine, Precipitation, and Satellite Analysis

NASA Technical Reports Server (NTRS)

Folmer, Michael J.; Berndt, E.; Clark, J.; Orrison, A.; Kibler, J.; Sienkiewicz, J.; Nelson, J.; Goldberg, M.; Sjoberg, W.

2016-01-01

The ocean prediction center at the national hurricane center's tropical analysis and forecast Branch, the Weather Prediction center and the Satellite analysis branch of NESDIS make up the Satellite Proving Ground for Marine, Precipitation and Satellite Analysis. These centers had early exposure to JPSS products using the S-NPP Satellite that was launched in 2011. Forecasters continue to evaluate new products in anticipation for the launch of JPSS-1 sometime in 2017.

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco

2012-01-01

Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.

How Satellites Have Contributed to Building a Weather Ready Nation

NASA Astrophysics Data System (ADS)

Lapenta, W.

2017-12-01

NOAA's primary mission since its inception has been to reduce the loss of life and property, as well as disruptions from, high impact weather and water-related events. In recent years, significant societal losses resulting even from well forecast extreme events have shifted attention from the forecast alone toward ensuring societal response is equal to the risks that exist for communities, businesses and the public. The responses relate to decisions ranging from coastal communities planning years in advance to mitigate impacts from rising sea level, to immediate lifesaving decisions such as a family seeking adequate shelter during a tornado warning. NOAA is committed to building a "Weather-Ready Nation" where communities are prepared for and respond appropriately to these events. The Weather-Ready Nation (WRN) strategic priority is building community resilience in the face of increasing vulnerability to extreme weather, water, climate and environmental threats. To build a Weather-Ready Nation, NOAA is enhancing Impact-Based Decision Support Services (IDSS), transitioning science and technology advances into forecast operations, applying social science research to improve the communication and usefulness of information, and expanding its dissemination efforts to achieve far-reaching readiness, responsiveness and resilience. These four components of Weather-Ready Nation are helping ensure NOAA data, products and services are fully utilized to minimize societal impacts from extreme events. Satellite data and satellite products have been important elements of the national Weather Service (NWS) operations for more than 40 years. When one examines the uses of satellite data specific to the internal forecast and warning operations of NWS, two main applications are evident. The first is the use of satellite data in numerical weather prediction models; the second is the use of satellite imagery and derived products for mesoscale and short-range weather warning and prediction. The purpose of this paper is to highlight the value of the satellite component of the global observing system to NWS operational weather forecasting and emphasize how these data form a critical component of the NWS ability to protect life and property and ensure economic well-being.

Forecasting sea fog on the coast of southern China

NASA Astrophysics Data System (ADS)

Huang, H.; Huang, B.; Liu, C.; Tu, J.; Wen, G.; Mao, W.

2016-12-01

Forecast sea fog is still full of challenges. We have performed the numerical forecasting of sea fog on the coast of southern China by using the operational meso-scale regional model GRAPES (Global/Regional assimilation and prediction system). The GRAPES model horizontal resolution was 3km and with 66 vertical levels. A total of 72 hours forecasting of sea fog was conducted with hourly outputs over the sea fog event. The results show that the model system can predict reasonable characteristics of typical sea fog events on the coast of southern China. The scope of sea fog coincides with the observations of meteorological stations, the observations of the Marine Meteorological Science Experiment Base (MMSEB) at Bohe, Maoming and satellite products of sea fog. The goal of this study is to establish an operational numerical forecasting model system of sea fog on the coast of southern China.

77 FR 69436 - JPSS Polar Satellite-Gap Mitigation-Request for Public Comment

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-19

... positive steps to mitigate the negative impacts to NOAA's numerical weather forecasts that could be...-satellite data, weather modeling, and data assimilation improvements. NOAA is convening teams of internal... of NOAA's numerical weather forecasts should we experience a loss of polar satellite environmental...

Enhancing Famine Early Warning Systems with Improved Forecasts, Satellite Observations and Hydrologic Simulations

NASA Astrophysics Data System (ADS)

Funk, C. C.; Verdin, J.; Thiaw, W. M.; Hoell, A.; Korecha, D.; McNally, A.; Shukla, S.; Arsenault, K. R.; Magadzire, T.; Novella, N.; Peters-Lidard, C. D.; Robjohn, M.; Pomposi, C.; Galu, G.; Rowland, J.; Budde, M. E.; Landsfeld, M. F.; Harrison, L.; Davenport, F.; Husak, G. J.; Endalkachew, E.

2017-12-01

Drought early warning science, in support of famine prevention, is a rapidly advancing field that is helping to save lives and livelihoods. In 2015-2017, a series of extreme droughts afflicted Ethiopia, Southern Africa, Eastern Africa in OND and Eastern Africa in MAM, pushing more than 50 million people into severe food insecurity. Improved drought forecasts and monitoring tools, however, helped motivate and target large and effective humanitarian responses. Here we describe new science being developed by a long-established early warning system - the USAID Famine Early Warning Systems Network (FEWS NET). FEWS NET is a leading provider of early warning and analysis on food insecurity. FEWS NET research is advancing rapidly on several fronts, providing better climate forecasts and more effective drought monitoring tools that are being used to support enhanced famine early warning. We explore the philosophy and science underlying these successes, suggesting that a modal view of climate change can support enhanced seasonal prediction. Under this modal perspective, warming of the tropical oceans may interact with natural modes of variability, like the El Niño-Southern Oscillation, to enhance Indo-Pacific sea surface temperature gradients during both El Niño and La Niña-like climate states. Using empirical data and climate change simulations, we suggest that a sequence of droughts may commence in northern Ethiopia and Southern Africa with the advent of a moderate-to-strong El Niño, and then continue with La Niña/West Pacific related droughts in equatorial eastern East Africa. Scientifically, we show that a new hybrid statistical-dynamic precipitation forecast system, the FEWS NET Integrated Forecast System (FIFS), based on reformulations of the Global Ensemble Forecast System weather forecasts and National Multi-Model Ensemble (NMME) seasonal climate predictions, can effectively anticipate recent East and Southern African drought events. Using cross-validation, we evaluate FIFS' skill and compare it to the NMME and the International Research Institute forecasts. Our study concludes with an overview of the satellite observations provided by FEWS NET partners at NOAA, NASA, USGS, and UC Santa Barbara, and the assimilation of these products within the FEWS NET Land Data Assimilation System (FLDAS).

Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

NASA Technical Reports Server (NTRS)

Zhu, Jiang; Stevens, E.; Zhang, X.; Zavodsky, B. T.; Heinrichs, T.; Broderson, D.

2014-01-01

A case study and monthly statistical analysis using sounder data assimilation to improve the Alaska regional weather forecast model are presented. Weather forecast in Alaska faces challenges as well as opportunities. Alaska has a large land with multiple types of topography and coastal area. Weather forecast models must be finely tuned in order to accurately predict weather in Alaska. Being in the high-latitudes provides Alaska greater coverage of polar orbiting satellites for integration into forecasting models than the lower 48. Forecasting marine low stratus clouds is critical to the Alaska aviation and oil industry and is the current focus of the case study. NASA AIRS/CrIS sounder profiles data are used to do data assimilation for the Alaska regional weather forecast model to improve Arctic marine stratus clouds forecast. Choosing physical options for the WRF model is discussed. Preprocess of AIRS/CrIS sounder data for data assimilation is described. Local observation data, satellite data, and global data assimilation data are used to verify and/or evaluate the forecast results by the MET tools Model Evaluation Tools (MET).

Rapid weather information dissemination in Florida

NASA Technical Reports Server (NTRS)

Martsolf, J. D.; Heinemann, P. H.; Gerber, J. F.; Crosby, F. L.; Smith, D. L.

1984-01-01

The development of the Florida Agricultural Services and Technology (FAST) plan to provide ports for users to call for weather information is described. FAST is based on the Satellite Frost Forecast System, which makes a broad base of weather data available to its users. The methods used for acquisition and dissemination of data from various networks under the FAST plan are examined. The system provides color coded IR or thermal maps, precipitation maps, and textural forecast information. A diagram of the system is provided.

Achieving Superior Tropical Cyclone Intensity Forecasts by Improving the Assimilation of High-Resolution Satellite Data into Mesoscale Prediction Models

DTIC Science & Technology

2013-09-30

using polar orbit microwave and infrared sounder measurements from the Global Telecommunication System (GTS). The SDAT system was developed as a...WRF/GSI initial conditions and WRF boundary conditions. • WRF system to do short-range forecasts (6 hours) to provide the background fields for GSI...UCAR is related to a NASA GNSS proposal: “Improving Tropical Prediction and Analysis using COSMIC Radio Occultation Observations and an Ensemble Data

Use and Assessment of Multi-Spectral Satellite Imagery in NWS Operational Forecasting Environments

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Fuell, Kevin; Stano, Geoffrey; McGrath, Kevin; Schultz, Lori; LeRoy, Anita

2015-01-01

NOAA's Satellite Proving Grounds have established partnerships between product developers and NWS WFOs for the evaluation of new capabilities from the GOES-R and JPSS satellite systems. SPoRT has partnered with various WFOs to evaluate multispectral (RGB) products from MODIS, VIIRS and Himawari/AHI to prepare for GOES-R/ABI. Assisted through partnerships with GINA, UW/CIMSS, NOAA, and NASA Direct Broadcast capabilities.

GOES-S Mission Science Briefing

NASA Image and Video Library

2018-02-27

In the Kennedy Space Center's Press Site auditorium, Jim Roberts, a scientist with the Earth System Research Laboratory's Office of Atmospheric Research for NOAA, left, and Kristin Calhoun, a research scientist with NOAA's National Severe Storms Laboratory, speak to members of the media at a mission briefing on National Oceanic and Atmospheric Administration's, or NOAA's, Geostationary Operational Environmental Satellite, or GOES-S. The spacecraft is the second satellite in a series of next-generation NOAA weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting. GOES-S is slated to lift off at 5:02 p.m. EST on March 1, 2018 aboard a United Launch Alliance Atlas V rocket.

GOES-S NASA Social

NASA Image and Video Library

2018-02-28

A.J. Sandora, Lockheed Martin's GOES-R Series Mechanical Operations Assembly, Test and Launch Operations (ATLO) manager, speaks to members of social media in the Kennedy Space Center’s Press Site auditorium. The briefing focused on the National Oceanic and Atmospheric Administration's, or NOAA's, Geostationary Operational Environmental Satellite, or GOES-S. Built by Lockheed Martin Space Systems of Littleton, Colorado, the spacecraft is the second satellite in a series of next-generation NOAA weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting. GOES-S is slated to lift off at 5:02 p.m. EST on March 1, 2018 aboard a United Launch Alliance Atlas V rocket.

NPP Satellite Launch

NASA Image and Video Library

2011-10-28

The Satellite Operations Facility of the National Oceanic and Atmospheric Administration (NOAA) is seen here minutes before the launch of the National Polar-orbiting Operational Environmental Satellite System Preparatory Project (NPP) on Friday, Oct. 28, 2011 in Suitland, Md. NPP is a joint venture between NASA and NOAA, and is the nation's newest Earth-observing satellite, which will provide data on climate change science, allow for accurate weather forecasts and advance warning for severe weather. NPP was launched from Vandenberg Air Force Base in California. Photo Credit: (NASA/Carla Cioffi)

Impact of data assimilation on ocean current forecasts in the Angola Basin

NASA Astrophysics Data System (ADS)

Phillipson, Luke; Toumi, Ralf

2017-06-01

The ocean current predictability in the data limited Angola Basin was investigated using the Regional Ocean Modelling System (ROMS) with four-dimensional variational data assimilation. Six experiments were undertaken comprising a baseline case of the assimilation of salinity/temperature profiles and satellite sea surface temperature, with the subsequent addition of altimetry, OSCAR (satellite-derived sea surface currents), drifters, altimetry and drifters combined, and OSCAR and drifters combined. The addition of drifters significantly improves Lagrangian predictability in comparison to the baseline case as well as the addition of either altimetry or OSCAR. OSCAR assimilation only improves Lagrangian predictability as much as altimetry assimilation. On average the assimilation of either altimetry or OSCAR with drifter velocities does not significantly improve Lagrangian predictability compared to the drifter assimilation alone, even degrading predictability in some cases. When the forecast current speed is large, it is more likely that the combination improves trajectory forecasts. Conversely, when the currents are weaker, it is more likely that the combination degrades the trajectory forecast.

A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen

The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solarmore » power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few minutes and forecasts that currently go out to about 15 min. This project has facilitated research in improving the hardware and software so that the new high definition cameras deployed at multiple nearby locations allow discernment of the clouds at varying levels and advection according to the winds observed at those levels. Improvements over “smart persistence” are about 29% for even these very short forecasts. StatCast is based on pyranometer data measured at the site as well as concurrent meteorological observations and forecasts. StatCast is based on regime-dependent artificial intelligence forecasting techniques and has been shown to improve on “smart persistence” forecasts by 15-50%. A second category of short-range forecasting systems employ satellite imagery and use that information to discern clouds and their motion, allowing them to project the clouds, and the resulting blockage of irradiance, in time. CIRACast (the system produced by the Cooperative Institute for Atmospheric Research [CIRA] at Colorado State University) was already one of the more advanced cloud motion systems, which is the reason that team was brought to this project. During the project timeframe, the CIRA team was able to advance cloud shadowing, parallax removal, and implementation of better advecting winds at different altitudes. CIRACast shows generally a 25-40% improvement over Smart Persistence between sunrise and approximately 1600 UTC (Coordinated Universal Time) . A second satellite-based system, MADCast (Multi-sensor Advective Diffusive foreCast system), assimilates data from multiple satellite imagers and profilers to assimilate a fully three-dimensional picture of the cloud into the dynamic core of WRF. During 2015, MADCast (provided at least 70% improvement over Smart Persistence, with most of that skill being derived during partly cloudy conditions. That allows advection of the clouds via the Weather Research and Forecasting (WRF) model dynamics directly. After WRF-Solar™ showed initial success, it was also deployed in nowcasting mode with coarser runs out to 6 hours made hourly. It provided improvements on the order of 50-60% over Smart Persistence for forecasts up to 1600 UTC. The advantages of WRF-Solar-Nowcasting and MADCast were then blended to develop the new MAD-WRF model that incorporates the most important features of each of those models, both assimilating satellite cloud fields and using WRF-So far physics to develop and dissipate clouds. MAE improvements for MAD-WRF for forecasts from 3-6 hours are improved over WRF-Solar-Now by 20%. While all the Nowcasting system components by themselves provide improvement over Smart Persistence, the largest benefit is derived when they are smartly blended together by the Nowcasting Integrator to produce an integrated forecast. The development of WRF-Solar™ under this project has provided the first numerical weather prediction (NWP) model specifically designed to meet the needs of irradiance forecasting. The first augmentation improved the solar tracking algorithm to account for deviations associated with the eccentricity of the Earth’s orbit and the obliquity of the Earth. Second, WRF-Solar™ added the direct normal irradiance (DNI) and diffuse (DIF) components from the radiation parameterization to the model output. Third, efficient parameterizations were implemented to either interpolate the irradiance in between calls to the expensive radiative transfer parameterization, or to use a fast radiative transfer code that avoids computing three-dimensional heating rates but provides the surface irradiance. Fourth, a new parameterization was developed to improve the representation of absorption and scattering of radiation by aerosols (aerosol direct effect). A fifth advance is that the aerosols now interact with the cloud microphysics, altering the cloud evolution and radiative properties, an effect that has been traditionally only implemented in atmospheric computationally costly chemistry models. A sixth development accounts for the feedbacks that sub-grid scale clouds produce in shortwave irradiance as implemented in a shallow cumulus parameterization Finally, WRF-Solar™ also allows assimilation of infrared irradiances from satellites to determine the three dimensional cloud field, allowing for an improved initialization of the cloud field that increases the performance of short-range forecasts. We find that WRF-Solar™ can improve clear sky irradiance prediction by 15-80% over a standard version of WRF, depending on location and cloud conditions. In a formal comparison to the NAM baseline, WRF-Solar™ showed improvements in the Day-Ahead forecast of 22-42%. The SunCast™ system requires substantial software engineering to blend all of the new model components as well as existing publically available NWP model runs. To do this we use an expert system for the Nowcasting blender and the Dynamic Integrated foreCast (DICast®) system for the NWP models. These two systems are then blended, we use an empirical power conversion method to convert the irradiance predictions to power, then apply an analog ensemble (AnEn) approach to further tune the forecast as well as to estimate its uncertainty. The AnEn module decreased RMSE (root mean squared error) by 17% over the blended SunCast™ power forecasts and provided skill in the probabilistic forecast with a Brier Skill Score of 0.55. In addition, we have also developed a Gridded Atmospheric Forecast System (GRAFS) in parallel, leveraging cost share funds. An economic evaluation based on Production Cost Modeling in the Public Service Company of Colorado showed that the observed 50% improvement in forecast accuracy will save their customers $819,200 with the projected MW deployment for 2024. Using econometrics, NCAR has scaled this savings to a national level and shown that an annual expected savings for this 50% forecast error reduction ranges from $11M in 2015 to $43M expected in 2040 with increased solar deployment. This amounts to a $455M discounted savings over the 26 year period of analysis.« less

Utilization of Precipitation and Moisture Products Derived from Satellites to Support NOAA Operational Precipitation Forecasts

NASA Astrophysics Data System (ADS)

Ferraro, R.; Zhao, L.; Kuligowski, R. J.; Kusselson, S.; Ma, L.; Kidder, S. Q.; Forsythe, J. M.; Jones, A. S.; Ebert, E. E.; Valenti, E.

2012-12-01

NOAA/NESDIS operates a constellation of polar and geostationary orbiting satellites to support weather forecasts and to monitor the climate. Additionally, NOAA utilizes satellite assets from other U.S. agencies like NASA and the Department of Defense, as well as those from other nations with similar weather and climate responsibilities (i.e., EUMETSAT and JMA). Over the past two decades, through joint efforts between U.S. and international government researchers, academic partners, and private sector corporations, a series of "value added" products have been developed to better serve the needs of weather forecasters and to exploit the full potential of precipitation and moisture products generated from these satellites. In this presentation, we will focus on two of these products - Ensemble Tropical Rainfall Potential (eTRaP) and Blended Total Precipitable Water (bTPW) - and provide examples on how they contribute to hydrometeorological forecasts. In terms of passive microwave satellite products, TPW perhaps is most widely used to support real-time forecasting applications, as it accurately depicts tropospheric water vapor and its movement. In particular, it has proven to be extremely useful in determining the location, timing, and duration of "atmospheric rivers" which contribute to and sustain flooding events. A multi-sensor approach has been developed and implemented at NESDIS in which passive microwave estimates from multiple satellites and sensors are merged to create a seamless, bTPW product that is more efficient for forecasters to use. Additionally, this product is being enhanced for utilization for television weather forecasters. Examples will be shown to illustrate the roll of atmospheric rivers and contribution to flooding events, and how the bTPW product was used to improve the forecast of these events. Heavy rains associated with land falling tropical cyclones (TC) frequently trigger floods that cause millions of dollars of damage and tremendous loss of lives. To provide observations-based forecast guidance for TC heavy rain, the Tropical Rainfall Potential (TRaP), an extrapolation forecast generated by accumulating rainfall estimates from satellites with microwave sensors as the storm is translated along the forecast track, was originally developed to predict the maximum rainfall at landfall, as well as the spatial pattern of precipitation. More recently, an enhancement has been made to combine the TRaP forecasts from multiple sensors and various start times into an ensemble (eTRaP). The ensemble approach provides not only more accurate quantitative precipitation forecasts, including more skillful maximum rainfall amount and location, it also produces probabilistic forecasts of rainfall exceeding various thresholds that decision makers can use to make critical risk assessments. Examples of the utilization and performance of eTRaP will be given in the presentation.

Assimilation of the seabird and ship drift data in the north-eastern sea of Japan into an operational ocean nowcast/forecast system

PubMed Central

Miyazawa, Yasumasa; Guo, Xinyu; Varlamov, Sergey M.; Miyama, Toru; Yoda, Ken; Sato, Katsufumi; Kano, Toshiyuki; Sato, Keiji

2015-01-01

At the present time, ocean current is being operationally monitored mainly by combined use of numerical ocean nowcast/forecast models and satellite remote sensing data. Improvement in the accuracy of the ocean current nowcast/forecast requires additional measurements with higher spatial and temporal resolution as expected from the current observation network. Here we show feasibility of assimilating high-resolution seabird and ship drift data into an operational ocean forecast system. Data assimilation of geostrophic current contained in the observed drift leads to refinement in the gyre mode events of the Tsugaru warm current in the north-eastern sea of Japan represented by the model. Fitting the observed drift to the model depends on ability of the drift representing geostrophic current compared to that representing directly wind driven components. A preferable horizontal scale of 50 km indicated for the seabird drift data assimilation implies their capability of capturing eddies with smaller horizontal scale than the minimum scale of 100 km resolved by the satellite altimetry. The present study actually demonstrates that transdisciplinary approaches combining bio-/ship- logging and numerical modeling could be effective for enhancement in monitoring the ocean current. PMID:26633309

GOES-S NASA Social

NASA Image and Video Library

2018-02-28

Tim Walsh, GOES-R System Program director for the National Oceanic and Atmospheric Administration, or NOAA, speaks to members of social media in the Kennedy Space Center’s Press Site auditorium. The briefing focused on the Geostationary Operational Environmental Satellite, or GOES-S, the second spacecraft in a series of next-generation NOAA weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting. GOES-S is slated to lift off at 5:02 p.m. EST on March 1, 2018 aboard a United Launch Alliance Atlas V rocket.

Better hurricane forecasts

NASA Astrophysics Data System (ADS)

Friebele, Elaine

People living in coastal areas can rely on better hurricane predictions because forecasters now have nearly instant access to global wind data. Measurements of wind speed and direction over the world's oceans are available within 3 hours of measurement from the Japanese satellite ADEOS (Advanced Earth Observing Satellite).Wind parameters at 25-km resolution are being measured by NASA's scatterometer traveling on the Japanese satellite ADEOS (Advanced Earth Observing Satellite). “The high accuracy and spatial resolution of the data were quickly recognized by our forecasters, who have been starved for data over significant expanses of the world's oceans,” said Jim Hoke, director of NOAA's Marine Prediction Center.

Real-time new satellite product demonstration from microwave sensors and GOES-16 at NRL TC web

NASA Astrophysics Data System (ADS)

Cossuth, J.; Richardson, K.; Surratt, M. L.; Bankert, R.

2017-12-01

The Naval Research Laboratory (NRL) Tropical Cyclone (TC) satellite webpage (https://www.nrlmry.navy.mil/TC.html) provides demonstration analyses of storm imagery to benefit operational TC forecast centers around the world. With the availability of new spectral information provided by GOES-16 satellite data and recent research into improved visualization methods of microwave data, experimental imagery was operationally tested to visualize the structural changes of TCs during the 2017 hurricane season. This presentation provides an introduction into these innovative satellite analysis methods, NRL's next generation satellite analysis system (the Geolocated Information Processing System, GeoIPSTM), and demonstration the added value of additional spectral frequencies when monitoring storms in near-realtime.

Evaluating the performance of real-time streamflow forecasting using multi-satellite precipitation products in the Upper Zambezi, Africa

NASA Astrophysics Data System (ADS)

Demaria, E. M.; Valdes, J. B.; Wi, S.; Serrat-Capdevila, A.; Valdés-Pineda, R.; Durcik, M.

2016-12-01

In under-instrumented basins around the world, accurate and timely forecasts of river streamflows have the potential of assisting water and natural resource managers in their management decisions. The Upper Zambezi river basin is the largest basin in southern Africa and its water resources are critical to sustainable economic growth and poverty reduction in eight riparian countries. We present a real-time streamflow forecast for the basin using a multi-model-multi-satellite approach that allows accounting for model and input uncertainties. Three distributed hydrologic models with different levels of complexity: VIC, HYMOD_DS, and HBV_DS are setup at a daily time step and a 0.25 degree spatial resolution for the basin. The hydrologic models are calibrated against daily observed streamflows at the Katima-Mulilo station using a Genetic Algorithm. Three real-time satellite products: Climate Prediction Center's morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM-3B42RT) are bias-corrected with daily CHIRPS estimates. Uncertainty bounds for predicted flows are estimated with the Inverse Variance Weighting method. Because concentration times in the basin range from a few days to more than a week, we include the use of precipitation forecasts from the Global Forecasting System (GFS) to predict daily streamflows in the basin with a 10-days lead time. The skill of GFS-predicted streamflows is evaluated and the usefulness of the forecasts for short term water allocations is presented.

Advancing Data assimilation for Baltic Monitoring and Forecasting Center: implementation and evaluation of HBP-PDAF system

NASA Astrophysics Data System (ADS)

Korabel, Vasily; She, Jun; Huess, Vibeke; Woge Nielsen, Jacob; Murawsky, Jens; Nerger, Lars

2017-04-01

The potential of an efficient data assimilation (DA) scheme to improve model forecast skill was successfully demonstrated by many operational centres around the world. The Baltic-North Sea region is one of the most heavily monitored seas. Ferryboxes, buoys, ADCP moorings, shallow water Argo floats, and research vessels are providing more and more near-real time observations. Coastal altimetry has now providing increasing amount of high resolution sea level observations, which will be significantly expanded by the launch of SWOT satellite in next years. This will turn operational DA into a valuable tool for improving forecast quality in the region. This motivated us to focus on advancing DA for the Baltic Monitoring and Forecasting Centre (BAL MFC) in order to create a common framework for operational data assimilation in the Baltic Sea. We have implemented HBM-PDAF system based on the Parallel Data Assimilation Framework (PDAF), a highly versatile and optimised parallel suit with a choice of sequential schemes originally developed at AWI, and a hydrodynamic HIROMB-BOOS Model (HBM). At initial phase, only the satellite Sea Surface Temperature (SST) Level 3 data has been assimilated. Several related aspects are discussed, including improvements of the forecast quality for both surface and subsurface fields, the estimation of ensemble-based forecast error covariance, as well as possibilities of assimilating new types of observations, such as in-situ salinity and temperature profiles, coastal altimetry, and ice concentration.

The Evolution of Operational Satellite Based Remote Sensing in Support of Weather Analysis, Nowcasting, and Hazard Mitigation

NASA Astrophysics Data System (ADS)

Hughes, B. K.

2010-12-01

The mission of the National Oceanic and Atmospheric Administration (NOAA) National Environmental Data Information Service (NESDIS) is to provide timely access to global environmental data from satellites and other sources to promote, protect, and enhance America’s economy, security, environment, and quality of life. To fulfill its responsibilities, NESDIS acquires and manages America’s operational environmental satellites, operates the NOAA National Data Centers, provides data and information services including Earth system monitoring, performs official assessments of the environment, and conducts related research. The Nation’s fleet of operational environmental satellites has proven to be very critical in the detection, analysis, and forecast of natural or man-made phenomena. These assets have provided for the protection of people and property while safeguarding the Nation’s commerce and enabling safe and effective military operations. This presentation will take the audience through the evolution of operational satellite based remote sensing in support of weather forecasting, nowcasting, warning operations, hazard detection and mitigation. From the very first experiments involving radiation budget to today’s fleet of Geostationary and Polar Orbiting satellites to tomorrow’s constellation of high resolution imagers and hyperspectral sounders, environmental satellites sustain key observations for current and future generations.

February 11, 2009, Subcommittee on Aviation Hearing on FAA Reauthorization Act of 2009 questions for the record.

DOT National Transportation Integrated Search

2009-02-01

The next generation air transportation system (NextGen) includes the : policies, procedures, and equipment that will allow satellite-based navigation in the : national airspace system. However, this systems ability to meet forecasted traffic : vol...

The Primi Project: August-September 2009 Validation Cruise On Oil Spill Detection And Fate

NASA Astrophysics Data System (ADS)

Santoleri, R.; Bignami, F.; Bohm, E.; Nichio, F.; De Dominicis, M.; Ruggieri, G.; Marulllo, S.; Trivero, P.; Zambianchi, E.; Archetti, R.; Adamo, M.; Biamino, W.; Borasi, M.; Buongiorno Nardelli, B.; Cavagnero, M.; Colao, F.; Colella, S.; Coppini, G.; Debettio, V.; De Carolis, G.; Forneris, V.; Griffa, A.; Iacono, R.; Lombardi, E.; Manzella, G.; Mercantini, A.; Napolitano, E.; Pinardi, N.; Pandiscia, G.; Pisano, A.; Rupolo, V.; Reseghetti, F.; Sabia, L.; Sorgente, R.; Sprovieri, M.; Terranova, G.; Trani, M.; Volpe, G.

2010-04-01

In the framework of the ASI PRIMI Project, CNR- ISAC, in collaboration with the PRIMI partners, organized a validation cruise for the PRIMI oil spill monitoring and forecasting system on board the CNR R/V Urania. The cruise (Aug. 6 - Sept. 7 2009) took place in the Sicily Strait, an area affected by large oil tanker traffic. The cruise plan was organized in order to have the ship within the selected SAR image frames at acquisition time so that the ship could move toward the oil slick and verify it via visual and instrumental inspection. During the cruise, several oil spills, presumably being the result of illegal tank washing, were detected by the PRIMI system and were verified in situ. Preliminary results indicate that SAR and optical satellites are able to detect heavy and thin film oil spills, the maturity of oil spill forecasting models and that further work combining satellite, model and in situ data is necessary to assess the spill severity from the signature in satellite imagery.

The Fixed-Bias Langmuir Probe on the Communication-Navigation Outage Forecast System Satellite: Calibration and Validation

NASA Technical Reports Server (NTRS)

Klenzing, J.; Rowland, D.

2012-01-01

A fixed-bias spherical Langmuir probe is included as part of the Vector Electric Field Instrument (VEFI) suite on the Communication Navigation Outage Forecast System (CNOFS) satellite.CNOFS gathers data in the equatorial ionosphere between 400 and 860 km, where the primary constituent ions are H+ and O+. The ion current collected by the probe surface per unit plasma density is found to be a strong function of ion composition. The calibration of the collected current to an absolute density is discussed, and the performance of the spherical probe is compared to other in situ instruments on board the CNOFS satellite. The application of the calibration is discussed with respect to future fixed-bias probes; in particular, it is demonstrated that some density fluctuations will be suppressed in the collected current if the plasma composition rapidly changes along with density. This is illustrated in the observation of plasma density enhancements on CNOFS.

Applications of Satellite Remote Sensing Products to Enhance and Evaluate the AIRPACT Regional Air Quality Modeling System

NASA Astrophysics Data System (ADS)

Herron-Thorpe, F. L.; Mount, G. H.; Emmons, L. K.; Lamb, B. K.; Jaffe, D. A.; Wigder, N. L.; Chung, S. H.; Zhang, R.; Woelfle, M.; Vaughan, J. K.; Leung, F. T.

2013-12-01

The WSU AIRPACT air quality modeling system for the Pacific Northwest forecasts hourly levels of aerosols and atmospheric trace gases for use in determining potential health and ecosystem impacts by air quality managers. AIRPACT uses the WRF/SMOKE/CMAQ modeling framework, derives dynamic boundary conditions from MOZART-4 forecast simulations with assimilated MOPITT CO, and uses the BlueSky framework to derive fire emissions. A suite of surface measurements and satellite-based remote sensing data products across the AIRPACT domain are used to evaluate and improve model performance. Specific investigations include anthropogenic emissions, wildfire simulations, and the effects of long-range transport on surface ozone. In this work we synthesize results for multiple comparisons of AIRPACT with satellite products such as IASI ammonia, AIRS carbon monoxide, MODIS AOD, OMI tropospheric ozone and nitrogen dioxide, and MISR plume height. Features and benefits of the newest version of AIRPACT's web-interface are also presented.

Forecast of space shuttle flight requirements for launch of commercial communications satellites

NASA Technical Reports Server (NTRS)

1977-01-01

The number of communication satellites required over the next 25 years to support domestic and regional communication systems for telephony, telegraphy and other low speed data; video teleconferencing, new data services, direct TV broadcasting; INTELSAT; and maritime and aeronautical services was estimated to determine the number of space shuttle flights necessary for orbital launching.

Using JPSS Retrievals to Implement a Multisensor, Synoptic, Layered Water Vapor Product for Forecasters

NASA Astrophysics Data System (ADS)

Forsythe, J. M.; Jones, A. S.; Kidder, S. Q.; Fuell, K.; LeRoy, A.; Bikos, D.; Szoke, E.

2015-12-01

Forecasters have been using the NOAA operational blended total precipitable water (TPW) product, developed by the Cooperative Institute for Research in the Atmosphere (CIRA), since 2009. Blended TPW has a wide variety of uses related to heavy precipitation and flooding, such as measuring the amount of moisture in an atmospheric river originating in the tropics. But blended TPW conveys no information on the vertical distribution of moisture, which is relevant to a variety of forecast concerns. Vertical profile information is particularly lacking over the oceans for landfalling storms. A blended six-satellite, four-layer, layered water vapor product demonstrated by CIRA and the NASA Short-term Prediction Research and Transition Center (SPoRT) in allows forecasters to see the vertical distribution of water vapor in near real-time. National Weather Service (NWS) forecaster feedback indicated that this new, vertically-resolved view of water vapor has a substantial impact on forecasts. This product uses NOAA investments in polar orbiting satellite sounding retrievals from passive microwave radiances, in particular, the Microwave Integrated Retrieval System (MIRS). The product currently utilizes data from the NOAA-18 and -19 spacecraft, Metop-A and -B, and the Defense Meteorological Program (DMSP) F18 spacecraft. The sounding instruments onboard the Suomi-NPP and JPSS spacecraft will be cornerstone instruments in the future evolution of this product. Applications of the product to heavy rain cases will be presented and compared to commonly used data such as radiosondes and Geostationary Operational Environmental Satellite (GOES) water vapor channel imagery. Research is currently beginning to implement advective blending, where model winds are used to move the water vapor profiles to a common time. Interactions with the NOAA Satellite Analysis Branch (SAB), National Center for Environmental Prediction (NCEP) centers including the Ocean Prediction Center (OPC) and Weather Prediction Center (WPC) will be discussed.

The 30/20 GHz fixed communications systems service demand assessment. Volume 3: Appendices

NASA Technical Reports Server (NTRS)

Gabriszeski, T.; Reiner, P.; Rogers, J.; Terbo, W.

1979-01-01

The market analysis of voice, video, and data 18/30 GHz communications systems services and satellite transmission services is discussed. Detail calculations, computer displays of traffic, survey questionnaires, and detailed service forecasts are presented.

A forecast of broadcast satellite communications

NASA Technical Reports Server (NTRS)

Martino, J. P.; Lenz, R. C., Jr.

1977-01-01

This paper presents forecasts of likely changes in broadcast satellite technology, the technology of ground terminals, and the technology of terrestrial communications competitive with satellites. The impacts of these changes in technology are then assessed, using a cross-impact model of U.S. domestic telecommunications, to determine the consequences of various possible changes in communications satellite technology. These consequences are discussed in terms of various possible services, for households, businesses, and specialized customers, which might become economically viable as a result of improvements in satellite technology.

Petascale Diagnostic Assessment of the Global Portfolio Rainfall Space Missions' Ability to Support Flood Forecasting

NASA Astrophysics Data System (ADS)

Reed, P. M.; Chaney, N.; Herman, J. D.; Wood, E. F.; Ferringer, M. P.

2015-12-01

This research represents a multi-institutional collaboration between Cornell University, The Aerospace Corporation, and Princeton University that has completed a Petascale diagnostic assessment of the current 10 satellite missions providing rainfall observations. Our diagnostic assessment has required four core tasks: (1) formally linking high-resolution astrodynamics design and coordination of space assets with their global hydrological impacts within a Petascale "many-objective" global optimization framework, (2) developing a baseline diagnostic evaluation of a 1-degree resolution global implementation of the Variable Infiltration Capacity (VIC) model to establish the required satellite observation frequencies and coverage to maintain acceptable global flood forecasts, (3) evaluating the limitations and vulnerabilities of the full suite of current satellite precipitation missions including the recently approved Global Precipitation Measurement (GPM) mission, and (4) conceptualizing the next generation spaced-based platforms for water cycle observation. Our team exploited over 100 Million hours of computing access on the 700,000+ core Blue Waters machine to radically advance our ability to discover and visualize key system tradeoffs and sensitivities. This project represents to our knowledge the first attempt to develop a 10,000 member Monte Carlo global hydrologic simulation at one degree resolution that characterizes the uncertain effects of changing the available frequencies of satellite precipitation on drought and flood forecasts. The simulation—optimization components of the work have set a theoretical baseline for the best possible frequencies and coverages for global precipitation given unlimited investment, broad international coordination in reconfiguring existing assets, and new satellite constellation design objectives informed directly by key global hydrologic forecasting requirements. Our research poses a step towards realizing the integrated global water cycle observatory long sought by the World Climate Research Programme, which has to date eluded the world's space agencies.

Observing System Forecast Experiments at the DAO

NASA Technical Reports Server (NTRS)

Atlas, Robert

2001-01-01

Since the advent of meteorological satellites in the 1960's, numerous experiments have been conducted in order to evaluate the impact of these and other data on atmospheric analysis and prediction. Such studies have included both OSE'S and OSSE's. The OSE's were conducted to evaluate the impact of specific observations or classes of observations on analyses and forecasts. Such experiments have been performed for selected types of conventional data and for various satellite data sets as they became available. (See for example the 1989 ECMWF/EUMETSAT workshop proceedings on "The use of satellite data in operational numerical weather prediction" and the references contained therein.) The ODYSSEY were conducted to evaluate the potential for future observing systems to improve Numerical Weather Prediction NWP and to plan for the Global Weather Experiment and more recently for EVANS (Atlas et al., 1985a; Arnold and Day, 1986; Hoffman et al., 1990). In addition, OSSE's have been run to evaluate trade-offs in the design of observing systems and observing networks (Atlas and Emmitt, 1991; Rohaly and Krishnamurti, 1993), and to test new methodology for data assimilation (Atlas and Bloom, 1989).

A cross impact methodology for the assessment of US telecommunications system with application to fiber optics development: Executive summary

NASA Technical Reports Server (NTRS)

Martino, J. P.; Lenz, R. C., Jr.; Chen, K. L.

1979-01-01

A cross impact model of the U.S. telecommunications system was developed. For this model, it was necessary to prepare forecasts of the major segments of the telecommunications system, such as satellites, telephone, TV, CATV, radio broadcasting, etc. In addition, forecasts were prepared of the traffic generated by a variety of new or expanded services, such as electronic check clearing and point of sale electronic funds transfer. Finally, the interactions among the forecasts were estimated (the cross impacts). Both the forecasts and the cross impacts were used as inputs to the cross impact model, which could then be used to stimulate the future growth of the entire U.S. telecommunications system. By varying the inputs, technology changes or policy decisions with regard to any segment of the system could be evaluated in the context of the remainder of the system. To illustrate the operation of the model, a specific study was made of the deployment of fiber optics, throughout the telecommunications system.

A cross impact methodology for the assessment of US telecommunications system with application to fiber optics development, volume 1

NASA Technical Reports Server (NTRS)

Martino, J. P.; Lenz, R. C., Jr.; Chen, K. L.; Kahut, P.; Sekely, R.; Weiler, J.

1979-01-01

A cross impact model of the U.S. telecommunications system was developed. It was necessary to prepare forecasts of the major segments of the telecommunications system, such as satellites, telephone, TV, CATV, radio broadcasting, etc. In addition, forecasts were prepared of the traffic generated by a variety of new or expanded services, such as electronic check clearing and point of sale electronic funds transfer. Finally, the interactions among the forecasts were estimated (the cross impact). Both the forecasts and the cross impacts were used as inputs to the cross impact model, which could then be used to stimulate the future growth of the entire U.S. telecommunications system. By varying the inputs, technology changes or policy decisions with regard to any segment of the system could be evaluated in the context of the remainder of the system. To illustrate the operation of the model, a specific study was made of the deployment of fiber optics throughout the telecommunications system.

Development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia

NASA Astrophysics Data System (ADS)

Lee, Sojin; Song, Chul-han; Park, Rae Seol; Park, Mi Eun; Han, Kyung man; Kim, Jhoon; Choi, Myungje; Ghim, Young Sung; Woo, Jung-Hun

2016-04-01

To improve short-term particulate matter (PM) forecasts in South Korea, the initial distribution of PM composition, particularly over the upwind regions, is primarily important. To prepare the initial PM composition, the aerosol optical depth (AOD) data retrieved from a geostationary equatorial orbit (GEO) satellite sensor, GOCI (Geostationary Ocean Color Imager) which covers a part of Northeast Asia (113-146° E; 25-47° N), were used. Although GOCI can provide a higher number of AOD data in a semicontinuous manner than low Earth orbit (LEO) satellite sensors, it still has a serious limitation in that the AOD data are not available at cloud pixels and over high-reflectance areas, such as desert and snow-covered regions. To overcome this limitation, a spatiotemporal-kriging (STK) method was used to better prepare the initial AOD distributions that were converted into the PM composition over Northeast Asia. One of the largest advantages in using the STK method in this study is that more observed AOD data can be used to prepare the best initial AOD fields compared with other methods that use single frame of observation data around the time of initialization. It is demonstrated in this study that the short-term PM forecast system developed with the application of the STK method can greatly improve PM10 predictions in the Seoul metropolitan area (SMA) when evaluated with ground-based observations. For example, errors and biases of PM10 predictions decreased by ˜ 60 and ˜ 70{%}, respectively, during the first 6 h of short-term PM forecasting, compared with those without the initial PM composition. In addition, the influences of several factors on the performances of the short-term PM forecast were explored in this study. The influences of the choices of the control variables on the PM chemical composition were also investigated with the composition data measured via PILS-IC (particle-into-liquid sampler coupled with ion chromatography) and low air-volume sample instruments at a site near Seoul. To improve the overall performances of the short-term PM forecast system, several future research directions were also discussed and suggested.

Development of an aerosol assimilation/forecasting system with Himawari-8 aerosol products

NASA Astrophysics Data System (ADS)

Maki, T.; Yumimoto, K.; Tanaka, T. Y.; Yoshida, M.; Kikuchi, M.; Nagao, T. M.; Murakami, H.; Ogi, A.; Sekiyama, T. T.

2016-12-01

A new generation geostationary meteorological satellite (GMS), Himawari-8, was launched on 7 October 2014 and became operational on 7 July 2015. Himawari-8 is equipped with more advanced multispectral imager (Advanced Himawari Imager; AHI) ahead of other planned GMSs (e.g., GEOS-R). The AHI has 16 observational bands including three visible lights (i.e. RGB) with high spatial (0.5-2 km) and temporal (every 10 minutes full-disk images) resolutions, and provides about 50 times more data than previous GMSs. It is attractive characteristics for aerosol study that the visible and near-infrared observational bands allow us to obtain full-disk maps of aerosol optical properties (i.e., aerosol optical thickness (AOT) and ångström exponent) with unprecedented temporal resolution. Meteorological Research Institute (MRI)/JMA and Japan Aerospace Exploration Agency (JAXA) have been developing an aerosol assimilation/forecasting system with a global aerosol transport model (MASINGAR mk-2), 2 dimensional variational (2D-Var) method, and the Himawari-8 AOTs. Forecasting results are quantitatively validated by AOTs measured by AERONET and PM2.5 concentrations obtained by in-situ stations. Figure 1 shows model-predicted and satellite-observed AOTs during the 2016 Siberian wildfire. Upper and lower panels exhibit maps of AOT at analysis time (0000 UTC on May 18, 2016) and 27-hour forecast time (03 UTC on May 19, 2016), respectively. The 27-hour forecasted AOT starting with the analyzed initial condition (Figure 1f) successfully predicts heavy smokes covering the northern part of Japan, which forecast without assimilation (Figure 1e) failed to reproduces. Figure 1: Horizontal distribution of observed and forecasted AOTs at 0000 UTC 18 May, 2016 (analysis time; upper panels) and 0300 UTC 19 May, 2016 (18-h forecast from the analysis time; lower panel). (a, d) observed AOT from Himawari-8, (b, e) forecasted AOT without assimilation, and (c, f) forecast AOT with assimilation.

Calibration of Ocean Forcing with satellite Flux Estimates (COFFEE)

NASA Astrophysics Data System (ADS)

Barron, Charlie; Jan, Dastugue; Jackie, May; Rowley, Clark; Smith, Scott; Spence, Peter; Gremes-Cordero, Silvia

2016-04-01

Predicting the evolution of ocean temperature in regional ocean models depends on estimates of surface heat fluxes and upper-ocean processes over the forecast period. Within the COFFEE project (Calibration of Ocean Forcing with satellite Flux Estimates, real-time satellite observations are used to estimate shortwave, longwave, sensible, and latent air-sea heat flux corrections to a background estimate from the prior day's regional or global model forecast. These satellite-corrected fluxes are used to prepare a corrected ocean hindcast and to estimate flux error covariances to project the heat flux corrections for a 3-5 day forecast. In this way, satellite remote sensing is applied to not only inform the initial ocean state but also to mitigate errors in surface heat flux and model representations affecting the distribution of heat in the upper ocean. While traditional assimilation of sea surface temperature (SST) observations re-centers ocean models at the start of each forecast cycle, COFFEE endeavors to appropriately partition and reduce among various surface heat flux and ocean dynamics sources. A suite of experiments in the southern California Current demonstrates a range of COFFEE capabilities, showing the impact on forecast error relative to a baseline three-dimensional variational (3DVAR) assimilation using operational global or regional atmospheric forcing. Experiment cases combine different levels of flux calibration with assimilation alternatives. The cases use the original fluxes, apply full satellite corrections during the forecast period, or extend hindcast corrections into the forecast period. Assimilation is either baseline 3DVAR or standard strong-constraint 4DVAR, with work proceeding to add a 4DVAR expanded to include a weak constraint treatment of the surface flux errors. Covariance of flux errors is estimated from the recent time series of forecast and calibrated flux terms. While the California Current examples are shown, the approach is equally applicable to other regions. These approaches within a 3DVAR application are anticipated to be useful for global and larger regional domains where a full 4DVAR methodology may be cost-prohibitive.

AIR QUALITY FORECAST VERIFICATION USING SATELLITE DATA

EPA Science Inventory

NOAA 's operational geostationary satellite retrievals of aerosol optical depths (AODs) were used to verify National Weather Service (NWS) experimental (research mode) particulate matter (PM2.5) forecast guidance issued during the summer 2004 International Consortium for Atmosp...

NASA's Potential Contributions to Avalanche Forecasting Using Active and Passive Microwave Measurements

NASA Technical Reports Server (NTRS)

Blonski, Slawomir

2007-01-01

This Candidate Solution is based on using active and passive microwave measurements acquired from NASA satellites to improve USDA (U.S. Department of Agriculture) Forest Service forecasting of avalanche danger. Regional Avalanche Centers prepare avalanche forecasts using ground measurements of snowpack and mountain weather conditions. In this Solution, range of the in situ observations is extended by adding remote sensing measurements of snow depth, snow water equivalent, and snowfall rate acquired by satellite missions that include Aqua, CloudSat, future GPM (Global Precipitation Measurement), and the proposed SCLP (Snow and Cold Land Processes). Measurements of snowpack conditions and time evolution are improved by combining the in situ and satellite observations with a snow model. Recurring snow observations from NASA satellites increase accuracy of avalanche forecasting, which helps the public and the managers of public facilities make better avalanche safety decisions.

Improved Orbit Determination and Forecasts with an Assimilative Tool for Satellite Drag Specification

NASA Astrophysics Data System (ADS)

Pilinski, M.; Crowley, G.; Sutton, E.; Codrescu, M.

2016-09-01

Much as aircraft are affected by the prevailing winds and weather conditions in which they fly, satellites are affected by the variability in density and motion of the near earth space environment. Drastic changes in the neutral density of the thermosphere, caused by geomagnetic storms or other phenomena, result in perturbations of LEO satellite motions through drag on the satellite surfaces. This can lead to difficulties in locating important satellites, temporarily losing track of satellites, and errors when predicting collisions in space. As the population of satellites in Earth orbit grows, higher space-weather prediction accuracy is required for critical missions, such as accurate catalog maintenance, collision avoidance for manned and unmanned space flight, reentry prediction, satellite lifetime prediction, defining on-board fuel requirements, and satellite attitude dynamics. We describe ongoing work to build a comprehensive nowcast and forecast system for specifying the neutral atmospheric state related to orbital drag conditions. The system outputs include neutral density, winds, temperature, composition, and the satellite drag derived from these parameters. This modeling tool is based on several state-of-the-art coupled models of the thermosphere-ionosphere as well as several empirical models running in real-time and uses assimilative techniques to produce a thermospheric nowcast. This software will also produce 72 hour predictions of the global thermosphere-ionosphere system using the nowcast as the initial condition and using near real-time and predicted space weather data and indices as the inputs. In this paper, we will review the driving requirements for our model, summarize the model design and assimilative architecture, and present preliminary validation results. Validation results will be presented in the context of satellite orbit errors and compared with several leading atmospheric models. As part of the analysis, we compare the drag observed by a variety of satellites which were not used as part of the assimilation-dataset and whose perigee altitudes span a range from 200 km to 700 km.

Operational Numerical Weather Prediction at the Met Office and potential ways forward for operational space weather prediction systems

NASA Astrophysics Data System (ADS)

Jackson, David

NICT (National Institute of Information and Communications Technology) has been in charge of space weather forecast service in Japan for more than 20 years. The main target region of the space weather is the geo-space in the vicinity of the Earth where human activities are dominant. In the geo-space, serious damages of satellites, international space stations and astronauts take place caused by energetic particles or electromagnetic disturbances: the origin of the causes is dynamically changing of solar activities. Positioning systems via GPS satellites are also im-portant recently. Since the most significant effect of positioning error comes from disturbances of the ionosphere, it is crucial to estimate time-dependent modulation of the electron density profiles in the ionosphere. NICT is one of the 13 members of the ISES (International Space Environment Service), which is an international assembly of space weather forecast centers under the UNESCO. With help of geo-space environment data exchanging among the member nations, NICT operates daily space weather forecast service every day to provide informa-tion on forecasts of solar flare, geomagnetic disturbances, solar proton event, and radio-wave propagation conditions in the ionosphere. The space weather forecast at NICT is conducted based on the three methodologies: observations, simulations and informatics (OSI model). For real-time or quasi real-time reporting of space weather, we conduct our original observations: Hiraiso solar observatory to monitor the solar activity (solar flare, coronal mass ejection, and so on), domestic ionosonde network, magnetometer HF radar observations in far-east Siberia, and south-east Asia low-latitude ionosonde network (SEALION). Real-time observation data to monitor solar and solar-wind activities are obtained through antennae at NICT from ACE and STEREO satellites. We have a middle-class super-computer (NEC SX-8R) to maintain real-time computer simulations for solar and solar-wind, magnetosphere and ionosphere. The three simulations are directly or indirectly connected each other based on real-time observa-tion data to reproduce a virtual geo-space region on the super-computer. Informatics is a new methodology to make precise forecast of space weather. Based on new information and communication technologies (ICT), it provides more information in both quality and quantity. At NICT, we have been developing a cloud-computing system named "space weather cloud" based on a high-speed network system (JGN2+). Huge-scale distributed storage (1PB), clus-ter computers, visualization systems and other resources are expected to derive new findings and services of space weather forecasting. The final goal of NICT space weather service is to predict near-future space weather conditions and disturbances which will be causes of satellite malfunctions, tele-communication problems, and error of GPS navigations. In the present talk, we introduce our recent activities on the space weather services and discuss how we are going to develop the services from the view points of space science and practical uses.

Forecasting forecast skill

NASA Technical Reports Server (NTRS)

Kalnay, Eugenia; Dalcher, Amnon

1987-01-01

It is shown that it is possible to predict the skill of numerical weather forecasts - a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average correlation) between members of an ensemble of forecasts started from five different analyses. The analyses had been previously derived for satellite-data-impact studies and included, in the Northern Hemisphere, moderate perturbations associated with the use of different observing systems. When the Northern Hemisphere was used as a verification region, the prediction of skill was rather poor. This is due to the fact that such a large area usually contains regions with excellent forecasts as well as regions with poor forecasts, and does not allow for discrimination between them. However, when regional verifications were used, the ensemble forecast dispersion provided a very good prediction of the quality of the individual forecasts.

KSC-2009-3841

NASA Image and Video Library

2009-06-25

CAPE CANAVERAL, Fla. – A prelaunch news conference on the Geostationary Operational Environmental Satellite-O mission is held in NASA's Kennedy Space Center press site auditorium. From left, the participants are George H. Diller, moderator, Media Services, Kennedy Space Center; Gary Davis, director, Office of Systems Development, NOAA Satellite and Information Service, Suitland, Md.; Kris Walsh, Commercial Programs manager, United Launch Alliance, Houston; Kevin Reyes, director, Business Development, Boeing Launch Services; Andre Dress, GOES-O deputy project manager, Goddard Space Flight Center; Charlie Maloney, GOES-O program manager, Boeing Space and Intelligence Systems, Seal Beach, Calif.; Bart Hagemeyer, meteorologist in charge, NOAA National Weather Service forecast office, Melbourne, Fla.; and Joel Tumbiolo, Delta IV launch weather officer, 45th Weather Squadron, Cape Canaveral Air Force Station. The GOES-O satellite is targeted to launch June 26. The latest Geostationary Operational Environmental Satellite, GOES-O was developed by NASA for the National Oceanic and Atmospheric Administration, or NOAA. Each of the GOES satellites continuously provides observations of 60 percent of the Earth including the continental United States, providing weather monitoring and forecast operations as well as a continuous and reliable stream of environmental information and severe weather warnings. Once in orbit, GOES-O will be designated GOES-14, and NASA will provide on-orbit checkout and then transfer operational responsibility to NOAA. Photo credit: NASA/Jim Grossmann

Impact of CYGNSS Data on Tropical Cyclone Analyses and Forecasts in a Regional OSSE Framework

NASA Astrophysics Data System (ADS)

Annane, B.; McNoldy, B. D.; Leidner, S. M.; Atlas, R. M.; Hoffman, R.; Majumdar, S.

2016-12-01

The Cyclone Global Navigation Satellite System, or CYGNSS, is a planned constellation of micro-satellites that will utilize reflected Global Positioning System (GPS) satellite signals to retrieve ocean surface wind speed along the satellites' ground tracks. The orbits are designed so that there is excellent coverage of the tropics and subtropics, resulting in more thorough spatial sampling and improved sampling intervals over tropical cyclones than is possible with current spaceborne scatterometer and passive microwave sensor platforms. Furthermore, CYGNSS will be able to retrieve winds under all precipitating conditions, and over a large range of wind speeds.A regional Observing System Simulation Experiment (OSSE) framework was developed at NOAA/AOML and University of Miami that features a high-resolution regional nature run (27-km regional domain with 9/3/1 km storm-following nests; Nolan et al., 2013) embedded within a lower-resolution global nature run . Simulated observations are generated by sampling from the nature run and are provided to a data assimilation scheme, which produces analyses for a high-resolution regional forecast model, the 2014 operational Hurricane-WRF model. For data assimilation, NOAA's GSI and EnKF systems are used. Analyses are performed on the parent domain at 9-km resolution. The forecast model uses a single storm-following 3-km resolution nest. Synthetic CYGNSS wind speed data have also been created, and the impacts of the assimilation of these data on the forecasts of tropical cyclone track and intensity will be discussed.In addition to the choice of assimilation scheme, we have also examined a number of other factors/parameters that effect the impact of simulated CYGNSS observations, including frequency of data assimilation cycling (e.g., hourly, 3-hourly and 6-hourly) and the assimilation of scalar versus vector synthetic CYGNSS winds.We have found sensitivity to all of the factors tested and will summarize the methods used for testing as well as results. Generally, we have found that more frequent cycling is better than less; and flow-dependent background error covariances (e.g., EnKF) are better than static or climatological assumptions about the background error covariance.

Analysis of the US Air Force Defense Meteorological Satellite Program Imagery for Global Lightning

NASA Technical Reports Server (NTRS)

Scharfen, Gregory R.

1999-01-01

The U. S. Air Force operates the Defense Meteorological Satellite Program (DMSP), a system of near-polar orbiting satellites designed for use in operational weather forecasting and other applications. DMSP satellites carry a suite of sensors that provide images of the earth and profiles of the atmosphere. The National Snow and Ice Data Center (NSIDC) at the University of Colorado has been involved with the archival of DMSP data and its use for several research projects since 1979. This report summarizes the portion of this involvement funded by NASA.

Detection of mesoscale zones of atmospheric instabilities using remote sensing and weather forecasting model data

NASA Astrophysics Data System (ADS)

Winnicki, I.; Jasinski, J.; Kroszczynski, K.; Pietrek, S.

2009-04-01

The paper presents elements of research conducted in the Faculty of Civil Engineering and Geodesy of the Military University of Technology, Warsaw, Poland, concerning application of mesoscale models and remote sensing data to determining meteorological conditions of aircraft flight directly related with atmospheric instabilities. The quality of meteorological support of aviation depends on prompt and effective forecasting of weather conditions changes. The paper presents a computer module for detecting and monitoring zones of cloud cover, precipitation and turbulence along the aircraft flight route. It consists of programs and scripts for managing, processing and visualizing meteorological and remote sensing databases. The application was developed in Matlab® for Windows®. The module uses products of COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) mesoscale non-hydrostatic model of the atmosphere developed by the US Naval Research Laboratory, satellite images acquisition system from the MSG-2 (Meteosat Second Generation) of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and meteorological radars data acquired from the Institute of Meteorology and Water Management (IMGW), Warsaw, Poland. The satellite images acquisition system and the COAMPS model are run operationally in the Faculty of Civil Engineering and Geodesy. The mesoscale model is run on an IA64 Feniks multiprocessor 64-bit computer cluster. The basic task of the module is to enable a complex analysis of data sets of miscellaneous information structure and to verify COAMPS results using satellite and radar data. The research is conducted using uniform cartographic projection of all elements of the database. Satellite and radar images are transformed into the Lambert Conformal projection of COAMPS. This facilitates simultaneous interpretation and supports decision making process for safe execution of flights. Forecasts are based on horizontal distributions and vertical profiles of meteorological parameters produced by the module. Verification of forecasts includes research of spatial and temporal correlations of structures generated by the model, e.g.: cloudiness, meteorological phenomena (fogs, precipitation, turbulence) and structures identified on current satellite images. The developed module determines meteorological parameters fields for vertical profiles of the atmosphere. Interpolation procedures run at user selected standard (pressure) or height levels of the model enable to determine weather conditions along any route of aircraft. Basic parameters of the procedures determining e.g. flight safety include: cloud base, visibility, cloud cover, turbulence coefficient, icing and precipitation intensity. Determining icing and turbulence characteristics is based on standard and new methods (from other mesoscale models). The research includes also investigating new generation mesoscale models, especially remote sensing data assimilation. This is required by necessity to develop and introduce objective methods of forecasting weather conditions. Current research in the Faculty of Civil Engineering and Geodesy concerns validation of the mesoscale module performance.

The surface drifter program for real time and off-line validation of ocean forecasts and reanalyses

NASA Astrophysics Data System (ADS)

Hernandez, Fabrice; Regnier, Charly; Drévillon, Marie

2017-04-01

As part of the Global Ocean Observing System, the Global Drifter Program (GDP) is comprised of an array of about 1250 drifting buoys spread over the global ocean, that provide operational, near-real time surface velocity, sea surface temperature (SST) and sea level pressure observations. This information is used mainly used for numerical weather forecasting, research, and in-situ calibration/verification of satellite observations. Since 2013 the drifting buoy SST measurements are used for near real time assessment of global forecasting systems from Canada, France, UK, USA, Australia in the frame of the GODAE OceanView Intercomparison and Validation Task. For most of these operational systems, these data are not used for assimilation, and offer an independent observation assessment. This approach mimics the validation performed for SST satellite products. More recently, validation procedures have been proposed in order to assess the surface dynamics of Mercator Océan global and regional forecast and reanalyses. Velocities deduced from drifter trajectories are used in two ways. First, the Eulerian approach where buoy and ocean model velocity values are compared at the position of drifters. Then, from discrepancies, statistics are computed and provide an evaluation of the ocean model's surface dynamics reliability. Second, the Lagrangian approach, where drifting trajectories are simulated at each location of the real drifter trajectory using the ocean model velocity fields. Then, on daily basis, real and simulated drifter trajectories are compared by analyzing the spread after one day, two days etc…. The cumulated statistics on specific geographical boxes are evaluated in term of dispersion properties of the "real ocean" as captured by drifters, and those properties in the ocean model. This approach allows to better evaluate forecasting score for surface dispersion applications, like Search and Rescue, oil spill forecast, drift of other objects or contaminant, larvae dispersion etc… These Eulerian and Lagrangian validation approach can be applied for real time or offline assessment of ocean velocity products. In real time, the main limitation is our capability to detect drifter drogue's loss, causing erroneous assessment. Several methods, by comparison to wind entrainment effect or other velocity estimates like from satellite altimetry, are used. These Eulerian and Lagrangian surface velocity validation methods are planned to be adopted by the GODAE OceanView operational community in order to offer independent verification of surface current forecast.

Impact of assimilating GOES imager clear-sky radiance with a rapid refresh assimilation system for convection-permitting forecast over Mexico

NASA Astrophysics Data System (ADS)

Yang, Chun; Liu, Zhiquan; Gao, Feng; Childs, Peter P.; Min, Jinzhong

2017-05-01

The Geostationary Operational Environmental Satellite (GOES) imager data could provide a continuous image of the evolutionary pattern of severe weather phenomena with its high spatial and temporal resolution. The capability to assimilate the GOES imager radiances has been developed within the Weather Research and Forecasting model's data assimilation system. Compared to the benchmark experiment with no GOES imager data, the impact of assimilating GOES imager radiances on the analysis and forecast of convective process over Mexico in 7-10 March 2016 was assessed through analysis/forecast cycling experiments using rapid refresh assimilation system with hybrid-3DEnVar scheme. With GOES imager radiance assimilation, better analyses were obtained in terms of the humidity, temperature, and simulated water vapor channel brightness temperature distribution. Positive forecast impacts from assimilating GOES imager radiance were seen when verified against the Tropospheric Airborne Meteorological Data Reporting observation, GOES imager observation, and Mexico station precipitation data.

Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Service (KMS)

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

2014-01-01

SPoRT/SERVIR/RCMRD/KMS Collaboration: Builds off strengths of each organization. SPoRT: Transition of satellite, modeling and verification capabilities; SERVIR-Africa/RCMRD: International capacity-building expertise; KMS: Operational organization with regional weather forecasting expertise in East Africa. Hypothesis: Improved land-surface initialization over Eastern Africa can lead to better temperature, moisture, and ultimately precipitation forecasts in NWP models. KMS currently initializes Weather Research and Forecasting (WRF) model with NCEP/Global Forecast System (GFS) model 0.5-deg initial / boundary condition data. LIS will provide much higher-resolution land-surface data at a scale more representative to regional WRF configuration. Future implementation of real-time NESDIS/VIIRS vegetation fraction to further improve land surface representativeness.

Advancing land surface model development with satellite-based Earth observations

NASA Astrophysics Data System (ADS)

Orth, Rene; Dutra, Emanuel; Trigo, Isabel F.; Balsamo, Gianpaolo

2017-04-01

The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help to improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology, but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability and understanding of climate system feedbacks. Orth, R., E. Dutra, I. F. Trigo, and G. Balsamo (2016): Advancing land surface model development with satellite-based Earth observations. Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-628

Weather monitoring and forecasting over eastern Attica (Greece) in the frame of FLIRE project

NASA Astrophysics Data System (ADS)

Kotroni, Vassiliki; Lagouvardos, Konstantinos; Chrysoulakis, Nektarios; Makropoulos, Christtos; Mimikou, Maria; Papathanasiou, Chrysoula; Poursanidis, Dimitris

2015-04-01

In the frame of FLIRE project a Decision Support System has been built with the aim to support decision making of Civil Protection Agencies and local stakeholders in the area of east Attica (Greece), in the cases of forest fires and floods. In this presentation we focus on a specific action that focuses on the provision of high resolution short-term weather forecasting data as well as of dense meteorological observations over the study area. Both weather forecasts and observations serve as an input in the Weather Information Management Tool (WIMT) of the Decision Support System. We focus on: (a) the description of the adopted strategy for setting-up the operational weather forecasting chain that provides the weather forecasts for the FLIRE project needs, (b) the presentation of the surface network station that provides real-time weather monitoring of the study area and (c) the strategy adopted for issuing smart alerts for thunderstorm forecasting based of real-time lightning observations as well as satellite observations.

Impact of single-point GPS integrated water vapor estimates on short-range WRF model forecasts over southern India

NASA Astrophysics Data System (ADS)

Kumar, Prashant; Gopalan, Kaushik; Shukla, Bipasha Paul; Shyam, Abhineet

2017-11-01

Specifying physically consistent and accurate initial conditions is one of the major challenges of numerical weather prediction (NWP) models. In this study, ground-based global positioning system (GPS) integrated water vapor (IWV) measurements available from the International Global Navigation Satellite Systems (GNSS) Service (IGS) station in Bangalore, India, are used to assess the impact of GPS data on NWP model forecasts over southern India. Two experiments are performed with and without assimilation of GPS-retrieved IWV observations during the Indian winter monsoon period (November-December, 2012) using a four-dimensional variational (4D-Var) data assimilation method. Assimilation of GPS data improved the model IWV analysis as well as the subsequent forecasts. There is a positive impact of ˜10 % over Bangalore and nearby regions. The Weather Research and Forecasting (WRF) model-predicted 24-h surface temperature forecasts have also improved when compared with observations. Small but significant improvements were found in the rainfall forecasts compared to control experiments.

AIRS associated accomplishments at the JCSDA: First use of full spatial resolution hyperspectral data show significant improvements in global forecasts

NASA Astrophysics Data System (ADS)

Le Marshall, J.; Jung, J.; Lord, S. J.; Derber, J. C.; Treadon, R.; Joiner, J.; Goldberg, M.; Wolf, W.; Liu, H. C.

2005-08-01

The National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), and Department of Defense (DoD), Joint Center for Satellite Data Assimilation (JCSDA) was established in 2000/2001. The goal of the JCSDA is to accelerate the use of observations from earth-orbiting satellites into operational numerical environmental analysis and prediction systems for the purpose of improving weather and oceanic forecasts, seasonal climate forecasts and the accuracy of climate data sets. As a result, a series of data assimilation experiments were undertaken at the JCSDA as part of the preparations for the operational assimilation of AIRS data by its partner organizations1,2. Here, for the first time full spatial resolution radiance data, available in real-time from the AIRS instrument, were used at the JCSDA in data assimilation studies over the globe utilizing the operational NCEP Global Forecast System (GFS). The radiance data from each channel of the instrument were carefully screened for cloud effects and those radiances which were deemed to be clear of cloud effects were used by the GFS forecast system. The result of these assimilation trials has been a first demonstration of significant improvements in forecast skill over both the Northern and Southern Hemisphere compared to the operational system without AIRS data. The experimental system was designed in a way that rendered it feasible for operational application, and that constraint involved using the subset of AIRS channels chosen for operational distribution and an analysis methodology close to the current analysis practice, with particular consideration given to time limitations. As a result, operational application of these AIRS data was enabled by the recent NCEP operational upgrade. In addition, because of the improved impact resulting from use of this enhanced data set compared to that used operationally to date, provision of a realtime "warmest field" of view data set has been established for use by international NWP Centers.

Frost Forecasting for Fruitgrowers

NASA Technical Reports Server (NTRS)

Martsolf, J. D.; Chen, E.

1983-01-01

Progress in forecasting from satellite data reviewed. University study found data from satellites displayed in color and used to predict frost are valuable aid to agriculture. Study evaluated scheme to use Earth-temperature data from Geostationary Operational Environmental Satellite in computer model that determines when and where freezing temperatures endanger developing fruit crops, such as apples, peaches and cherries in spring and citrus crops in winter.

Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi

2014-01-01

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA's Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Previously, it has been shown that cloud top designation associated with quality control procedures within the Gridpoint Statistical Interpolation (GSI) system used operationally by a number of Joint Center for Satellite Data Assimilation (JCSDA) partners may not provide the best representation of cloud top pressure (CTP). Because this designated CTP determines which channels are cloud-free and, thus, available for assimilation, ensuring the most accurate representation of this value is imperative to obtaining the greatest impact from satellite radiances. This paper examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing analysis increments and numerical forecasts generated using operational techniques with a research technique that swaps CTP from the Moderate-resolution Imaging Spectroradiometer (MODIS) for the value of CTP calculated from the radiances within GSI.

Results on SSH neural network forecasting in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Rixen, Michel; Beckers, Jean-Marie; Alvarez, Alberto; Tintore, Joaquim

2002-01-01

Nowadays, satellites are the only monitoring systems that cover almost continuously all possible ocean areas and are now an essential part of operational oceanography. A novel approach based on artificial intelligence (AI) concepts, exploits pasts time series of satellite images to infer near future ocean conditions at the surface by neural networks and genetic algorithms. The size of the AI problem is drastically reduced by splitting the spatio-temporal variability contained in the remote sensing data by using empirical orthogonal function (EOF) decomposition. The problem of forecasting the dynamics of a 2D surface field can thus be reduced by selecting the most relevant empirical modes, and non-linear time series predictors are then applied on the amplitudes only. In the present case study, we use altimetric maps of the Mediterranean Sea, combining TOPEX-POSEIDON and ERS-1/2 data for the period 1992 to 1997. The learning procedure is applied to each mode individually. The final forecast is then reconstructed form the EOFs and the forecasted amplitudes and compared to the real observed field for validation of the method.

A parsimonious land data assimilation system for the SMAP/GPM satellite era

USDA-ARS?s Scientific Manuscript database

Land data assimilation systems typically require complex parameterizations in order to: define required observation operators, quantify observing/forecasting errors and calibrate a land surface assimilation model. These parameters are commonly defined in an arbitrary manner and, if poorly specified,...

MMAB Operational Products

Science.gov Websites

Atlantic Real-Time Ocean Forecast System NOAA Wavewatch III® Ocean Wave Model Sea Ice Concentration Analysis Satellite Derived Ocean Surface Winds Daily Sea Surface Temperature Analysis Sea Ice Drift Model

The use of satellite data assimilation methods in regional NWP for solar irradiance forecasting

NASA Astrophysics Data System (ADS)

Kurzrock, Frederik; Cros, Sylvain; Chane-Ming, Fabrice; Potthast, Roland; Linguet, Laurent; Sébastien, Nicolas

2016-04-01

As an intermittent energy source, the injection of solar power into electricity grids requires irradiance forecasting in order to ensure grid stability. On time scales of more than six hours ahead, numerical weather prediction (NWP) is recognized as the most appropriate solution. However, the current representation of clouds in NWP models is not sufficiently precise for an accurate forecast of solar irradiance at ground level. Dynamical downscaling does not necessarily increase the quality of irradiance forecasts. Furthermore, incorrectly simulated cloud evolution is often the cause of inaccurate atmospheric analyses. In non-interconnected tropical areas, the large amplitudes of solar irradiance variability provide abundant solar yield but present significant problems for grid safety. Irradiance forecasting is particularly important for solar power stakeholders in these regions where PV electricity penetration is increasing. At the same time, NWP is markedly more challenging in tropic areas than in mid-latitudes due to the special characteristics of tropical homogeneous convective air masses. Numerous data assimilation methods and strategies have evolved and been applied to a large variety of global and regional NWP models in the recent decades. Assimilating data from geostationary meteorological satellites is an appropriate approach. Indeed, models converting radiances measured by satellites into cloud properties already exist. Moreover, data are available at high temporal frequencies, which enable a pertinent cloud cover evolution modelling for solar energy forecasts. In this work, we present a survey of different approaches which aim at improving cloud cover forecasts using the assimilation of geostationary meteorological satellite data into regional NWP models. Various approaches have been applied to a variety of models and satellites and in different regions of the world. Current methods focus on the assimilation of cloud-top information, derived from infrared channels. For example, those information have been directly assimilated by modifying the water vapour profile in the initial conditions of the WRF model in California using GOES satellite imagery. In Europe, the assimilation of cloud-top height and relative humidity has been performed in an indirect approach using an ensemble Kalman filter. In this case Meteosat SEVIRI cloud information has been assimilated in the COSMO model. Although such methods generally provide improved cloud cover forecasts in mid-latitudes, the major limitation is that only clear-sky or completely cloudy cases can be considered. Indeed, fractional clouds cause a measured signal mixing cold clouds and warmer Earth surface. If the model's initial state is directly forced by cloud properties observed by satellite, the changed model fields have to be smoothed in order to avoid numerical instability. Other crucial aspects which influence forecast quality in the case of satellite radiance assimilation are channel selection, bias and error treatment. The overall promising satellite data assimilation methods in regional NWP have not yet been explicitly applied and tested under tropical conditions. Therefore, a deeper understanding on the benefits of such methods is necessary to improve irradiance forecast schemes.

Polar2Grid 2.0: Reprojecting Satellite Data Made Easy

NASA Astrophysics Data System (ADS)

Hoese, D.; Strabala, K.

2015-12-01

Polar-orbiting multi-band meteorological sensors such as those on the Suomi National Polar-orbiting Partnership (SNPP) satellite pose substantial challenges for taking imagery the last mile to forecast offices, scientific analysis environments, and the general public. To do this quickly and easily, the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin has created an open-source, modular application system, Polar2Grid. This bundled solution automates tools for converting various satellite products like those from VIIRS and MODIS into a variety of output formats, including GeoTIFFs, AWIPS compatible NetCDF files, and NinJo forecasting workstation compatible TIFF images. In addition to traditional visible and infrared imagery, Polar2Grid includes three perceptual enhancements for the VIIRS Day-Night Band (DNB), as well as providing the capability to create sharpened true color, sharpened false color, and user-defined RGB images. Polar2Grid performs conversions and projections in seconds on large swaths of data. Polar2Grid is currently providing VIIRS imagery over the Continental United States, as well as Alaska and Hawaii, from various Direct-Broadcast antennas to operational forecasters at the NOAA National Weather Service (NWS) offices in their AWIPS terminals, within minutes of an overpass of the Suomi NPP satellite. Three years after Polar2Grid development started, the Polar2Grid team is now releasing version 2.0 of the software; supporting more sensors, generating more products, and providing all of its features in an easy to use command line interface.

Seasat data applications in ocean industries

NASA Technical Reports Server (NTRS)

Montgomery, D. R.

1985-01-01

It is pointed out that the world population expansion and resulting shortages of food, minerals, and fuel have focused additional attention on the world's oceans. In this context, aspects of weather prediction and the monitoring/prediction of long-range climatic anomalies become more important. In spite of technological advances, the commercial ocean industry and the naval forces suffer now from inadequate data and forecast products related to the oceans. The Seasat Program and the planned Navy-Remote Oceanographic Satellite System (N-ROSS) represent major contributions to improved observational coverage and the processing needed to achieve better forecasts. The Seasat Program was initiated to evaluate the effectiveness of the remote sensing of oceanographic phenomena from a satellite platform. Possible oceanographic satellite applications are presented in a table, and the impact of Seasat data on industry sectors is discussed. Attention is given to offshore oil development, deep-ocean mining, fishing, and marine transportation.

Development of a multi-sensor based urban discharge forecasting system using remotely sensed data: A case study of extreme rainfall in South Korea

NASA Astrophysics Data System (ADS)

Yoon, Sunkwon; Jang, Sangmin; Park, Kyungwon

2017-04-01

Extreme weather due to changing climate is a main source of water-related disasters such as flooding and inundation and its damage will be accelerated somewhere in world wide. To prevent the water-related disasters and mitigate their damage in urban areas in future, we developed a multi-sensor based real-time discharge forecasting system using remotely sensed data such as radar and satellite. We used Communication, Ocean and Meteorological Satellite (COMS) and Korea Meteorological Agency (KMA) weather radar for quantitative precipitation estimation. The Automatic Weather System (AWS) and McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) were used for verification of rainfall accuracy. The optimal Z-R relation was applied the Tropical Z-R relationship (Z=32R1.65), it has been confirmed that the accuracy is improved in the extreme rainfall events. In addition, the performance of blended multi-sensor combining rainfall was improved in 60mm/h rainfall and more strong heavy rainfall events. Moreover, we adjusted to forecast the urban discharge using Storm Water Management Model (SWMM). Several statistical methods have been used for assessment of model simulation between observed and simulated discharge. In terms of the correlation coefficient and r-squared discharge between observed and forecasted were highly correlated. Based on this study, we captured a possibility of real-time urban discharge forecasting system using remotely sensed data and its utilization for real-time flood warning. Acknowledgement This research was supported by a grant (13AWMP-B066744-01) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport (MOLIT) of Korean government.

Satellite Capabilities Mapping - Utilizing Small Satellites

DTIC Science & Technology

2010-09-01

Metrics Definition…………………………..50 Figure 19. System and Requirements Decomposition…………………………………...59 Figure 20. TPS Fuctional Mapping Process...offered by small satellites. “The primary force in our corner of the universe is our sun. The sun is constantly radiating enormous amounts of...weather prediction models, a primary tool for forecasting weather” [19]. The NPOESS was a tri-agency program intended to develop and operate the next

Advance Technology Satellites in the Commercial Environment. Volume 2: Final Report

NASA Technical Reports Server (NTRS)

1984-01-01

A forecast of transponder requirements was obtained. Certain assumptions about system configurations are implicit in this process. The factors included are interpolation of baseline year values to produce yearly figures, estimation of satellite capture, effects of peak-hours and the time-zone staggering of peak hours, circuit requirements for acceptable grade of service capacity of satellite transponders, including various compression methods where applicable, and requirements for spare transponders in orbit. The graphical distribution of traffic requirements was estimated.

Applications of satellite snow cover in computerized short-term streamflow forecasting. [Conejos River, Colorado

NASA Technical Reports Server (NTRS)

Leaf, C. F.

1975-01-01

A procedure is described whereby the correlation between: (1) satellite derived snow-cover depletion and (2) residual snowpack water equivalent, can be used to update computerized residual flow forecasts for the Conejos River in southern Colorado.

Activities of NICT space weather project

NASA Astrophysics Data System (ADS)

Murata, Ken T.; Nagatsuma, Tsutomu; Watari, Shinichi; Shinagawa, Hiroyuki; Ishii, Mamoru

NICT (National Institute of Information and Communications Technology) has been in charge of space weather forecast service in Japan for more than 20 years. The main target region of the space weather is the geo-space in the vicinity of the Earth where human activities are dominant. In the geo-space, serious damages of satellites, international space stations and astronauts take place caused by energetic particles or electromagnetic disturbances: the origin of the causes is dynamically changing of solar activities. Positioning systems via GPS satellites are also im-portant recently. Since the most significant effect of positioning error comes from disturbances of the ionosphere, it is crucial to estimate time-dependent modulation of the electron density profiles in the ionosphere. NICT is one of the 13 members of the ISES (International Space Environment Service), which is an international assembly of space weather forecast centers under the UNESCO. With help of geo-space environment data exchanging among the member nations, NICT operates daily space weather forecast service every day to provide informa-tion on forecasts of solar flare, geomagnetic disturbances, solar proton event, and radio-wave propagation conditions in the ionosphere. The space weather forecast at NICT is conducted based on the three methodologies: observations, simulations and informatics (OSI model). For real-time or quasi real-time reporting of space weather, we conduct our original observations: Hiraiso solar observatory to monitor the solar activity (solar flare, coronal mass ejection, and so on), domestic ionosonde network, magnetometer HF radar observations in far-east Siberia, and south-east Asia low-latitude ionosonde network (SEALION). Real-time observation data to monitor solar and solar-wind activities are obtained through antennae at NICT from ACE and STEREO satellites. We have a middle-class super-computer (NEC SX-8R) to maintain real-time computer simulations for solar and solar-wind, magnetosphere and ionosphere. The three simulations are directly or indirectly connected each other based on real-time observa-tion data to reproduce a virtual geo-space region on the super-computer. Informatics is a new methodology to make precise forecast of space weather. Based on new information and communication technologies (ICT), it provides more information in both quality and quantity. At NICT, we have been developing a cloud-computing system named "space weather cloud" based on a high-speed network system (JGN2+). Huge-scale distributed storage (1PB), clus-ter computers, visualization systems and other resources are expected to derive new findings and services of space weather forecasting. The final goal of NICT space weather service is to predict near-future space weather conditions and disturbances which will be causes of satellite malfunctions, tele-communication problems, and error of GPS navigations. In the present talk, we introduce our recent activities on the space weather services and discuss how we are going to develop the services from the view points of space science and practical uses.

An Investigation of Multi-Satellite Stratospheric Measurements on Tropospheric Weather Predictions over Continental United States

NASA Astrophysics Data System (ADS)

Shao, Min

The troposphere and stratosphere are the two closest atmospheric layers to the Earth's surface. These two layers are separated by the so-called tropopause. On one hand, these two layers are largely distinguished, on the other hand, lots of evidences proved that connections are also existed between these two layers via various dynamical and chemical feedbacks. Both tropospheric and stratospheric waves can propagate through the tropopause and affect the down streams, despite the fact that this propagation of waves is relatively weaker than the internal interactions in both atmospheric layers. Major improvements have been made in numerical weather predictions (NWP) via data assimilation (DA) in the past 30 years. From optimal interpolation to variational methods and Kalman Filter, great improvements are also made in the development of DA technology. The availability of assimilating satellite radiance observation and the increasing amount of satellite measurements enabled the generation of better atmospheric initials for both global and regional NWP systems. The selection of DA schemes is critical for regional NWP systems. The performance of three major data assimilation (3D-Var, Hybrid, and EnKF) schemes on regional weather forecasts over the continental United States during winter and summer is investigated. Convergence rate in the variational methods can be slightly accelerated especially in summer by the inclusion of ensembles. When the regional model lid is set at 50-mb, larger improvements (10˜20%) in the initials are obtained over the tropopause and lower troposphere. Better forecast skills (˜10%) are obtained in all three DA schemes in summer. Among these three DA schemes, slightly better (˜1%) forecast skills are obtained in Hybrid configuration than 3D-Var. Overall better forecast skills are obtained in summer via EnKF scheme. An extra 22% skill in predicting summer surface pressure but 10% less skills in winter are given by EnKF when compared to 3D-Var. The different forecast skills obtained between variational methods and EnKF are mainly due to the opposite incremental features over ocean and mountainous regions and the inclusion of ensembles. Diurnal variations are observed in predictions. Variations in temperature and humidity are mainly produced by the one-time assimilation in a day and the variations in wind predictions are mainly come from model systematic errors. The assimilation of microwave and infrared satellite measurements alone is compared. Compared to microwave measurements, less than 1% extra performance skill is obtained over the tropopause when infrared measurements are assimilated alone. Large differences are observed in winter analysis when Hybrid scheme is applied. Compared to infrared measurements, an averaged extra 5% performance skill is obtained when microwave measurements are assimilated alone. Predictions made by microwave configuration (MW) shows an extra 3% forecast skill than infrared configuration (IR) at early forecasts. Major differences between MW and IR are located over the tropopause and lower troposphere. Extra 3% and 15% forecast skills for the tropopause wind and temperature are obtained by assimilating microwave measurements alone, respectively. Infrared measurements show slightly better forecast skills at lower troposphere at later forecast lead times. The impacts of the extended stratospheric layers by raising regional model lid from 50-mb to 10-mb and then to 1-mb and the assimilated stratospheric satellite measurements on tropospheric weather predictions are explored in the last section. An extra 10% performance skill over the initial tropopause is obtained by extending the model top to 1-mb. Significant improvements (15˜50%) in initials are obtained over tropopause and lower troposphere by assimilating stratospheric measurements. In the predictions, the stratospheric information can propagate through the tropopause layers and affect the lower troposphere after 2-3 days' propagation. The major improvements made by the extended stratospheric layers and measurements are located in the tropopause. An averaged extra 5% forecast skill is obtained by raising the model lid from 10-mb to 1-mb. An extra 7% forecast skill is obtained in the tropospheric humidity by assimilating stratospheric measurements. Significant improvements in the tropopause and tropospheric predictions are observed when multi-satellite stratospheric measurements extended to 1-mb are assimilated in regional NWP system. Major positive impacts on the tropospheric weather predictions are observed in the first 72-h forecast lead times due to the downward propagation of the microwave stratospheric measurements. A two-season comparison study shows that the assimilation of microwave stratospheric measurements extended to 1-mb will lead to an adjusted stratospheric temperature distribution which may related to an adjusted BDC. Small impacts on the tropospheric general circulations are also found. The tropospheric forecast skills are slightly improved in response to the stratospheric initial conditions and adjusted tropospheric general circulations. For the prediction of heavy precipitation events, an extra 14% forecast skill is obtained when the microwave stratospheric measurements extend to 1-mb are assimilated. The results obtained in this thesis indicate that the assimilation of satellite microwave measurements has the advantages for short-term regional weather forecast using ensemble related data assimilation scheme. Also, this thesis proposed that the assimilation of microwave stratospheric measurements extended to 1-mb can slightly improve the tropospheric weather forecast skills as a result of the tropospheric general circulations responded to the adjusted stratospheric initials.

Japanese 25-year reanalysis (JRA-25)

NASA Astrophysics Data System (ADS)

Ohkawara, Nozomu

2006-12-01

A long term global atmospheric reanalysis Japanese 25-year Reanalysis (JRA-25) which covers from 1979 to 2004 was completed using the Japan Meteorological Agency (JMA) numerical assimilation and forecast system. This is the first long term reanalysis undertaken in Asia. JMA's latest numerical assimilation system, and observational data collected as much as possible, were used in JRA-25 to generate a consistent and high quality reanalysis dataset to contribute to climate research and operational work. One purpose of JRA-25 is to enhance to a high quality the analysis in the Asian region. 6-hourly data assimilation cycles were performed and produced 6-hourly atmospheric analysis and forecast fields with various kinds of physical variables. The global model used in JRA-25 has a spectral resolution of T106 (equivalent to a horizontal grid size of around 120km) and 40 vertical layers with the top level at 0.4hPa. For observational data, a great deal of satellite data was used in addition to conventional surface and upper air data. Atmospheric Motion Vector (AMV) data retrieved from geostationary satellites, brightness temperature (TBB) data from TIROS Operational Vertical Sounder (TOVS), precipitable water retrieved from radiance of microwave radiometer from orbital satellites and some other satellite data were assimilated with 3-dimensional variational method (3DVAR). Many advantages have been found in the JRA-25 reanalysis. Firstly, forecast 6-hour global total precipitation in JRA-25 performs well, distribution and amount are properly represented both in space and time. JRA-25 has the best performance compared to other reanalysis with respect to time series of global precipitation over many years, with few unrealistic variations caused by degraded quality of satellite data due to volcanic eruptions. Secondly, JRA-25 is the first reanalysis which assimilated wind profiles surrounding tropical cyclones retrieved from historical best track information; tropical cyclones were analyzed correctly in all the global regions. Additionally, low-level cloud along the subtropical western coast of continents is forecast very accurately, and snow depth analysis is also good.

The Global Precipitation Mission

NASA Technical Reports Server (NTRS)

Braun, Scott; Kummerow, Christian

2000-01-01

The Global Precipitation Mission (GPM), expected to begin around 2006, is a follow-up to the Tropical Rainfall Measuring Mission (TRMM). Unlike TRMM, which primarily samples the tropics, GPM will sample both the tropics and mid-latitudes. The primary, or core, satellite will be a single, enhanced TRMM satellite that can quantify the 3-D spatial distributions of precipitation and its associated latent heat release. The core satellite will be complemented by a constellation of very small and inexpensive drones with passive microwave instruments that will sample the rainfall with sufficient frequency to be not only of climate interest, but also have local, short-term impacts by providing global rainfall coverage at approx. 3 h intervals. The data is expected to have substantial impact upon quantitative precipitation estimation/forecasting and data assimilation into global and mesoscale numerical models. Based upon previous studies of rainfall data assimilation, GPM is expected to lead to significant improvements in forecasts of extratropical and tropical cyclones. For example, GPM rainfall data can provide improved initialization of frontal systems over the Pacific and Atlantic Oceans. The purpose of this talk is to provide information about GPM to the USWRP (U.S. Weather Research Program) community and to discuss impacts on quantitative precipitation estimation/forecasting and data assimilation.

Tracking, sensing and predicting flood wave propagation using nomadic satellite communication systems and hydrodynamic models

NASA Astrophysics Data System (ADS)

Hostache, R.; Matgen, P.; Giustarini, L.; Tailliez, C.; Iffly, J.-F.

2011-11-01

The main objective of this study is to contribute to the development and the improvement of flood forecasting systems. Since hydrometric stations are often poorly distributed for monitoring the propagation of extreme flood waves, the study aims at evaluating the hydrometric value of the Global Navigation Satellite System (GNSS). Integrated with satellite telecommunication systems, drifting or anchored floaters equipped with navigation systems such as GPS and Galileo, enable the quasi-continuous measurement and near real-time transmission of water level and flow velocity data, from virtually any point in the world. The presented study investigates the effect of assimilating GNSS-derived water level and flow velocity measurements into hydraulic models in order to reduce the associated predictive uncertainty.

Initial Assessment of Cyclone Global Navigation Satellite System (CYGNSS) Observations

NASA Astrophysics Data System (ADS)

McKague, D. S.; Ruf, C. S.

2017-12-01

The NASA Cyclone Global Navigation Satellite System (CYNSS) mission provides high temporal resolution observations of cyclones from a constellation of eight low-Earth orbiting satellites. Using the relatively new technique of Global Navigation Satellite System reflectometry (GNSS-R), all-weather observations are possible, penetrating even deep convection within hurricane eye walls. The compact nature of the GNSS-R receivers permits the use of small satellites, which in turn enables the launch of a constellation of satellites from a single launch vehicle. Launched in December of 2016, the eight CYGNSS satellites provide 25 km resolution observations of mean square slope (surface roughness) and surface winds with a 2.8 hour median revisit time from 38 S to 38 N degrees latitude. In addition to the calibration and validation of CYGNSS sea state observations, the CYGNSS science team is assessing the ability of the mission to provide estimates of cyclone size, intensity, and integrated kinetic energy. With its all-weather ability and high temporal resolution, the CYGNSS mission will add significantly to our ability to monitor cyclone genesis and intensification and will significantly reduce uncertainties in our ability to estimate cyclone intensity, a key variable in predicting its destructive potential. Members of the CYGNSS Science Team are also assessing the assimilation of CYGNSS data into hurricane forecast models to determine the impact of the data on forecast skill, using the data to study extra-tropical cyclones, and looking at connections between tropical cyclones and global scale weather, including the global hydrologic cycle. This presentation will focus on the assessment of early on-orbit observations of cyclones with respect to these various applications.

Post Alpbach-summerschool project: CARRINGTON MISSION FOR CME DETECTION TO IMPROVE SPACE WEATHER FORECAST

NASA Astrophysics Data System (ADS)

Scheucher, Markus; Urbar, Jaroslav; Musset, Sophie; Andersson, Viktor; Gini, Francesco; Gorski, Jedrzej; Jüstel, Peter; Kiefer, René; Lee, Arrow; Meskers, Arjan; Miles, Oscar; Perakis, Nikolas; Rußwurm, Michael; Scully, Stephen; Seifert, Bernhard; Sorba, Arianna

2014-05-01

The effects of solar activity, especially Coronal Mass Ejections (CMEs), on Earth- and satellite-based systems are well-known and can cause major damage to space-dependent infrastructure. The main problem in current space weather forecasting is the inability to determine necessary forecast parameters of CMEs and Corotating Interaction Regions (CIRs) early enough to react. We present the design for a novel space mission consisting of two spacecraft that is aimed to perform stereoscopic measurements on Earth-directed CMEs and in-situ measurements of CIRs. The magnetic field orientation and structure of CMEs will be measured close to the Sun, using spectro-polarimetry. Geoeffectiveness will be derived by remote sensing the CMEs magnetic field at 0.64AU from the Sun, determining the full magnetic field vector of a CME. This will be achieved by the novel concept of measuring its polarising effects on spacecraft to spacecraft laser beams based upon heterodyne interferometry. Overall structure and trajectory of CMEs will also be monitored by heliospheric imagers and in-situ plasma instruments. To achieve the mission objectives, the orbit is heliocentric at 1AU with a separation angle from the Earth of ±50°. The operational mission lifetime is 6 years with a proposed 6 year extension. If implemented, Carrington will serve as a forecast system which will significantly improve the minimum forecast time for the fastest CMEs with 2000 km/s, from 13 minutes based on current L1 satellites, to around 3 hours.

USDA Foreign Agricultural Service overview for operational monitoring of current crop conditions and production forecasts.

NASA Astrophysics Data System (ADS)

Crutchfield, J.

2016-12-01

The presentation will discuss the current status of the International Production Assessment Division of the USDA ForeignAgricultural Service for operational monitoring and forecasting of current crop conditions, and anticipated productionchanges to produce monthly, multi-source consensus reports on global crop conditions including the use of Earthobservations (EO) from satellite and in situ sources.United States Department of Agriculture (USDA) Foreign Agricultural Service (FAS) International Production AssessmentDivision (IPAD) deals exclusively with global crop production forecasting and agricultural analysis in support of the USDAWorld Agricultural Outlook Board (WAOB) lockup process and contributions to the World Agricultural Supply DemandEstimates (WASE) report. Analysts are responsible for discrete regions or countries and conduct in-depth long-termresearch into national agricultural statistics, farming systems, climatic, environmental, and economic factors affectingcrop production. IPAD analysts become highly valued cross-commodity specialists over time, and are routinely soughtout for specialized analyses to support governmental studies. IPAD is responsible for grain, oilseed, and cotton analysison a global basis. IPAD is unique in the tools it uses to analyze crop conditions around the world, including customweather analysis software and databases, satellite imagery and value-added image interpretation products. It alsoincorporates all traditional agricultural intelligence resources into its forecasting program, to make the fullest use ofavailable information in its operational commodity forecasts and analysis. International travel and training play animportant role in learning about foreign agricultural production systems and in developing analyst knowledge andcapabilities.

Utilization of satellite data and regional scale numerical models in short range weather forecasting

NASA Technical Reports Server (NTRS)

Kreitzberg, C. W.

1985-01-01

Overwhelming evidence was developed in a number of studies of satellite data impact on numerical weather prediction that it is unrealistic to expect satellite temperature soundings to improve detailed regional numerical weather prediction. It is likely that satellite data over the United States would substantially impact mesoscale dynamical predictions if the effort were made to develop a composite moisture analysis system. The horizontal variability of moisture, most clearly depicited in images from satellite water vapor channels, would not be determined from conventional rawinsondes even if that network were increased by a doubling of both the number of sites and the time frequency.

A quantitative analysis of inter-island telephony traffic in the Pacific Basin Region (PBR)

NASA Technical Reports Server (NTRS)

Evans, D. D.; Arth, C. H.

1980-01-01

As part of NASA's continuing assessment of future communication satellite requirements, a study was conducted to quantitatively scope current and future telecommunication traffic demand in the South Pacific Archipelagos. This demand was then converted to equivalent satellite transponder capacities. Only inter-island telephony traffic for the Pacific Basin Region was included. The results show that if all this traffic were carried by a satellite system, one-third of a satellite transponder would be needed to satisfy the base-year (1976-1977) requirement and about two-thirds of a satellite transponder would be needed to satisfy the forecasted 1985 requirement.

Snow mapping from space platforms

NASA Technical Reports Server (NTRS)

Itten, K. I.

1980-01-01

The paper considers problems of optimum resolution, periodicity, and wavelength bands used for snow mapping. Analog and digital methods were used for application of satellite data; techniques were developed for producing steamflow forecasts, hydroelectric power generation regulation data, irrigation potentials, and information on the availability of drinking water supplies. Future systems will utilize improved spectral band selection, new spectral regions, higher repetition rates, and more rapid access to satellite data.

JPSS Proving Ground Activities with NASA's Short-term Prediction Research and Transition (SPoRT) Center

NASA Astrophysics Data System (ADS)

Schultz, L. A.; Smith, M. R.; Fuell, K.; Stano, G. T.; LeRoy, A.; Berndt, E.

2015-12-01

Instruments aboard the Joint Polar Satellite System (JPSS) series of satellites will provide imagery and other data sets relevant to operational weather forecasts. To prepare current and future weather forecasters in application of these data sets, Proving Ground activities have been established that demonstrate future JPSS capabilities through use of similar sensors aboard NASA's Terra and Aqua satellites, and the S-NPP mission. As part of these efforts, NASA's Short-term Prediction Research and Transition (SPoRT) Center in Huntsville, Alabama partners with near real-time providers of S-NPP products (e.g., NASA, UW/CIMSS, UAF/GINA, etc.) to demonstrate future capabilities of JPSS. This includes training materials and product distribution of multi-spectral false color composites of the visible, near-infrared, and infrared bands of MODIS and VIIRS. These are designed to highlight phenomena of interest to help forecasters digest the multispectral data provided by the VIIRS sensor. In addition, forecasters have been trained on the use of the VIIRS day-night band, which provides imagery of moonlit clouds, surface, and lights emitted by human activities. Hyperspectral information from the S-NPP/CrIS instrument provides thermodynamic profiles that aid in the detection of extremely cold air aloft, helping to map specific aviation hazards at high latitudes. Hyperspectral data also support the estimation of ozone concentration, which can highlight the presence of much drier stratospheric air, and map its interaction with mid-latitude or tropical cyclones to improve predictions of their strengthening or decay. Proving Ground activities are reviewed, including training materials and methods that have been provided to forecasters, and forecaster feedback on these products that has been acquired through formal, detailed assessment of their applicability to a given forecast threat or task. Future opportunities for collaborations around the delivery of training are proposed, along with other applications of multispectral data and derived, more quantitative products.

Analysis of Summer Thunderstorms in Central Alabama Using the NASA Land Information System

NASA Technical Reports Server (NTRS)

James, Robert; Case, Jonathan; Molthan, Andrew; Jedloved, Gary

2010-01-01

Forecasters have difficulty predicting "random" afternoon thunderstorms during the summer months. Differences in soil characteristics could be a contributing factor for storms. The NASA Land Information System (LIS) may assist forecasters in predicting summer convection by identifying boundaries in land characteristics. This project identified case dates during the summer of 2009 by analyzing synoptic weather maps, radar, and satellite data to look for weak atmospheric forcing and disorganized convective development. Boundaries in land characteristics that may have lead to convective initiation in central Alabama were then identified using LIS.

An Overview of Scientific and Space Weather Results from the Communication/Navigation Outage Forecasting System (C/NOFS) Mission

NASA Technical Reports Server (NTRS)

Pfaff, R.; de la Beaujardiere, O.; Hunton, D.; Heelis, R.; Earle, G.; Strauss, P.; Bernhardt, P.

2012-01-01

The Communication/Navigation Outage Forecasting System (C/NOFS) Mission of the Air Force Research Laboratory is described. C/NOFS science objectives may be organized into three categories: (1) to understand physical processes active in the background ionosphere and thermosphere in which plasma instabilities grow; (2) to identify mechanisms that trigger or quench the plasma irregularities responsible for signal degradation; and (3) to determine how the plasma irregularities affect the propagation of electromagnetic waves. The satellite was launched in April, 2008 into a low inclination (13 deg), elliptical (400 x 850 km) orbit. The satellite sensors measure the following parameters in situ: ambient and fluctuating electron densities, AC and DC electric and magnetic fields, ion drifts and large scale ion composition, ion and electron temperatures, and neutral winds. C/NOFS is also equipped with a GPS occultation receiver and a radio beacon. In addition to the satellite sensors, complementary ground-based measurements, theory, and advanced modeling techniques are also important parts of the mission. We report scientific and space weather highlights of the mission after nearly four years in orbit

SPoRT: Transitioning NASA and NOAA Experimental Data to the Operational Weather Community

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.

2013-01-01

Established in 2002 to demonstrate the weather and forecasting application of real-time EOS measurements, the NASA Short-term Prediction Research and Transition (SPoRT) program has grown to be an end-to-end research to operations activity focused on the use of advanced NASA modeling and data assimilation approaches, nowcasting techniques, and unique high-resolution multispectral data from EOS satellites to improve short-term weather forecasts on a regional and local scale. With the ever-broadening application of real-time high resolution satellite data from current EOS, Suomi NPP, and planned JPSS and GOES-R sensors to weather forecast problems, significant challenges arise in the acquisition, delivery, and integration of the new capabilities into the decision making process of the operational weather community. For polar orbiting sensors such as MODIS, AIRS, VIIRS, and CRiS, the use of direct broadcast ground stations is key to the real-time delivery of the data and derived products in a timely fashion. With the ABI on the geostationary GOES-R satellite, the data volumes will likely increase by a factor of 5-10 from current data streams. However, the high data volume and limited bandwidth of end user facilities presents a formidable obstacle to timely access to the data. This challenge can be addressed through the use of subsetting techniques, innovative web services, and the judicious selection of data formats. Many of these approaches have been implemented by SPoRT for the delivery of real-time products to NWS forecast offices and other weather entities. Once available in decision support systems like AWIPS II, these new data and products must be integrated into existing and new displays that allow for the integration of the data with existing operational products in these systems. SPoRT is leading the way in demonstrating this enhanced capability. This paper will highlight the ways SPoRT is overcoming many of the challenges presented by the enormous data volumes of current and future satellite systems to get unique high quality research data into the operational weather environment.

Challenges in Transitioning Research Data to Operations: The SPoRT Paradigm

NASA Technical Reports Server (NTRS)

Jedloved, Gary J.; Smith, Matt; McGrath, Kevin

2010-01-01

Established in 2002 to demonstrate the weather and forecasting application of real-time EOS measurements, the NASA Short-term Prediction Research and Transition (SPoRT) program has grown to be an end-to-end research to operations activity focused on the use of advanced NASA modeling and data assimilation approaches, nowcasting techniques, and unique high-resolution multispectral data from EOS satellites to improve short-term weather forecasts on a regional and local scale. With the ever-broadening application of real-time high resolution satellite data from current EOS and planned NPP, JPSS, and GOES-R sensors to weather forecast problems, significant challenges arise in the acquisition, delivery, and integration of the new capabilities into the decision making process of the operational weather community. For polar orbiting sensors such as MODIS, AIRS, VIIRS, and CRiS, the use of direct broadcast ground stations is key to the real-time delivery of the data and derived products in a timely fashion. With the ABI on the geostationary GOES-R satellite, the data volume will likely increase by a factor of 5- 10 from current data streams. However, the high data volume and limited bandwidth of end user facilities presents a formidable obstacle to timely access to the data. This challenge can be addressed through the use of subsetting techniques, innovative web services, and the judicious selection of data formats. Many of these approaches have been implemented by SPoRT for the delivery of real-time products to NWS forecast offices and other weather entities. Once available in decision support systems like AWIPS II, these new data and products must be integrated into existing and new displays that allow for the integration of the data with existing operational products in these systems. SPoRT is leading the way in demonstrating this enhanced capability. This paper will highlight the ways SPoRT is overcoming many of the challenges presented by the enormous data volumes of current and future satellite systems to get unique high quality research data into the operational weather environment.

NPP Satellite Launch

NASA Image and Video Library

2011-10-28

NASA Deputy Administrator Lori Garver, left, watches the launch of the National Polar-orbiting Operational Environmental Satellite System Preparatory Project (NPP) at the National Oceanic and Atmospheric Administration (NOAA) Satellite Operations Center on Friday, Oct. 28, 2011 in Suitland, Md. U.S Congresswoman Donna Edwards, D-Md., is seen next to Garver. NPP is a joint venture between NASA and NOAA, and is the nation's newest Earth-observing satellite, which will provide data on climate change science, allow for accurate weather forecasts and advance warning for severe weather. NPP was launched from Vandenberg Air Force Base in California. Photo Credit: (NASA/Carla Cioffi)

NPP Satellite Launch

NASA Image and Video Library

2011-10-28

Dr. Kathy Sullivan, center, Deputy Administrator of the National Oceanic and Atmospheric Administration (NOAA) and former NASA astronaut is interviewed by a local television network at NOAA's Satellite Operations Facility in Suitland, Md. after the successful launch of the National Polar-orbiting Operational Environmental Satellite System Preparatory Project (NPP) on Friday, Oct. 28, 2011. NPP is a joint venture between NASA and NOAA, and is the nation's newest Earth-observing satellite, which will provide data on climate change science, allow for accurate weather forecasts and advance warning for severe weather. NPP was launched from Vandenberg Air Force Base in California. Photo Credit: (NASA/Carla Cioffi)

Impact of Aquarius and SMAP Sea Surface Salinity Observations on Seasonal Predictions of the 2015 El Nino

NASA Technical Reports Server (NTRS)

Hackert, E.; Kovach, R.; Marshak, J.; Borovikov, A.; Molod, A.; Vernieres, G.

2018-01-01

We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO forecasts for the big 2015 El Nino event. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP (Soil Moisture Active Passive Mission), allows a unique opportunity to compare and contrast forecasts generated with the benefit of these two satellite SSS observation types. Four distinct experiments are presented that include 1) freely evolving model SSS (i.e. no satellite SSS), relaxation to 2) climatological SSS (i.e. WOA13 SSS), 3) Aquarius, and 4) SMAP initialization. Coupled hindcasts are then generated from these initial conditions for March 2015. These forecasts are then validated against observations and evaluated with respect to the observed El Nino development.

National Weather Service Forecast Office Guam Home

Science.gov Websites

National Alerts Text Current Conditions Observations Satellite Hydrology River & Lake AHPS Radar Imagery AAFB (Guam) AAFB (Guam) Dial up CONUS Radar Forecasts Activity Planner Guam Public Marine Aviation ; Weather Topics: Local Alerts, Current Conditions, Radar, Satellite, Climate, W-GUM.Webmaster@noaa.gov

Development of an Adaptable Display and Diagnostic System for the Evaluation of Tropical Cyclone Forecasts

NASA Astrophysics Data System (ADS)

Kucera, P. A.; Burek, T.; Halley-Gotway, J.

2015-12-01

NCAR's Joint Numerical Testbed Program (JNTP) focuses on the evaluation of experimental forecasts of tropical cyclones (TCs) with the goal of developing new research tools and diagnostic evaluation methods that can be transitioned to operations. Recent activities include the development of new TC forecast verification methods and the development of an adaptable TC display and diagnostic system. The next generation display and diagnostic system is being developed to support evaluation needs of the U.S. National Hurricane Center (NHC) and broader TC research community. The new hurricane display and diagnostic capabilities allow forecasters and research scientists to more deeply examine the performance of operational and experimental models. The system is built upon modern and flexible technology that includes OpenLayers Mapping tools that are platform independent. The forecast track and intensity along with associated observed track information are stored in an efficient MySQL database. The system provides easy-to-use interactive display system, and provides diagnostic tools to examine forecast track stratified by intensity. Consensus forecasts can be computed and displayed interactively. The system is designed to display information for both real-time and for historical TC cyclones. The display configurations are easily adaptable to meet the needs of the end-user preferences. Ongoing enhancements include improving capabilities for stratification and evaluation of historical best tracks, development and implementation of additional methods to stratify and compute consensus hurricane track and intensity forecasts, and improved graphical display tools. The display is also being enhanced to incorporate gridded forecast, satellite, and sea surface temperature fields. The presentation will provide an overview of the display and diagnostic system development and demonstration of the current capabilities.

Satellite-based Calibration of Heat Flux at the Ocean Surface

NASA Astrophysics Data System (ADS)

Barron, C. N.; Dastugue, J. M.; May, J. C.; Rowley, C. D.; Smith, S. R.; Spence, P. L.; Gremes-Cordero, S.

2016-02-01

Model forecasts of upper ocean heat content and variability on diurnal to daily scales are highly dependent on estimates of heat flux through the air-sea interface. Satellite remote sensing is applied to not only inform the initial ocean state but also to mitigate errors in surface heat flux and model representations affecting the distribution of heat in the upper ocean. Traditional assimilation of sea surface temperature (SST) observations re-centers ocean models at the start of each forecast cycle. Subsequent evolution depends on estimates of surface heat fluxes and upper-ocean processes over the forecast period. The COFFEE project (Calibration of Ocean Forcing with satellite Flux Estimates) endeavors to correct ocean forecast bias through a responsive error partition among surface heat flux and ocean dynamics sources. A suite of experiments in the southern California Current demonstrates a range of COFFEE capabilities, showing the impact on forecast error relative to a baseline three-dimensional variational (3DVAR) assimilation using Navy operational global or regional atmospheric forcing. COFFEE addresses satellite-calibration of surface fluxes to estimate surface error covariances and links these to the ocean interior. Experiment cases combine different levels of flux calibration with different assimilation alternatives. The cases may use the original fluxes, apply full satellite corrections during the forecast period, or extend hindcast corrections into the forecast period. Assimilation is either baseline 3DVAR or standard strong-constraint 4DVAR, with work proceeding to add a 4DVAR expanded to include a weak constraint treatment of the surface flux errors. Covariance of flux errors is estimated from the recent time series of forecast and calibrated flux terms. While the California Current examples are shown, the approach is equally applicable to other regions. These approaches within a 3DVAR application are anticipated to be useful for global and larger regional domains where a full 4DVAR methodology may be cost-prohibitive.

Impact of SMOS soil moisture data assimilation on NCEP-GFS forecasts

NASA Astrophysics Data System (ADS)

Zhan, X.; Zheng, W.; Meng, J.; Dong, J.; Ek, M.

2012-04-01

Soil moisture is one of the few critical land surface state variables that have long memory to impact the exchanges of water, energy and carbon between the land surface and atmosphere. Accurate information about soil moisture status is thus required for numerical weather, seasonal climate and hydrological forecast as well as for agricultural production forecasts, water management and many other water related economic or social activities. Since the successful launch of ESA's soil moisture ocean salinity (SMOS) mission in November 2009, about 2 years of soil moisture retrievals has been collected. SMOS is believed to be the currently best satellite sensors for soil moisture remote sensing. Therefore, it becomes interesting to examine how the collected SMOS soil moisture data are compared with other satellite-sensed soil moisture retrievals (such as NASA's Advanced Microwave Scanning Radiometer -AMSR-E and EUMETSAT's Advanced Scatterometer - ASCAT)), in situ soil moisture measurements, and how these data sets impact numerical weather prediction models such as the Global Forecast System of NOAA-NCEP. This study implements the Ensemble Kalman filter in GFS to assimilate the AMSR-E, ASCAT and SMOS soil moisture observations after a quantitative assessment of their error rate based on in situ measurements from ground networks around contiguous United States. in situ soil moisture measurements from ground networks (such as USDA Soil Climate Analysis network - SCAN and NOAA's U.S. Climate Reference Network -USCRN) are used to evaluate the GFS soil moisture simulations (analysis). The benefits and uncertainties of assimilating the satellite data products in GFS are examined by comparing the GFS forecasts of surface temperature and rainfall with and without the assimilations. From these examinations, the advantages of SMOS soil moisture data products over other satellite soil moisture data sets will be evaluated. The next step toward operationally assimilating soil moisture and other land observations into GFS will also be discussed.

Forecasting vegetation greenness with satellite and climate data

USGS Publications Warehouse

Ji, Lei; Peters, Albert J.

2004-01-01

A new and unique vegetation greenness forecast (VGF) model was designed to predict future vegetation conditions to three months through the use of current and historical climate data and satellite imagery. The VGF model is implemented through a seasonality-adjusted autoregressive distributed-lag function, based on our finding that the normalized difference vegetation index is highly correlated with lagged precipitation and temperature. Accurate forecasts were obtained from the VGF model in Nebraska grassland and cropland. The regression R2 values range from 0.97-0.80 for 2-12 week forecasts, with higher R2 associated with a shorter prediction. An important application would be to produce real-time forecasts of greenness images.

Economic benefits of improved meteorological forecasts - The construction industry

NASA Technical Reports Server (NTRS)

Bhattacharyya, R. K.; Greenberg, J. S.

1976-01-01

Estimates are made of the potential economic benefits accruing to particular industries from timely utilization of satellite-derived six-hour weather forecasts, and of economic penalties resulting from failure to utilize such forecasts in day-to-day planning. The cost estimate study is centered on the U.S. construction industry, with results simplified to yes/no 6-hr forecasts on thunderstorm activity and work/no work decisions. Effects of weather elements (thunderstorms, snow and sleet) on various construction operations are indicated. Potential dollar benefits for other industries, including air transportation and other forms of transportation, are diagrammed for comparison. Geosynchronous satellites such as STORMSAT, SEOS, and SMS/GOES are considered as sources of the forecast data.

Improving the Forecast Accuracy of an Ocean Observation and Prediction System by Adaptive Control of the Sensor Network

NASA Astrophysics Data System (ADS)

Talukder, A.; Panangadan, A. V.; Blumberg, A. F.; Herrington, T.; Georgas, N.

2008-12-01

The New York Harbor Observation and Prediction System (NYHOPS) is a real-time, estuarine and coastal ocean observing and modeling system for the New York Harbor and surrounding waters. Real-time measurements from in-situ mobile and stationary sensors in the NYHOPS networks are assimilated into marine forecasts in order to reduce the discrepancy with ground truth. The forecasts are obtained from the ECOMSED hydrodynamic model, a shallow water derivative of the Princeton Ocean Model. Currently, all sensors in the NYHOPS system are operated in a fixed mode with uniform sampling rates. This technology infusion effort demonstrates the use of Model Predictive Control (MPC) to autonomously adapt the operation of both mobile and stationary sensors in response to changing events that are -automatically detected from the ECOMSED forecasts. The controller focuses sensing resources on those regions that are expected to be impacted by the detected events. The MPC approach involves formulating the problem of calculating the optimal sensor parameters as a constrained multi-objective optimization problem. We have developed an objective function that takes into account the spatiotemporal relationship of the in-situ sensor locations and the locations of events detected by the model. Experiments in simulation were carried out using data collected during a freshwater flooding event. The location of the resulting freshwater plume was calculated from the corresponding model forecasts and was used by the MPC controller to derive control parameters for the sensing assets. The operational parameters that are controlled include the sampling rates of stationary sensors, paths of unmanned underwater vehicles (UUVs), and data transfer routes between sensors and the central modeling computer. The simulation experiments show that MPC-based sensor control reduces the RMS error in the forecast by a factor of 380% as compared to uniform sampling. The paths of multiple UUVs were simultaneously calculated such that measurements from on-board sensors would lead to maximal reduction in the forecast error after data assimilation. The MPC controller also reduces the consumption of system resources such as energy expended in sampling and wireless communication. The MPC-based control approach can be generalized to accept data from remote sensing satellites. This will enable in-situ sensors to be regulated using forecasts generated by assimilating local high resolution in-situ measurements with wide-area observations from remote sensing satellites.

Drought Monitoring and Forecasting: Experiences from the US and Africa

NASA Astrophysics Data System (ADS)

Sheffield, Justin; Chaney, Nate; Yuan, Xing; Wood, Eric

2013-04-01

Drought has important but very different consequences regionally due to differences in vulnerability. These differences derive from variations in exposure related to climate variability and change, sensitivity of local populations, and coping capacity at all levels. Managing the risk of drought impacts relies on a variety of measures to reduce vulnerability that includes forewarning of drought development through early-warning systems. Existing systems rely on a variety of observing systems from satellites to local observers, modeling tools, and data dissemination methods. They range from sophisticated state-of-the-art systems to simple ground reports. In some regions, systems are virtually non-existent due to limited national capacity. This talk describes our experiences in developing and implementing drought monitoring and seasonal forecast systems in the US and sub-Saharan Africa as contrasting examples of the scientific challenges and user needs in developing early warning systems. In particular, early warning can help improve livelihoods based on subsistence farming in sub-Saharan Africa; whist reduction of economic impacts is generally foremost in the US. For the US, our national drought monitoring and seasonal forecast system has been operational for over 8 years and provides near real-time updates on hydrological states at ~12km resolution and hydrological forecasts out to 9 months. Output from the system contributes to national assessments such as from the NOAA Climate Prediction Center (CPC) and the US National Drought Monitor (USDM). For sub-Saharan Africa, our experimental drought monitoring system was developed as a translation of the US system but presents generally greater challenges due to, for example, lack of ground data and unique user needs. The system provides near real-time updates based on hydrological modeling and satellite based precipitation estimates, and has recently been augmented by a seasonal forecast component. We discuss the differences in experiences in development and implementation between the two systems in terms of the scientific challenges and the utility of the systems to stakeholders, for whom the information must be relevant to local conditions and needs.

Equatorial scintillation and systems support

NASA Astrophysics Data System (ADS)

Groves, K. M.; Basu, S.; Weber, E. J.; Smitham, M.; Kuenzler, H.; Valladares, C. E.; Sheehan, R.; MacKenzie, E.; Secan, J. A.; Ning, P.; McNeill, W. J.; Moonan, D. W.; Kendra, M. J.

1997-09-01

The need to nowcast and forecast scintillation for the support of operational systems has been recently identified by the interagency National Space Weather Program. This issue is addressed in the present paper in the context of nighttime irregularities in the equatorial ionosphere that cause intense amplitude and phase scintillations of satellite signals in the VHF/UHF range of frequencies and impact satellite communication, Global Positioning System navigation, and radar systems. Multistation and multifrequency satellite scintillation observations have been used to show that even though equatorial scintillations vary in accordance with the solar cycle, the extreme day-to-day variability of unknown origin modulates the scintillation occurrence during all phases of the solar cycle. It is shown that although equatorial scintillation events often show correlation with magnetic activity, the major component of scintillation is observed during magnetically quiet periods. In view of the day-to-day variability of the occurrence and intensity of scintillating regions, their latitude extent, and their zonal motion, a regional specification and short-term forecast system based on real-time measurements has been developed. This system, named the Scintillation Network Decision Aid, consists of two latitudinally dispersed stations, each of which uses spaced antenna scintillation receiving systems to monitor 250-MHz transmissions from two longitudinally separated geostationary satellites. The scintillation index and zonal irregularity drift are processed on-line and are retrieved by a remote operator on the Internet. At the operator terminal the data are combined with an empirical plasma bubble model to generate three-dimensional maps of irregularity structures and two-dimensional outage maps for the region.

Relation between Ocean SST Dipoles and Downwind Continental Croplands Assessed for Early Management Using Satellite-based Photosynthesis Models

NASA Astrophysics Data System (ADS)

Kaneko, Daijiro

2015-04-01

Crop-monitoring systems with the unit of carbon-dioxide sequestration for environmental issues related to climate adaptation to global warming have been improved using satellite-based photosynthesis and meteorological conditions. Early management of crop status is desirable for grain production, stockbreeding, and bio-energy providing that the seasonal climate forecasting is sufficiently accurate. Incorrect seasonal forecasting of crop production can damage global social activities if the recognized conditions are unsatisfied. One cause of poor forecasting related to the atmospheric dynamics at the Earth surface, which reflect the energy budget through land surface, especially the oceans and atmosphere. Recognition of the relation between SST anomalies (e.g. ENSO, Atlantic Niño, Indian dipoles, and Ningaloo Niño) and crop production, as expressed precisely by photosynthesis or the sequestrated-carbon rate, is necessary to elucidate the mechanisms related to poor production. Solar radiation, surface air temperature, and water stress all directly affect grain vegetation photosynthesis. All affect stomata opening, which is related to the water balance or definition by the ratio of the Penman potential evaporation and actual transpiration. Regarding stomata, present data and reanalysis data give overestimated values of stomata opening because they are extended from wet models in forests rather than semi-arid regions commonly associated with wheat, maize, and soybean. This study applies a complementary model based on energy conservation for semi-arid zones instead of the conventional Penman-Monteith method. Partitioning of the integrated Net PSN enables precise estimation of crop yields by modifying the semi-closed stomata opening. Partitioning predicts production more accurately using the cropland distribution already classified using satellite data. Seasonal crop forecasting should include near-real-time monitoring using satellite-based process crop models to avoid social difficulties that can derive from uncertain seasonal predictions produced from long-term forecasting. Acknowledgement The author appreciates scientific discussions held with the application team of seasonal prediction at the Japan Agency for Marine-Earth Science and Technology. Key words: crop production, monitoring, forecasting, SST anomaly, remote sensing

Improving volcanic sulfur dioxide cloud dispersal forecasts by progressive assimilation of satellite observations

NASA Astrophysics Data System (ADS)

Boichu, Marie; Clarisse, Lieven; Khvorostyanov, Dmitry; Clerbaux, Cathy

2014-04-01

Forecasting the dispersal of volcanic clouds during an eruption is of primary importance, especially for ensuring aviation safety. As volcanic emissions are characterized by rapid variations of emission rate and height, the (generally) high level of uncertainty in the emission parameters represents a critical issue that limits the robustness of volcanic cloud dispersal forecasts. An inverse modeling scheme, combining satellite observations of the volcanic cloud with a regional chemistry-transport model, allows reconstructing this source term at high temporal resolution. We demonstrate here how a progressive assimilation of freshly acquired satellite observations, via such an inverse modeling procedure, allows for delivering robust sulfur dioxide (SO2) cloud dispersal forecasts during the eruption. This approach provides a computationally cheap estimate of the expected location and mass loading of volcanic clouds, including the identification of SO2-rich parts.

Assimilation of Satellite Sea Surface Salinity Fields: Validating Ocean Analyses and Identifying Errors in Surface Buoyancy Fluxes

NASA Astrophysics Data System (ADS)

Mehra, A.; Nadiga, S.; Bayler, E. J.; Behringer, D.

2014-12-01

Recently available satellite sea-surface salinity (SSS) fields provide an important new global data stream for assimilation into ocean forecast systems. In this study, we present results from assimilating satellite SSS fields from NASA's Aquarius mission into the National Oceanic and Atmospheric Administration's (NOAA) operational Modular Ocean Model version 4 (MOM4), the oceanic component of NOAA's operational seasonal-interannual Climate Forecast System (CFS). Experiments on the sensitivity of the ocean's overall state to different relaxation time periods were run to evaluate the importance of assimilating high-frequency (daily to mesoscale) and low-frequency (seasonal) SSS variability. Aquarius SSS data (Aquarius Data Processing System (ADPS) version 3.0), mapped daily fields at 1-degree spatial resolution, were used. Four model simulations were started from the same initial ocean condition and forced with NOAA's daily Climate Forecast System Reanalysis (CFSR) fluxes, using a relaxation technique to assimilate daily satellite sea surface temperature (SST) fields and selected SSS fields, where, except as noted, a 30-day relaxation period is used. The simulations are: (1) WOAMC, the reference case and similar to the operational setup, assimilating monthly climatological SSS from the 2009 NOAA World Ocean Atlas; (2) AQ_D, assimilating daily Aquarius SSS; (3) AQ_M, assimilating monthly Aquarius SSS; and (4) AQ_D10, assimilating daily Aquarius SSS, but using a 10-day relaxation period. The analysis focuses on the tropical Pacific Ocean, where the salinity dynamics are intense and dominated by El Niño interannual variability in the cold tongue region and by high-frequency precipitation events in the western Pacific warm pool region. To assess the robustness of results and conclusions, we also examine the results for the tropical Atlantic and Indian Oceans. Preliminary validation studies are conducted using observations, such as satellite sea-surface height (SSH) fields and in situ Argo buoy vertical profiles of temperature and salinity, to demonstrate that SSS data assimilation improves ocean state representation of the following variables: ocean heat content (0-300m), dynamic height (0-1000m), mixed-layer depth, sea surface heigh, and surface buoyancy fluxes.

Forecast of the United States telecommunications demand through the year 2000

NASA Astrophysics Data System (ADS)

Kratochvil, D.

1984-01-01

The telecommunications forecasts considered in the present investigation were developed in studies conducted by Kratochvil et al. (1983). The overall purpose of these studies was to forecast the potential U.S. domestic telecommunications demand for satellite-provided fixed communications voice, data, and video services through the year 2000, so that this information on service demand would be available to aid in NASA communications program planning. Aspects of forecasting methodology are discussed, taking into account forecasting activity flow, specific services and selected techniques, and an event/trend cross-impact model. Events, or market determinant factors, which are very likely to occur by 1995 and 2005, are presented in a table. It is found that the demand for telecommunications in general, and for satellite telecommunications in particular, will increase significantly between now and the year 2000. The required satellite capacity will surpass both the potential and actual capacities in the early 1990s, indicating a need for Ka-band at that time.

Concepts for on-board satellite image registration. Volume 3: Impact of VLSI/VHSIC on satellite on-board signal processing

NASA Technical Reports Server (NTRS)

Aanstoos, J. V.; Snyder, W. E.

1981-01-01

Anticipated major advances in integrated circuit technology in the near future are described as well as their impact on satellite onboard signal processing systems. Dramatic improvements in chip density, speed, power consumption, and system reliability are expected from very large scale integration. Improvements are expected from very large scale integration enable more intelligence to be placed on remote sensing platforms in space, meeting the goals of NASA's information adaptive system concept, a major component of the NASA End-to-End Data System program. A forecast of VLSI technological advances is presented, including a description of the Defense Department's very high speed integrated circuit program, a seven-year research and development effort.

Global crop production forecasting: An analysis of the data system problems and their solutions

NASA Technical Reports Server (NTRS)

Neiers, J.; Graf, H.

1978-01-01

Data related problems in the acquisition and use of satellite data necessary for operational forecasting of global crop production are considered for the purpose of establishing a measurable baseline. For data acquisition the world was divided into 37 crop regions in 22 countries. These regions represent approximately 95 percent of the total world production of the selected crops of interest, i.e., wheat, corn, soybeans, and rice. Targets were assigned to each region. Limited time periods during which data could be taken (windows) were assigned to each target. Each target was assigned to a cloud region. The DSDS was used to measure the success of obtaining data for each target during the specified windows for the regional cloud conditions and the specific alternatives being analyzed. The results of this study suggest several approaches for an operational system that will perform satisfactorily with two LANDSAT type satellites.

Interpretation of snowcover from satellite imagery for use in water supply forecasts in the Sierra Nevada

NASA Technical Reports Server (NTRS)

Brown, A. J.; Hannaford, J. F.

1975-01-01

The California ASVT test area is composed of two study areas; one in Northern California covering the Upper Sacramento and Feather River Basins, and the other covering the Southern Sierra Basins of the San Joaquin, Kings, Kaweah, Tule, and Kern Rivers. Experiences of reducing snowcover from satellite imagery; the accuracy of present water supply forecast schemes; and the potential advantages of introducing snowcover into the forecast procedures are described.

A case study of the sensitivity of forecast skill to data and data analysis techniques

NASA Technical Reports Server (NTRS)

Baker, W. E.; Atlas, R.; Halem, M.; Susskind, J.

1983-01-01

A series of experiments have been conducted to examine the sensitivity of forecast skill to various data and data analysis techniques for the 0000 GMT case of January 21, 1979. These include the individual components of the FGGE observing system, the temperatures obtained with different satellite retrieval methods, and the method of vertical interpolation between the mandatory pressure analysis levels and the model sigma levels. It is found that NESS TIROS-N infrared retrievals seriously degrade a rawinsonde-only analysis over land, resulting in a poorer forecast over North America. Less degradation in the 72-hr forecast skill at sea level and some improvement at 500 mb is noted, relative to the control with TIROS-N retrievals produced with a physical inversion method which utilizes a 6-hr forecast first guess. NESS VTPR oceanic retrievals lead to an improved forecast over North America when added to the control.

Use of satellite data in runoff forecasting in the heavily forested, cloud-covered Pacific Northwest. [Upper Snake, Boise, Dworshak, Libby and Hungry Horse River Basins

NASA Technical Reports Server (NTRS)

Dillard, J. P.; Orwig, C. F. (Principal Investigator)

1980-01-01

The author has identified the following significant results. Satellite-derived snow cover data improves forecasts of stream flow but not at a statistically significant amount and should not be used exclusively because of persistent cloud cover. Based upon reconstruction runs, satellite data can be used to augment snow-flight data in the Upper Snake, Boise, Dworshak, and Hungry Horse basins. Satellite data does not compare well with aerial snow-flight data in the Libby basin.

The 20/30 GHz satellite systems technology needs assessment

NASA Technical Reports Server (NTRS)

Stevens, G.; Wright, D.

1978-01-01

Rain attenuation in the 20/30 GHz bands, and the resultant impact on system user costs were estimated for a variety of satellite communication system concepts. Results of previous and current NASA Lewis contractual and in-house studies on system design are reported as well as market studies conducted to evaluate the concepts and test their relevancy against forecasted market needs. The 20/30 GHz bands appear attractive economically and, with certain technology, appear to offer a virtually unlimited spectrum resource. This attractiveness is especially relevant to high density trunking where there is sufficient traffic to justify dual-station site diversity.

NOAA-L

NASA Technical Reports Server (NTRS)

McCain, Harry G. (Technical Monitor)

2000-01-01

The National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) have jointly developed a valuable series of polar-orbiting Earth environmental observation satellites since 1978. These satellites provide global data to NOAA's short- and long-range weather forecasting systems. The system consists of two polar-orbiting satellites known as the Advanced Television Infrared Observation Satellites (TIROS-N) (ATN). Operating as a pair, these satellites ensure that environmental data, for any region of the Earth, is no more than six hours old. These polar-orbiting satellites have not only provided cost-effective data for very immediate and real needs but also for extensive climate and research programs. The weather data (including images seen on television news programs) has afforded both convenience and safety to viewers throughout the world. The satellites also support the SARSAT (Search and Rescue Satellite Aided Tracking) part of the COSPAS-SARSAT constellation. Russia provides the COSPAS (Russian for Space Systems for the Search of Vessels in Distress) satellites. The international COSPAS-SARSAT system provides for the detection and location of emergency beacons for ships, aircraft, and people in distress and has contributed to the saving of more than 10,000 lives since its inception in 1982.

NASA SPoRT JPSS PG Activities in Alaska

NASA Technical Reports Server (NTRS)

Berndt, Emily; Molthan, Andrew; Fuell, Kevin; McGrath, Kevin; Smith, Matt; LaFontaine, Frank; Leroy, Anita; White, Kris

2018-01-01

SPoRT (NASA's Short-term Prediction Research and Transition Center) has collaboratively worked with Alaska WFOs (Weather Forecast Offices) to introduce RGB (Red/Green/Blue false color image) imagery to prepare for NOAA-20 (National Oceanic and Atmospheric Administration, JPSS (Joint Polar Satellite System) series-20 satellite) VIIRS (Visible Infrared Imaging Radiometer Suite) and improve forecasting aviation-related hazards. Last R2O/O2R (Research-to-Operations/Operations-to-Research) steps include incorporating NOAA-20 VIIRS in RGB suite and fully transitioning client-side RGB processing to GINA (Geographic Information Network of Alaska) and Alaska Region. Alaska Region WFOs have been part of the successful R2O/O2R story to assess the use of NESDIS (National Environmental Satellite, Data, and Information Service) Snowfall Rate product in operations. SPoRT introduced passive microwave rain rate and IMERG (Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement)) (IMERG) to Alaska WFOs for use in radar-void areas and assessing flooding potential. SPoRT has been part of the multi-organization collaborative effort to introduce Gridded NUCAPS (NOAA Unique CrIS/ATMS (Crosstrack Infrared Sounder/Advanced Technology Microwave Sounder) Processing System) to the Anchorage CWSU (Center Weather Service Unit) to assess Cold Air Aloft events, [and as part of NOAA's PG (Product Generation) effort].

Preparing for Operational Use of High Priority Products from the Joint Polar Satellite System (JPSS) in Numerical Weather Prediction

NASA Astrophysics Data System (ADS)

Nandi, S.; Layns, A. L.; Goldberg, M.; Gambacorta, A.; Ling, Y.; Collard, A.; Grumbine, R. W.; Sapper, J.; Ignatov, A.; Yoe, J. G.

2017-12-01

This work describes end to end operational implementation of high priority products from National Oceanic and Atmospheric Administration's (NOAA) operational polar-orbiting satellite constellation, to include Suomi National Polar-orbiting Partnership (S-NPP) and the Joint Polar Satellite System series initial satellite (JPSS-1), into numerical weather prediction and earth systems models. Development and evaluation needed for the initial implementations of VIIRS Environmental Data Records (EDR) for Sea Surface Temperature ingestion in the Real-Time Global Sea Surface Temperature Analysis (RTG) and Polar Winds assimilated in the National Weather Service (NWS) Global Forecast System (GFS) is presented. These implementations ensure continuity of data in these models in the event of loss of legacy sensor data. Also discussed is accelerated operational implementation of Advanced Technology Microwave Sounder (ATMS) Temperature Data Records (TDR) and Cross-track Infrared Sounder (CrIS) Sensor Data Records, identified as Key Performance Parameters by the National Weather Service. Operational use of SNPP after 28 October, 2011 launch took more than one year due to the learning curve and development needed for full exploitation of new remote sensing capabilities. Today, ATMS and CrIS data positively impact weather forecast accuracy. For NOAA's JPSS initial satellite (JPSS-1), scheduled for launch in late 2017, we identify scope and timelines for pre-launch and post-launch activities needed to efficiently transition these capabilities into operations. As part of these alignment efforts, operational readiness for KPPs will be possible as soon as 90 days after launch. The schedule acceleration is possible because of the experience with S-NPP. NOAA operational polar-orbiting satellite constellation provides continuity and enhancement of earth systems observations out to 2036. Program best practices and lessons learned will inform future implementation for follow-on JPSS-3 and -4 missions ensuring benefits and enhancements during the system's design life.

Satellite freeze forecast system. System configuration definition manual

NASA Technical Reports Server (NTRS)

Martsolf, J. D. (Principal Investigator)

1983-01-01

Equipment listings, interconnection information, and a basic overview is given of the hardware interaction of the Ruskin HP-100 computer system. A block diagram is included of the SFFS system at the National Weather Service Office in Ruskin, Florida. The generation answer file used to create the RTE-IVB operating system currently resident in Ruskin HP-1000 computer system is also described.

Real-time Transmission and Distribution of NOAA Tail Doppler Radar Data and Other Data Products

NASA Astrophysics Data System (ADS)

Carswell, J.; Chang, P.; Robinson, D.; Gamache, J.; Hill, J.

2011-12-01

The NOAA WP-3D and G-IV aircraft have conducted and continue to conduct numerous research and operational measurement missions. However, typically only a fraction of the data collected aboard each flight is transmitted to the ground in near real-time utilizing low bandwidth satellite data links. The advancements in aircraft satellite phones have increased available bandwidth and reliability to a point where these systems can be utilized for near real-time data flow in support of decision making. A robust and flexible data delivery system has been developed by Remote Sensing Solutions with support from NOAA's National Environmental Satellite, Data and Information Service (NESDIS), Aircraft Operations Center (AOC) and Hurricane Forecast Improvement Project (HFIP). X-band Doppler/reflectivity measurements of tropical storms and cyclones collected from the NOAA WP-3D aircraft have been the most recent focus. Doppler measurements from volume backscatter precipitation profiles can provide critical observations of the horizontal winds as the precipitation advects with these winds. The data delivery system captures these profiles and send the radial Doppler profile observations to National Weather Service in near real-time over satellite communication data link. The design of this transmission system included features to enhance the reliability and robustness of the data flow from the P-3 aircraft to the end user. Routine real-time transmission, using this system, of the full resolution Tail Doppler Radar profile data to the ground and distribution to the NOAA's Hurricane Research Division for analysis and processing in support of initializing the operational HWRF model is planned. The end objective is to provide these Doppler profiles in a routine fashion to NWS and others in the forecasting community for operational utilization in support of hurricane forecasting and warning. Other data sources that are being collected and transmitted to the ground with this system for distribution in near real-time, include but are not limited to, the NOAA Lower Fuselage Radar reflectivity profiles, SFMR retrievals, flight level data, AXBT profiles and Imaging Wind and Rain Airborne Profiler data. The transmission and distribution of these data has a latency of only several seconds from initial acquisition on the aircraft to end users accessing the data through the Internet enabling end users to have a virtual seat on the aircraft and quick dissemination critical observations to the hurricane research, forecasting and modeling communities. In this presentation, the system capabilities and architecture will be described. Examples of the data products and data visualization tools (client applications) will be shown.

Assessing the Impact of Pre-gpm Microwave Precipitation Observations in the Goddard WRF Ensemble Data Assimilation System

NASA Technical Reports Server (NTRS)

Chambon, Philippe; Zhang, Sara Q.; Hou, Arthur Y.; Zupanski, Milija; Cheung, Samson

2013-01-01

The forthcoming Global Precipitation Measurement (GPM) Mission will provide next generation precipitation observations from a constellation of satellites. Since precipitation by nature has large variability and low predictability at cloud-resolving scales, the impact of precipitation data on the skills of mesoscale numerical weather prediction (NWP) is largely affected by the characterization of background and observation errors and the representation of nonlinear cloud/precipitation physics in an NWP data assimilation system. We present a data impact study on the assimilation of precipitation-affected microwave (MW) radiances from a pre-GPM satellite constellation using the Goddard WRF Ensemble Data Assimilation System (Goddard WRF-EDAS). A series of assimilation experiments are carried out in a Weather Research Forecast (WRF) model domain of 9 km resolution in western Europe. Sensitivities to observation error specifications, background error covariance estimated from ensemble forecasts with different ensemble sizes, and MW channel selections are examined through single-observation assimilation experiments. An empirical bias correction for precipitation-affected MW radiances is developed based on the statistics of radiance innovations in rainy areas. The data impact is assessed by full data assimilation cycling experiments for a storm event that occurred in France in September 2010. Results show that the assimilation of MW precipitation observations from a satellite constellation mimicking GPM has a positive impact on the accumulated rain forecasts verified with surface radar rain estimates. The case-study on a convective storm also reveals that the accuracy of ensemble-based background error covariance is limited by sampling errors and model errors such as precipitation displacement and unresolved convective scale instability.

Assessing High-Resolution Weather Research and Forecasting (WRF) Forecasts Using an Object-Based Diagnostic Evaluation

DTIC Science & Technology

2014-02-01

Operational Model Archive and Distribution System ( NOMADS ). The RTMA product was generated using a 2-D variational method to assimilate point weather...observations and satellite-derived measurements (National Weather Service, 2013). The products were downloaded using the NOMADS General Regularly...of the completed WRF run" read Start_Date echo $Start_Date echo " " echo "Enter 2- digit , zulu, observation hour (HH) for remapping" read oHH

An airborne meteorological data collection system using satellite relay (ASDAR)

NASA Technical Reports Server (NTRS)

Bagwell, J. W.; Lindow, B. G.

1978-01-01

The National Aeronautics and Space Administration (NASA) has developed an airborne data acquisition and communication system for the National Oceanic and Atmospheric Administration (NOAA). This system known as ASDAR, the Aircraft to Satellite Data Relay, consists of a microprocessor based controller, time clock, transmitter and antenna. Together they acquire meteorological and position information from existing aircraft systems on B-747 aircraft, convert and format these, and transmit them to the ground via the GOES meteorological satellite series. The development and application of the ASDAR system is described with emphasis on unique features. Performance to date is exceptional, providing horizon-to-horizon coverage of aircraft flights. The data collected is of high quality and is considered a valuable addition to the data base from which NOAA generates its weather forecasts.

Steps Towards an Operational Service Using Near Real-Time Altimeter Data

NASA Astrophysics Data System (ADS)

Ash, E. R.

2006-07-01

Thanks largely to modern computing power, numerical forecasts of w inds and waves over the oceans ar e ev er improving, offering greater accuracy and finer resolution in time and sp ace. Howev er, it is recognized that met-ocean models still have difficulty in accurately forecasting sever e w eather conditions, conditions that cause the most damag e and difficulty in mar itime operations. Ther efore a key requir emen t is to provid e improved information on sever e conditions. No individual measur emen t or prediction system is perfect. Offshore buoys provide a continuous long-ter m record of wind and wave conditions, but only at a limited numb er of sites. Satellite data offer all-weath er global cov erage, but with relatively infrequen t samp ling. Forecasts rely on imperf ect numerical schemes and the ab ility to manage a vast quantity of input data. Therefore the best system is one that integr ates information from all available sources, taking advantage of the benef its that each can offer. We report on an initiative supported by the European Space Agen cy (ESA) which investig ated how satellite data could be used to enhan ce systems to provide Near Real Time mon itor ing of met-ocean conditions.

Impact of Scatterometer Ocean Wind Vector Data on NOAA Operations

NASA Astrophysics Data System (ADS)

Jelenak, Z.; Chang, P.; Brennan, M. J.; Sienkiewicz, J. M.

2015-12-01

Near real-time measurements of ocean surface vector winds (OSVW), including both wind speed and direction from non-NOAA satellites, are being widely used in critical operational NOAA forecasting and warning activities. The scatterometer wind data data have had major operational impact in: a) determining wind warning areas for mid-latitude systems (gale, storm,hurricane force); b) determining tropical cyclone 34-knot and 50-knot wind radii. c) tracking the center location of tropical cyclones, including the initial identification of their formation. d) identifying and warning of extreme gap and jet wind events at all latitudes. e) identifying the current location of frontal systems and high and low pressure centers. f) improving coastal surf and swell forecasts Much has been learned about the importance and utility of satellite OSVW data in operational weather forecasting and warning by exploiting OSVW research satellites in near real-time. Since December 1999 when first data from QuikSCAT scatterometer became available in near real time NOAA operations have been benefiting from ASCAT scatterometer observations on MetOp-A and B, Indian OSCAT scatterometer on OceanSat-3 and lately NASA's RapidScat mission on International Space Station. With oceans comprising over 70 percent of the earth's surface, the impacts of these data have been tremendous in serving society's needs for weather and water information and in supporting the nation's commerce with information for safe, efficient, and environmentally sound transportation and coastal preparedness. The satellite OSVW experience that has been gained over the past decade by users in the operational weather community allows for realistic operational OSVW requirements to be properly stated for future missions. Successful model of transitioning research data into operation implemented by Ocean Winds Team in NOAA's NESDIS/STAR office and subsequent data impacts will be presented and discussed.

Implementation of a state of the art automated system for the production of cloud/water vapor motion winds from geostationary satellites

NASA Technical Reports Server (NTRS)

Velden, Christopher S.

1994-01-01

The thrust of the proposed effort under this contract is aimed at improving techniques to track water vapor data in sequences of imagery from geostationary satellites. In regards to this task, significant testing, evaluation, and progress was accomplished during this period. Sets of winds derived from Meteosat data were routinely produced during Atlantic hurricane events in the 1993 season. These wind sets were delivered via Internet in real time to the Hurricane Research Division in Miami for their evaluation in a track forecast model. For eighteen cases in which 72-hour forecasts were produced, thirteen resulted in track forecast improvements (some quite significant). In addition, quality-controlled Meteosat water vapor winds produced by NESDIS were validated against rawinsondes, yielding an 8 m/s RMS. This figure is comparable to upper-level cloud drift wind accuracies. Given the complementary horizontal coverage in cloud-free areas, we believe that water vapor vectors can supplement cloud-drift wind information to provide good full-disk coverage of the upper tropospheric flow. The impact of these winds on numerical analysis and forecasts will be tested in the next reporting period.

Total Electron Content forecast model over Australia

NASA Astrophysics Data System (ADS)

Bouya, Zahra; Terkildsen, Michael; Francis, Matthew

Ionospheric perturbations can cause serious propagation errors in modern radio systems such as Global Navigation Satellite Systems (GNSS). Forecasting ionospheric parameters is helpful to estimate potential degradation of the performance of these systems. Our purpose is to establish an Australian Regional Total Electron Content (TEC) forecast model at IPS. In this work we present an approach based on the combined use of the Principal Component Analysis (PCA) and Artificial Neural Network (ANN) to predict future TEC values. PCA is used to reduce the dimensionality of the original TEC data by mapping it into its eigen-space. In this process the top- 5 eigenvectors are chosen to reflect the directions of the maximum variability. An ANN approach was then used for the multicomponent prediction. We outline the design of the ANN model with its parameters. A number of activation functions along with different spectral ranges and different numbers of Principal Components (PCs) were tested to find the PCA-ANN models reaching the best results. Keywords: GNSS, Space Weather, Regional, Forecast, PCA, ANN.

GEARS: An Enterprise Architecture Based On Common Ground Services

NASA Astrophysics Data System (ADS)

Petersen, S.

2014-12-01

Earth observation satellites collect a broad variety of data used in applications that range from weather forecasting to climate monitoring. Within NOAA the National Environmental Satellite Data and Information Service (NESDIS) supports these applications by operating satellites in both geosynchronous and polar orbits. Traditionally NESDIS has acquired and operated its satellites as stand-alone systems with their own command and control, mission management, processing, and distribution systems. As the volume, velocity, veracity, and variety of sensor data and products produced by these systems continues to increase, NESDIS is migrating to a new concept of operation in which it will operate and sustain the ground infrastructure as an integrated Enterprise. Based on a series of common ground services, the Ground Enterprise Architecture System (GEARS) approach promises greater agility, flexibility, and efficiency at reduced cost. This talk describes the new architecture and associated development activities, and presents the results of initial efforts to improve product processing and distribution.

Use of EOS Data in AWIPS for Weather Forecasting

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Haines, Stephanie L.; Suggs, Ron J.; Bradshaw, Tom; Darden, Chris; Burks, Jason

2003-01-01

Operational weather forecasting relies heavily on real time data and modeling products for forecast preparation and dissemination of significant weather information to the public. The synthesis of this information (observations and model products) by the meteorologist is facilitated by a decision support system to display and integrate the information in a useful fashion. For the NWS this system is called Advanced Weather Interactive Processing System (AWIPS). Over the last few years NASA has launched a series of new Earth Observation Satellites (EOS) for climate monitoring that include several instruments that provide high-resolution measurements of atmospheric and surface features important for weather forecasting and analysis. The key to the utilization of these unique new measurements by the NWS is the real time integration of the EOS data into the AWIPS system. This is currently being done in the Huntsville and Birmingham NWS Forecast Offices under the NASA Short-term Prediction Research and Transition (SPORT) Program. This paper describes the use of near real time MODIS and AIRS data in AWIPS to improve the detection of clouds, moisture variations, atmospheric stability, and thermal signatures that can lead to significant weather development. The paper and the conference presentation will focus on several examples where MODIS and AIRS data have made a positive impact on forecast accuracy. The results of an assessment of the utility of these products for weather forecast improvement made at the Huntsville NWS Forecast Office will be presented.

Tropopause sharpening by data assimilation

NASA Astrophysics Data System (ADS)

Pilch Kedzierski, R.; Neef, L.; Matthes, K.

2016-08-01

Data assimilation was recently suggested to smooth out the sharp gradients that characterize the tropopause inversion layer (TIL) in systems that did not assimilate TIL-resolving observations. We investigate whether this effect is present in the ERA-Interim reanalysis and the European Centre for Medium-Range Weather Forecasts (ECMWF) operational forecast system (which assimilate high-resolution observations) by analyzing the 4D-Var increments and how the TIL is represented in their data assimilation systems. For comparison, we also diagnose the TIL from high-resolution GPS radio occultation temperature profiles from the COSMIC satellite mission, degraded to the same vertical resolution as ERA-Interim and ECMWF operational analyses. Our results show that more recent reanalysis and forecast systems improve the representation of the TIL, updating the earlier hypothesis. However, the TIL in ERA-Interim and ECMWF operational analyses is still weaker and farther away from the tropopause than GPS radio occultation observations of the same vertical resolution.

Ionospheric research for space weather service support

NASA Astrophysics Data System (ADS)

Stanislawska, Iwona; Gulyaeva, Tamara; Dziak-Jankowska, Beata

2016-07-01

Knowledge of the behavior of the ionosphere is very important for space weather services. A wide variety of ground based and satellite existing and future systems (communications, radar, surveillance, intelligence gathering, satellite operation, etc) is affected by the ionosphere. There are the needs for reliable and efficient support for such systems against natural hazard and minimalization of the risk failure. The joint research Project on the 'Ionospheric Weather' of IZMIRAN and SRC PAS is aimed to provide on-line the ionospheric parameters characterizing the space weather in the ionosphere. It is devoted to science, techniques and to more application oriented areas of ionospheric investigation in order to support space weather services. The studies based on data mining philosophy increasing the knowledge of ionospheric physical properties, modelling capabilities and gain applications of various procedures in ionospheric monitoring and forecasting were concerned. In the framework of the joint Project the novel techniques for data analysis, the original system of the ionospheric disturbance indices and their implementation for the ionosphere and the ionospheric radio wave propagation are developed since 1997. Data of ionosonde measurements and results of their forecasting for the ionospheric observatories network, the regional maps and global ionospheric maps of total electron content from the navigational satellite system (GNSS) observations, the global maps of the F2 layer peak parameters (foF2, hmF2) and W-index of the ionospheric variability are provided at the web pages of SRC PAS and IZMIRAN. The data processing systems include analysis and forecast of geomagnetic indices ap and kp and new eta index applied for the ionosphere forecasting. For the first time in the world the new products of the W-index maps analysis are provided in Catalogues of the ionospheric storms and sub-storms and their association with the global geomagnetic Dst storms is investigated. The products of the Project web sites at http://www.cbk.waw.pl/rwc and http://www.izmiran.ru/services/iweather are widely used in scientific investigations and numerous applications by the telecommunication and navigation operators and users whose number at the web sites is growing substantially from month to month.

GOES-S Liftoff

NASA Image and Video Library

2018-03-01

A United Launch Alliance Atlas V rocket lifts off from Space Launch Complex 41 at Cape Canaveral Air Force Station carrying the NOAA Geostationary Operational Environmental Satellite, or GOES-S. Liftoff was at 5:02 p.m. EST. GOES-S is the second satellite in a series of next-generation weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting.

Fishing Forecasts

NASA Technical Reports Server (NTRS)

1988-01-01

ROFFS stands for Roffer's Ocean Fishing Forecasting Service, Inc. Roffer combines satellite and computer technology with oceanographic information from several sources to produce frequently updated charts sometimes as often as 30 times a day showing clues to the location of marlin, sailfish, tuna, swordfish and a variety of other types. Also provides customized forecasts for racing boats and the shipping industry along with seasonal forecasts that allow the marine industry to formulate fishing strategies based on foreknowledge of the arrival and departure times of different fish. Roffs service exemplifies the potential for benefits to marine industries from satellite observations. Most notable results are reduced search time and substantial fuel savings.

Validation of Volcanic Ash Forecasting Performed by the Washington Volcanic Ash Advisory Center

NASA Astrophysics Data System (ADS)

Salemi, A.; Hanna, J.

2009-12-01

In support of NOAA’s mission to protect life and property, the Satellite Analysis Branch (SAB) uses satellite imagery to monitor volcanic eruptions and track volcanic ash. The Washington Volcanic Ash Advisory Center (VAAC) was established in late 1997 through an agreement with the International Civil Aviation Organization (ICAO). A volcanic ash advisory (VAA) is issued every 6 hours while an eruption is occurring. Information about the current location and height of the volcanic ash as well as any pertinent meteorological information is contained within the VAA. In addition, when ash is detected in satellite imagery, 6-, 12- and 18-hour forecasts of ash height and location are provided. This information is garnered from many sources including Meteorological Watch Offices (MWOs), pilot reports (PIREPs), model forecast winds, radiosondes and volcano observatories. The Washington VAAC has performed a validation of their 6, 12 and 18 hour airborne volcanic ash forecasts issued since October, 2007. The volcanic ash forecasts are viewed dichotomously (yes/no) with the frequency of yes and no events placed into a contingency table. A large variety of categorical statistics useful in describing forecast performance are then computed from the resulting contingency table.

Towards uncertainty estimates in global operational forecasts of trace gases in the Copernicus Atmosphere Monitoring System

NASA Astrophysics Data System (ADS)

Huijnen, V.; Bouarar, I.; Chabrillat, S. H.; Christophe, Y.; Thierno, D.; Karydis, V.; Marecal, V.; Pozzer, A.; Flemming, J.

2017-12-01

Operational atmospheric composition analyses and forecasts such as developed in the Copernicus Atmosphere Monitoring Service (CAMS) rely on modules describing emissions, chemical conversion, transport and removal processing, as well as data assimilation methods. The CAMS forecasts can be used to drive regional air quality models across the world. Critical analyses of uncertainties in any of these processes are continuously needed to advance the quality of such systems on a global scale, ranging from the surface up to the stratosphere. With regard to the atmospheric chemistry to describe the fate of trace gases, the operational system currently relies on a modified version of the CB05 chemistry scheme for the troposphere combined with the Cariolle scheme to describe stratospheric ozone, as integrated in ECMWF's Integrated Forecasting System (IFS). It is further constrained by assimilation of satellite observations of CO, O3 and NO2. As part of CAMS we have recently developed three fully independent schemes to describe the chemical conversion throughout the atmosphere. These parameterizations originate from parent model codes in MOZART, MOCAGE and a combination of TM5/BASCOE. In this contribution we evaluate the correspondence and elemental differences in the performance of the three schemes in an otherwise identical model configuration (excluding data-assimilation) against a large range of in-situ and satellite-based observations of ozone, CO, VOC's and chlorine-containing trace gases for both troposphere and stratosphere. This analysis aims to provide a measure of model uncertainty in the operational system for tracers that are not, or poorly, constrained by data assimilation. It aims also to provide guidance on the directions for further model improvement with regard to the chemical conversion module.

Operational Use of the AIRS Total Column Ozone Retrievals Along with the RGB Air Mass Product as Part of the GOES-R Proving Ground

NASA Technical Reports Server (NTRS)

Folmer, Michael; Zavodsky, Bradley; Molthan, Andrew

2012-01-01

The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Hydrometeorological Prediction Center (HPC) and Ocean Prediction Center (OPC) provide short-term and medium-range forecast guidance of heavy precipitation, strong winds, and other features often associated with mid-latitude cyclones over both land and ocean. As a result, detection of factors that lead to rapid cyclogenesis and high wind events is key to improving forecast skill. One phenomenon that has been identified with these events is the stratospheric intrusion that occurs near tropopause folds. This allows for deep mixing near the top of the atmosphere where dry air high in ozone concentrations and potential vorticity descends (sometimes rapidly) deep into the mid-troposphere. Observations from satellites can aid in detection of these stratospheric air intrusions (SAI) regions. Specifically, multispectral composite imagery assign a variety of satellite spectral bands to the red, green, and blue (RGB) color components of imagery pixels and result in color combinations that can assist in the detection of dry stratospheric air associated with PV advection, which in turn may alert forecasters to the possibility of a rapidly strengthening storm system. Single channel or RGB satellite imagery lacks quantitative information about atmospheric moisture unless the sampled brightness temperatures or other data are converted to estimates of moisture via a retrieval process. Thus, complementary satellite observations are needed to capture a complete picture of a developing storm system. Here, total column ozone retrievals derived from a hyperspectral sounder are used to confirm the extent and magnitude of SAIs. Total ozone is a good proxy for defining locations and intensity of SAIs and has been used in studies evaluating that phenomenon (e.g. Tian et al. 2007, Knox and Schmidt 2005). Steep gradients in values of total ozone seen by satellites have been linked to stratosphere-troposphere exchange (WMO, 1985).

Regional Model Nesting Within GFS Daily Forecasts Over West Africa

NASA Technical Reports Server (NTRS)

Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben

2010-01-01

The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger is shown.

Forecasting Space Weather-Induced GPS Performance Degradation Using Random Forest

NASA Astrophysics Data System (ADS)

Filjar, R.; Filic, M.; Milinkovic, F.

2017-12-01

Space weather and ionospheric dynamics have a profound effect on positioning performance of the Global Satellite Navigation System (GNSS). However, the quantification of that effect is still the subject of scientific activities around the world. In the latest contribution to the understanding of the space weather and ionospheric effects on satellite-based positioning performance, we conducted a study of several candidates for forecasting method for space weather-induced GPS positioning performance deterioration. First, a 5-days set of experimentally collected data was established, encompassing the space weather and ionospheric activity indices (including: the readings of the Sudden Ionospheric Disturbance (SID) monitors, components of geomagnetic field strength, global Kp index, Dst index, GPS-derived Total Electron Content (TEC) samples, standard deviation of TEC samples, and sunspot number) and observations of GPS positioning error components (northing, easting, and height positioning error) derived from the Adriatic Sea IGS reference stations' RINEX raw pseudorange files in quiet space weather periods. This data set was split into the training and test sub-sets. Then, a selected set of supervised machine learning methods based on Random Forest was applied to the experimentally collected data set in order to establish the appropriate regional (the Adriatic Sea) forecasting models for space weather-induced GPS positioning performance deterioration. The forecasting models were developed in the R/rattle statistical programming environment. The forecasting quality of the regional forecasting models developed was assessed, and the conclusions drawn on the advantages and shortcomings of the regional forecasting models for space weather-caused GNSS positioning performance deterioration.

Satellite Altimetry based River Forecasting of Transboundary Flow

NASA Astrophysics Data System (ADS)

Hossain, F.; Siddique-E-Akbor, A.; Lee, H.; Shum, C.; Biancamaria, S.

2012-12-01

Forecasting of this transboundary flow in downstream nations however remains notoriously difficult due to the lack of basin-wide in-situ hydrologic measurements or its real-time sharing among nations. In addition, human regulation of upstream flow through diversion projects and dams, make hydrologic models less effective for forecasting on their own. Using the Ganges-Brahmaputra (GB) basin as an example, this study assesses the feasibility of using JASON-2 satellite altimetry for forecasting such transboundary flow at locations further inside the downstream nation of Bangladesh by propagating forecasts derived from upstream (Indian) locations through a hydrodynamic river model. The 5-day forecast of river levels at upstream boundary points inside Bangladesh are used to initialize daily simulation of the hydrodynamic river model and yield the 5-day forecast river level further downstream inside Bangladesh. The forecast river levels are then compared with the 5-day-later "now cast" simulation by the river model based on in-situ river level at the upstream boundary points in Bangladesh. Future directions for satellite-based forecasting of flow are also briefly overviewed.round tracks or virtual stations of JASON-2 (J2) altimeter over the GB basin shown in yellow lines. The locations where the track crosses a river and used for deriving forecasting rating curves is shown with a circle and station number (magenta- Brahmaputra basin; blue - Ganges basin). Circles without a station number represent the broader view of sampling by JASON-2 if all the ground tracks on main stem rivers and neighboring tributaries of Ganges and Brahmaputra are considered.

Volcanic Ash Data Assimilation System for Atmospheric Transport Model

NASA Astrophysics Data System (ADS)

Ishii, K.; Shimbori, T.; Sato, E.; Tokumoto, T.; Hayashi, Y.; Hashimoto, A.

2017-12-01

The Japan Meteorological Agency (JMA) has two operations for volcanic ash forecasts, which are Volcanic Ash Fall Forecast (VAFF) and Volcanic Ash Advisory (VAA). In these operations, the forecasts are calculated by atmospheric transport models including the advection process, the turbulent diffusion process, the gravitational fall process and the deposition process (wet/dry). The initial distribution of volcanic ash in the models is the most important but uncertain factor. In operations, the model of Suzuki (1983) with many empirical assumptions is adopted to the initial distribution. This adversely affects the reconstruction of actual eruption plumes.We are developing a volcanic ash data assimilation system using weather radars and meteorological satellite observation, in order to improve the initial distribution of the atmospheric transport models. Our data assimilation system is based on the three-dimensional variational data assimilation method (3D-Var). Analysis variables are ash concentration and size distribution parameters which are mutually independent. The radar observation is expected to provide three-dimensional parameters such as ash concentration and parameters of ash particle size distribution. On the other hand, the satellite observation is anticipated to provide two-dimensional parameters of ash clouds such as mass loading, top height and particle effective radius. In this study, we estimate the thickness of ash clouds using vertical wind shear of JMA numerical weather prediction, and apply for the volcanic ash data assimilation system.

SONARC: A Sea Ice Monitoring and Forecasting System to Support Safe Operations and Navigation in Arctic Seas

NASA Astrophysics Data System (ADS)

Stephenson, S. R.; Babiker, M.; Sandven, S.; Muckenhuber, S.; Korosov, A.; Bobylev, L.; Vesman, A.; Mushta, A.; Demchev, D.; Volkov, V.; Smirnov, K.; Hamre, T.

2015-12-01

Sea ice monitoring and forecasting systems are important tools for minimizing accident risk and environmental impacts of Arctic maritime operations. Satellite data such as synthetic aperture radar (SAR), combined with atmosphere-ice-ocean forecasting models, navigation models and automatic identification system (AIS) transponder data from ships are essential components of such systems. Here we present first results from the SONARC project (project term: 2015-2017), an international multidisciplinary effort to develop novel and complementary ice monitoring and forecasting systems for vessels and offshore platforms in the Arctic. Automated classification methods (Zakhvatkina et al., 2012) are applied to Sentinel-1 dual-polarization SAR images from the Barents and Kara Sea region to identify ice types (e.g. multi-year ice, level first-year ice, deformed first-year ice, new/young ice, open water) and ridges. Short-term (1-3 days) ice drift forecasts are computed from SAR images using feature tracking and pattern tracking methods (Berg & Eriksson, 2014). Ice classification and drift forecast products are combined with ship positions based on AIS data from a selected period of 3-4 weeks to determine optimal vessel speed and routing in ice. Results illustrate the potential of high-resolution SAR data for near-real-time monitoring and forecasting of Arctic ice conditions. Over the next 3 years, SONARC findings will contribute new knowledge about sea ice in the Arctic while promoting safe and cost-effective shipping, domain awareness, resource management, and environmental protection.

Maintaining a Local Data Integration System in Support of Weather Forecast Operations

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian

2010-01-01

Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) have used a local data integration system (LDIS) as part of their forecast and warning operations. Each has benefited from 3-dimensional analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive and complete understanding of evolving fine-scale weather features. Recent efforts have been undertaken to update the LDIS through the formal tasking process of NASA's Applied Meteorology Unit. The goals include upgrading LDIS with the latest version of ADAS, incorporating new sources of observational data, and making adjustments to shell scripts written to govern the system. A series of scripts run a complete modeling system consisting of the preprocessing step, the main model integration, and the post-processing step. The preprocessing step prepares the terrain, surface characteristics data sets, and the objective analysis for model initialization. Data ingested through ADAS include (but are not limited to) Level II Weather Surveillance Radar- 1988 Doppler (WSR-88D) data from six Florida radars, Geostationary Operational Environmental Satellites (GOES) visible and infrared satellite imagery, surface and upper air observations throughout Florida from NOAA's Earth System Research Laboratory/Global Systems Division/Meteorological Assimilation Data Ingest System (MADIS), as well as the Kennedy Space Center ICape Canaveral Air Force Station wind tower network. The scripts provide NWS MLB and SMG with several options for setting a desirable runtime configuration of the LDIS to account for adjustments in grid spacing, domain location, choice of observational data sources, and selection of background model fields, among others. The utility of an improved LDIS will be demonstrated through postanalysis warm and cool season case studies that compare high-resolution model output with and without the ADAS analyses. Operationally, these upgrades will result in more accurate depictions of the current local environment to help with short-range weather forecasting applications, while also offering an improved initialization for local versions of the Weather Research and Forecasting model.

GOES-S Mission Science Briefing

NASA Image and Video Library

2018-02-27

In the Kennedy Space Center's Press Site auditorium, members of the media participate in a mission briefing on National Oceanic and Atmospheric Administration's, or NOAA's, Geostationary Operational Environmental Satellite, or GOES-S. Briefing participants from left are: Steve Cole of NASA Communications; Dan Lindsey, GOES-R senior scientific advisor for NOAA; Louis Uccellini, director of the National Weather Service for NOAA; Jim Roberts, a scientist with the Earth System Research Laboratory's Office of Atmospheric Research for NOAA; Kristin Calhoun, a research scientist with NOAA's National Severe Storms Laboratory, and George Morrow, deputy director of NASA's Goddard Space Flight Center in Greenbelt, Maryland. GOES-S is the second satellite in a series of next-generation NOAA weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting. GOES-S is slated to lift off at 5:02 p.m. EST on March 1, 2018 aboard a United Launch Alliance Atlas V rocket.

Development of a WRF-RTFDDA-based high-resolution hybrid data-assimilation and forecasting system toward to operation in the Middle East

NASA Astrophysics Data System (ADS)

Liu, Y.; Wu, W.; Zhang, Y.; Kucera, P. A.; Liu, Y.; Pan, L.

2012-12-01

Weather forecasting in the Middle East is challenging because of its complicated geographical nature including massive coastal area and heterogeneous land, and regional spare observational network. Strong air-land-sea interactions form multi-scale weather regimes in the area, which require a numerical weather prediction model capable of properly representing multi-scale atmospheric flow with appropriate initial conditions. The WRF-based Real-Time Four Dimensional Data Assimilation (RTFDDA) system is one of advanced multi-scale weather analysis and forecasting facilities developed at the Research Applications Laboratory (RAL) of NCAR. The forecasting system is applied for the Middle East with careful configuration. To overcome the limitation of the very sparsely available conventional observations in the region, we develop a hybrid data assimilation algorithm combining RTFDDA and WRF-3DVAR, which ingests remote sensing data from satellites and radar. This hybrid data assimilation blends Newtonian nudging FDDA and 3DVAR technology to effectively assimilate both conventional observations and remote sensing measurements and provide improved initial conditions for the forecasting system. For brevity, the forecasting system is called RTF3H (RTFDDA-3DVAR Hybrid). In this presentation, we will discuss the hybrid data assimilation algorithm, and its implementation, and the applications for high-impact weather events in the area. Sensitivity studies are conducted to understand the strength and limitations of this hybrid data assimilation algorithm.

International MODIS and AIRS Processing Package (IMAPP) Implementation of Infusion of Satellite Data into Environmental Applications-International (IDEA-I) for Air Quality Forecasts using Suomi-NPP, Terra and Aqua Aerosol Retrievals

NASA Astrophysics Data System (ADS)

Davies, J. E.; Strabala, K.; Pierce, R. B.; Huang, A.

2016-12-01

Fine mode aerosols play a significant role in public health through their impact on respiratory and cardiovascular disease. IDEA-I (Infusion of Satellite Data into Environmental Applications-International) is a real-time system for trajectory-based forecasts of aerosol dispersion that can assist in the prediction of poor air quality events. We released a direct broadcast version of IDEA-I for aerosol trajectory forecasts in June 2012 under the International MODIS and AIRS Processing Package (IMAPP). In January 2014 we updated this application with website software to display multi-satellite products. Now we have added VIIRS aerosols from Suomi National Polar-orbiting Partnership (S-NPP). IMAPP is a NASA-funded and freely-distributed software package developed at Space Science and Engineering Center of University of Wisconsin-Madison that has over 2,300 registered users worldwide. With IMAPP, any ground station capable of receiving direct broadcast from Terra or Aqua can produce calibrated and geolocated radiances and a suite of environmental products. These products include MODIS AOD required for IDEA-I. VIIRS AOD for IDEA-I can be generated by Community Satellite Processing Package (CSPP) VIIRS EDR Version 2.0 Software for Suomi NPP. CSPP is also developed and distributed by Space Science & Engineering Center. This presentation describes our updated IMAPP implementation of IDEA-I through an example of its operation in a region known for episodic poor air quality events.

Biomass burning source characterization requirements in air quality models with and without data assimilation: challenges and opportunities

NASA Astrophysics Data System (ADS)

Hyer, E. J.; Zhang, J. L.; Reid, J. S.; Curtis, C. A.; Westphal, D. L.

2007-12-01

Quantitative models of the transport and evolution of atmospheric pollution have graduated from the laboratory to become a part of the operational activity of forecast centers. Scientists studying the composition and variability of the atmosphere put great efforts into developing methods for accurately specifying sources of pollution, including natural and anthropogenic biomass burning. These methods must be adapted for use in operational contexts, which impose additional strictures on input data and methods. First, only input data sources available in near real-time are suitable for use in operational applications. Second, operational applications must make use of redundant data sources whenever possible. This is a shift in philosophy: in a research context, the most accurate and complete data set will be used, whereas in an operational context, the system must be designed with maximum redundancy. The goal in an operational context is to produce, to the extent possible, consistent and timely output, given sometimes inconsistent inputs. The Naval Aerosol Analysis and Prediction System (NAAPS), a global operational aerosol analysis and forecast system, recently began incorporating assimilation of satellite-derived aerosol optical depth. Assimilation of satellite AOD retrievals has dramatically improved aerosol analyses and forecasts from this system. The use of aerosol data assimilation also changes the strategy for improving the smoke source function. The absolute magnitude of emissions events can be refined through feedback from the data assimilation system, both in real- time operations and in post-processing analysis of data assimilation results. In terms of the aerosol source functions, the largest gains in model performance are now to be gained by reducing data latency and minimizing missed detections. In this presentation, recent model development work on the Fire Locating and Monitoring of Burning Emissions (FLAMBE) system that provides smoke aerosol boundary conditions for NAAPS is described, including redundant integration of multiple satellite platforms and development of feedback loops between the data assimilation system and smoke source.

Assimilating All-Sky GPM Microwave Imager(GMI) Radiance Data in NASA GEOS-5 System for Global Cloud and Precipitation Analyses

NASA Astrophysics Data System (ADS)

Kim, M. J.; Jin, J.; McCarty, W.; Todling, R.; Holdaway, D. R.; Gelaro, R.

2014-12-01

The NASA Global Modeling and Assimilation Office (GMAO) works to maximize the impact of satellite observations in the analysis and prediction of climate and weather through integrated Earth system modeling and data assimilation. To achieve this goal, the GMAO undertakes model and assimilation development, generates products to support NASA instrument teams and the NASA Earth science program. Currently Atmospheric Data Assimilation System (ADAS) in the Goddard Earth Observing System Model, Version 5(GEOS-5) system combines millions of observations and short-term forecasts to determine the best estimate, or analysis, of the instantaneous atmospheric state. However, ADAS has been geared towards utilization of observations in clear sky conditions and the majority of satellite channel data affected by clouds are discarded. Microwave imager data from satellites can be a significant source of information for clouds and precipitation but the data are presently underutilized, as only surface rain rates from the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) are assimilated with small weight assigned in the analysis process. As clouds and precipitation often occur in regions with high forecast sensitivity, improvements in the temperature, moisture, wind and cloud analysis of these regions are likely to contribute to significant gains in numerical weather prediction accuracy. This presentation is intended to give an overview of GMAO's recent progress in assimilating the all-sky GPM Microwave Imager (GMI) radiance data in GEOS-5 system. This includes development of various new components to assimilate cloud and precipitation affected data in addition to data in clear sky condition. New observation operators, quality controls, moisture control variables, observation and background error models, and a methodology to incorporate the linearlized moisture physics in the assimilation system are described. In addition preliminary results showing impacts of assimilating all-sky GMI data on GEOS-5 forecasts are discussed.

The Purpose of the Sensor Web

NASA Technical Reports Server (NTRS)

Schoeberl, Mark R.

2004-01-01

The Sensor Web concept emerged as the number of Earth Science Satellites began to increase in the recent years. The idea, part of a vision for the future of earth science, was that the sensor systems would be linked in an active way to provide improved forecast capability. This means that a system that is nearly autonomous would need to be developed to allow the satellites to re-target and deploy assets for particular phenomena or provide on board processing for real time data. This talk will describe several elements of the sensor web.

Combining Satellite Ocean Color Imagery and Circulation Modeling to Forecast Bio-Optical Properties: Comparison of Models and Advection Schemes

DTIC Science & Technology

2008-10-01

Director NCST E. R. Franchi , 7000 ^^M^4^k ro£— 4// 2^/s y Public Affairs (Unclassified/ Unlimited Only), Code 7030 4 Division, Code Author, Code...from the Navy Operational Global Atmospheric Prediction System (NOGAPS, Hogan and Rosmond, 1991) and assimilates data via the Navy Coupled Ocean...forecasts using Global , Atlantic, Gulf of Mexico, and northern Gulf of Mexico configurations of HYCOM. Proceedings, Ocean Optics XIX, Castelvecchio Pascoli

Evaluation of Satellite and Model Precipitation Products Over Turkey

NASA Astrophysics Data System (ADS)

Yilmaz, M. T.; Amjad, M.

2017-12-01

Satellite-based remote sensing, gauge stations, and models are the three major platforms to acquire precipitation dataset. Among them satellites and models have the advantage of retrieving spatially and temporally continuous and consistent datasets, while the uncertainty estimates of these retrievals are often required for many hydrological studies to understand the source and the magnitude of the uncertainty in hydrological response parameters. In this study, satellite and model precipitation data products are validated over various temporal scales (daily, 3-daily, 7-daily, 10-daily and monthly) using in-situ measured precipitation observations from a network of 733 gauges from all over the Turkey. Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 version 7 and European Center of Medium-Range Weather Forecast (ECMWF) model estimates (daily, 3-daily, 7-daily and 10-daily accumulated forecast) are used in this study. Retrievals are evaluated for their mean and standard deviation and their accuracies are evaluated via bias, root mean square error, error standard deviation and correlation coefficient statistics. Intensity vs frequency analysis and some contingency table statistics like percent correct, probability of detection, false alarm ratio and critical success index are determined using daily time-series. Both ECMWF forecasts and TRMM observations, on average, overestimate the precipitation compared to gauge estimates; wet biases are 10.26 mm/month and 8.65 mm/month, respectively for ECMWF and TRMM. RMSE values of ECMWF forecasts and TRMM estimates are 39.69 mm/month and 41.55 mm/month, respectively. Monthly correlations between Gauges-ECMWF, Gauges-TRMM and ECMWF-TRMM are 0.76, 0.73 and 0.81, respectively. The model and the satellite error statistics are further compared against the gauges error statistics based on inverse distance weighting (IWD) analysis. Both the model and satellite data have less IWD errors (14.72 mm/month and 10.75 mm/month, respectively) compared to gauges IWD error (21.58 mm/month). These results show that, on average, ECMWF forecast data have higher skill than TRMM observations. Overall, both ECMWF forecast data and TRMM observations show good potential for catchment scale hydrological analysis.

Japanese Global Precipitation Measurement (GPM) mission status and application of satellite-based global rainfall map

NASA Astrophysics Data System (ADS)

Kachi, Misako; Shimizu, Shuji; Kubota, Takuji; Yoshida, Naofumi; Oki, Riko; Kojima, Masahiro; Iguchi, Toshio; Nakamura, Kenji

2010-05-01

As accuracy of satellite precipitation estimates improves and observation frequency increases, application of those data to societal benefit areas, such as weather forecasts and flood predictions, is expected, in addition to research of precipitation climatology to analyze precipitation systems. There is, however, limitation on single satellite observation in coverage and frequency. Currently, the Global Precipitation Measurement (GPM) mission is scheduled under international collaboration to fulfill various user requirements that cannot be achieved by the single satellite, like the Tropical Rainfall Measurement Mission (TRMM). The GPM mission is an international mission to achieve high-accurate and high-frequent rainfall observation over a global area. GPM is composed of a TRMM-like non-sun-synchronous orbit satellite (GPM core satellite) and constellation of satellites carrying microwave radiometer instruments. The GPM core satellite carries the Dual-frequency Precipitation Radar (DPR), which is being developed by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT), and microwave radiometer provided by the National Aeronautics and Space Administration (NASA). Development of DPR instrument is in good progress for scheduled launch in 2013, and DPR Critical Design Review has completed in July - September 2009. Constellation satellites, which carry a microwave imager and/or sounder, are planned to be launched around 2013 by each partner agency for its own purpose, and will contribute to extending coverage and increasing frequency. JAXA's future mission, the Global Change Observation Mission (GCOM) - Water (GCOM-W) satellite will be one of constellation satellites. The first generation of GCOM-W satellite is scheduled to be launched in 2011, and it carries the Advanced Microwave Scanning Radiometer 2 (AMSR2), which is being developed based on the experience of the AMSR-E on EOS Aqua satellite. Collaboration with GCOM-W is not only limited to its participation to GPM constellation but also coordination in areas of algorithm development and validation in Japan. Generation of high-temporal and high-accurate global rainfall map is one of targets of the GPM mission. As a proto-type for GPM era, JAXA has developed and operates the Global Precipitation Map algorithm in near-real-time since October 2008, and hourly and 0.1-degree resolution binary data and images available at http://sharaku.eorc.jaxa.jp/GSMaP/ four hours after observation. The algorithms are based on outcomes from the Global Satellite Mapping for Precipitation (GSMaP) project, which was sponsored by the Japan Science and Technology Agency (JST) under the Core Research for Evolutional Science and Technology (CREST) framework between 2002 and 2007 (Okamoto et al., 2005; Aonashi et al., 2009; Ushio et al., 2009). Target of GSMaP project is to produce global rainfall maps that are highly accurate and in high temporal and spatial resolution through the development of rain rate retrieval algorithms based on reliable precipitation physical models by using several microwave radiometer data, and comprehensive use of precipitation radar and geostationary infrared imager data. Near-real-time GSMaP data is distributed via internet and utilized by end users. Purpose of data utilization by each user covers broad areas and in world wide; Science researches (model validation, data assimilation, typhoon study, etc.), weather forecast/service, flood warning and rain analysis over river basin, oceanographic condition forecast, agriculture, and education. Toward the GPM era, operational application should be further emphasized as well as science application. JAXA continues collaboration with hydrological communities to utilize satellite-based precipitation data as inputs to future flood prediction and warning system, as well as with meteorological agencies to proceed further data utilization in numerical weather prediction system and forecasts.

Impact of satellite-based data on FGGE general circulation statistics

NASA Technical Reports Server (NTRS)

Salstein, David A.; Rosen, Richard D.; Baker, Wayman E.; Kalnay, Eugenia

1987-01-01

The NASA Goddard Laboratory for Atmospheres (GLA) analysis/forecast system was run in two different parallel modes in order to evaluate the influence that data from satellites and other FGGE observation platforms can have on analyses of large scale circulation; in the first mode, data from all observation systems were used, while in the second only conventional upper air and surface reports were used. The GLA model was also integrated for the same period without insertion of any data; an independent objective analysis based only on rawinsonde and pilot balloon data is also performed. A small decrease in the vigor of the general circulation is noted to follow from the inclusion of satellite observations.

Al Roker Interview with NASA for GOES-R Mission

NASA Image and Video Library

2016-11-19

During the countdown for the launch of NOAA's Geostationary Operational Environmental Satellite, or GOES-R, Stephanie Martin of NASA Communications, right, interviews Al Roker, weather forecaster on NBC's "Today Show." GOES-R is the first satellite in a series of next-generation GOES satellites for NOAA, the National Oceanographic and Atmospheric Administration. It will launch to a geostationary orbit over the western hemisphere to provide images of storms and help meteorologists predict severe weather conditionals and develop long-range forecasts.

Al Roker Interview with NASA for GOES-R Mission

NASA Image and Video Library

2016-11-19

During the countdown for the launch of NOAA's Geostationary Operational Environmental Satellite, or GOES-R, Stephanie Martin of NASA Communications, left, interviews Al Roker, weather forecaster on NBC's "Today Show." GOES-R is the first satellite in a series of next-generation GOES satellites for NOAA, the National Oceanographic and Atmospheric Administration. It will launch to a geostationary orbit over the western hemisphere to provide images of storms and help meteorologists predict severe weather conditionals and develop long-range forecasts.

KSC-20180301-VP-CDC01_0001-GOES_S_Launch_Commentary-3182524

NASA Image and Video Library

2018-03-01

A United Launch Alliance Atlas V rocket lifts off from Space Launch Complex 41 at Cape Canaveral Air Force Station carrying the NOAA Geostationary Operational Environmental Satellite, or GOES-S. Liftoff was at 5:02 p.m. EST. GOES-S is the second satellite in a series of next-generation weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting.

Retrieval of spatially distributed hydrological properties from satellite observations for spatial evaluation of a national water resources model.

NASA Astrophysics Data System (ADS)

Mendiguren González, G.; Stisen, S.; Koch, J.

2016-12-01

The NASA Cyclone Global Navigation Satellite System (CYNSS) mission provides high temporal resolution observations of cyclones from a constellation of eight low-Earth orbiting satellites. Using the relatively new technique of Global Navigation Satellite System reflectometry (GNSS-R), all-weather observations are possible, penetrating even deep convection within hurricane eye walls. The compact nature of the GNSS-R receivers permits the use of small satellites, which in turn enables the launch of a constellation of satellites from a single launch vehicle. Launched in December of 2016, the eight CYGNSS satellites provide 25 km resolution observations of mean square slope (surface roughness) and surface winds with a 2.8 hour median revisit time from 38 S to 38 N degrees latitude. In addition to the calibration and validation of CYGNSS sea state observations, the CYGNSS science team is assessing the ability of the mission to provide estimates of cyclone size, intensity, and integrated kinetic energy. With its all-weather ability and high temporal resolution, the CYGNSS mission will add significantly to our ability to monitor cyclone genesis and intensification and will significantly reduce uncertainties in our ability to estimate cyclone intensity, a key variable in predicting its destructive potential. Members of the CYGNSS Science Team are also assessing the assimilation of CYGNSS data into hurricane forecast models to determine the impact of the data on forecast skill, using the data to study extra-tropical cyclones, and looking at connections between tropical cyclones and global scale weather, including the global hydrologic cycle. This presentation will focus on the assessment of early on-orbit observations of cyclones with respect to these various applications.

Rural land mobile radio market assessment and satellite and terrestrial system concepts

NASA Technical Reports Server (NTRS)

Stevenson, S. M.; Provencher, C. E.

1984-01-01

Market potential exists; the nature of the market in terms of service needs, usage characteristics, service requirements, and forecasting the demand to the year 2000; alternative system cncepts that show promise in addressing the identified needs, in a cost effective manner; and advanced technology requirements associated with these concepts are considered.

Toward an Integrated Solution to Mitigate the Impact of Volcanic Ash to Aviation

NASA Technical Reports Server (NTRS)

Murray, John J.; Dezitter, Fabien; Fairlie, T. Duncan; Krotkov, Nickolay; Lekki, John; Lindsay, Francis; Pavolonis, Mike; Pieri, David; Prata, Fred; Vernier, Jean-Paul

2015-01-01

The science community is making a concerted effort to improve the reliability of dispersion models for the forecasting of volcanic ash plumes. Toward this end, it has been observed that the assimilation of diverse, accurate and frequent surface, airborne and satellite observations of the source and distal ash plumes may hold the key. Various international research organizations and operational agencies make these observations using a variety of active and passive remote sensing systems and use them to initialize atmospheric trajectory and dispersion models. These observation systems range from surface LIDAR and ceilometers, to airborne radiometers and nephelometers, to satellite radiometers, multi-spectral imagers, LIDAR and UV-photometers. None of these systems alone is a panacea, however, their synergistic application holds great promise toward solving this complex problem. Additionally, the aeronautical and science communities are working to better understand the quantitative thresholds and tolerances of aviation systems to volcanic ash to better inform scientists of the accuracy requirements for dispersion model forecasts. A number of the most recent and promising efforts in all of these area are discussed in this presentation.

COOP 3D ARPA Experiment 109 National Center for Atmospheric Research

NASA Technical Reports Server (NTRS)

1998-01-01

Coupled atmospheric and hydrodynamic forecast models were executed on the supercomputing resources of the National Center for Atmospheric Research (NCAR) in Boulder, Colorado and the Ohio Supercomputing Center (OSC)in Columbus, Ohio. respectively. The interoperation of the forecast models on these geographically diverse, high performance Cray platforms required the transfer of large three dimensional data sets at very high information rates. High capacity, terrestrial fiber optic transmission system technologies were integrated with those of an experimental high speed communications satellite in Geosynchronous Earth Orbit (GEO) to test the integration of the two systems. Operation over a spacecraft in GEO orbit required modification of the standard configuration of legacy data communications protocols to facilitate their ability to perform efficiently in the changing environment characteristic of a hybrid network. The success of this performance tuning enabled the use of such an architecture to facilitate high data rate, fiber optic quality data communications between high performance systems not accessible to standard terrestrial fiber transmission systems. Thus obviating the performance degradation often found in contemporary earth/satellite hybrids.

Status of the NASA GMAO Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, Nikki C.; Errico, Ronald M.

2014-01-01

An Observing System Simulation Experiment (OSSE) is a pure modeling study used when actual observations are too expensive or difficult to obtain. OSSEs are valuable tools for determining the potential impact of new observing systems on numerical weather forecasts and for evaluation of data assimilation systems (DAS). An OSSE has been developed at the NASA Global Modeling and Assimilation Office (GMAO, Errico et al 2013). The GMAO OSSE uses a 13-month integration of the European Centre for Medium- Range Weather Forecasts 2005 operational model at T511/L91 resolution for the Nature Run (NR). Synthetic observations have been updated so that they are based on real observations during the summer of 2013. The emulated observation types include AMSU-A, MHS, IASI, AIRS, and HIRS4 radiance data, GPS-RO, and conventional types including aircraft, rawinsonde, profiler, surface, and satellite winds. The synthetic satellite wind observations are colocated with the NR cloud fields, and the rawinsondes are advected during ascent using the NR wind fields. Data counts for the synthetic observations are matched as closely as possible to real data counts, as shown in Figure 2. Errors are added to the synthetic observations to emulate representativeness and instrument errors. The synthetic errors are calibrated so that the statistics of observation innovation and analysis increments in the OSSE are similar to the same statistics for assimilation of real observations, in an iterative method described by Errico et al (2013). The standard deviations of observation minus forecast (xo-H(xb)) are compared for the OSSE and real data in Figure 3. The synthetic errors include both random, uncorrelated errors, and an additional correlated error component for some observational types. Vertically correlated errors are included for conventional sounding data and GPS-RO, and channel correlated errors are introduced to AIRS and IASI (Figure 4). HIRS, AMSU-A, and MHS have a component of horizontally correlated error. The forecast model used by the GMAO OSSE is the Goddard Earth Observing System Model, Version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) DAS. The model version has been updated to v. 5.13.3, corresponding to the current operational model. Forecasts are run on a cube-sphere grid with 180 points along each edge of the cube (approximately 0.5 degree horizontal resolution) with 72 vertical levels. The DAS is cycled at 6-hour intervals, with 240 hour forecasts launched daily at 0000 UTC. Evaluation of the forecasting skill for July and August is currently underway. Prior versions of the GMAO OSSE have been found to have greater forecasting skill than real world forecasts. It is anticipated that similar forecast skill will be found in the updated OSSE.

Magnetogram Forecast: An All-Clear Space Weather Forecasting System

NASA Technical Reports Server (NTRS)

Barghouty, Nasser; Falconer, David

2015-01-01

Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output

Development and Evaluation of a Gridded CrIS/ATMS Visualization for Operational Forecasting

NASA Astrophysics Data System (ADS)

Zavodsky, B.; Smith, N.; Dostalek, J.; Stevens, E.; Nelson, K.; Weisz, E.; Berndt, E.; Line, W.; Barnet, C.; Gambacorta, A.; Reale, A.; Hoese, D.

2016-12-01

Upper-air observations from radiosondes are limited in spatial coverage and are primarily launched only at synoptic times, potentially missing evolving air masses. For forecast challenges which require diagnosis of the three-dimensional extent of the atmosphere, these observations may not be enough for forecasters. Currently, forecasters rely on model output alongside the sparse network of radiosondes for characterizing the three-dimensional atmosphere. However, satellite information can help fill in the spatial and temporal gaps in radiosonde observations. In particular, temperature and moisture retrievals from the NOAA-Unique Combined Atmospheric Processing System (NUCAPS), which combines infrared soundings from the Cross-track Infrared Sounder (CrIS) with the Advanced Technology Microwave Sounder (ATMS) to retrieve profiles of temperature and moisture. NUCAPS retrievals are available in a wide swath of observations with approximately 45-km spatial resolution at nadir and a local Equator crossing time of 1:30 A.M./P.M. enabling three-dimensional observations at asynoptic times. For forecasters to make the best use of these observations, these satellite-based soundings must be displayed in the National Weather Service's decision support system, the Advanced Weather Interactive Processing System (AWIPS). NUCAPS profiles are currently available in AWIPS as point observations that can be displayed on Skew-T diagrams. This presentation discusses the development of a new visualization capability for NUCAPS within AWIPS that will allow the data to be viewed in gridded horizontal maps or as vertical cross sections, giving forecasters additional tools for diagnosing atmospheric features. Forecaster feedback and examples of operational applications from two testbed activities will be highlighted. First is a product evaluation at the Hazardous Weather Testbed for severe weather—such as high winds, large hail, tornadoes—where the vertical distribution of temperature and moisture ahead of frontal boundaries was assessed. Second, is a product evaluation with the Alaska Center Weather Service Unit for cold air aloft—where the detection of the three-dimension extent of exterior aircraft temperatures lower than -65°C (temperatures at which jet fuel may begin to freeze)—was assessed.

Towards the development of full-fledged forest fire information systems

NASA Astrophysics Data System (ADS)

Baetens, J.; De Baets, B.

2012-12-01

Throughout the last decades much efforts have been spent in obtaining an increased understanding of wildfire dynamics and the way it is influenced by prevailing environmental conditions and settings, such as temperature, humidity, topography, vegetation abundance, and so on, since such a profound apprehension is a prerequisite for achieving enhanced wildfire prevention measures, as well as for optimizing fire fighting and disaster management. Amongst other things, this pursuit has culminated in the deployment of wildfire information systems, such as the Canadian Wildfire Information System (CWFIS), the European Forest Fire Information System (EFFIS) and the United States Active Fire Mapping Program and Landscape Fire and Resource Management Planning Tools (LANDFIRE), which inform any interested stakeholder, be it a citizen or a government official, about the current fire risk, the extent and location of current fires, the inflammability of the vegetation, and so on. Taking into account the coverage of these systems, it should be clear that they strongly rely upon satellite imagery that is obtained from dedicated sensors, such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board of NASA's Terra and Aqua satellites and the Advanced Very High Resolution Radiometer (AVHRR) that is carried by NOAA satellites, or more general-purpose instruments on board of spacecrafts such as Landsat or SPOT. Yet, to this day the aforementioned information systems have not yet embraced the power of mathematical modeling in order to enable trustworthy forecasts of the spatio-temporal propagation of wildfires given their current extent, which would nonetheless be extremely useful for optimizing fire fighting and disaster management, taking appropriate preventive measures, and so on. The deployment of such full-fledged wildfire information systems requires a high-level integration of (real-time) satellite imagery, weather reports and forecasts, geographic information, and finally mathematical models that constitute a mathematization of the underlying environmental processes, and which are indispensable for attaining sound and trustworthy wildfire forecasts, just as their meteorological counterparts are exploited to yield meaningful weather forecasts. As a very first step towards the development of a full-fledged wildfire information system, we demonstrate how MODIS imagery, Anderson fuel maps and geographic information can be combined to achieve meaningful wildfire forecasts given the current extent of the considered wildfire. Such a high-level integration is illustrated for a wildfire that swept through a natural area in Arizona, United States, near the border with New Mexico, between days 148 and 166 of the year 2011. Taking into account the spatial discreteness of the exploited information, which follows from its storage in geographical information systems, we rely upon a spatially discrete mathematical model, i.e. a coupled-map lattice, for mimicking the spatio-temporal wildfire propagation that can be extended in a next stage. Since setting up a full-fledged wildfire information system requires a highly multidisciplinary approach in which foresters, mathematicians, computer scientists, physicists, ecologists and others need to be involved, we hope to stimulate the joint efforts in accomplishing this task by means of our contribution.

Demonstrating the Operational Value of Thermodynamic Hyperspectral Profiles in the Pre-Convective Environment

NASA Technical Reports Server (NTRS)

Kozlowski, Danielle; Zavodsky, Bradley T.; Jedlovec, Gary J.

2011-01-01

The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service (NWS) Weather Forecasting Offices (WFO). As a part of the transition to operations process, SPoRT attempts to identify possible limitations in satellite observations and provide operational forecasters a product that will result in the most impact on their forecasts. One operational forecast challenge that some NWS offices face, is forecasting convection in data-void regions such as large bodies of water. The Atmospheric Infrared Sounder (AIRS) is a sounding instrument aboard NASA's Aqua satellite that provides temperature and moisture profiles of the atmosphere. This paper will demonstrate an approach to assimilate AIRS profile data into a regional configuration of the WRF model using its three-dimensional variational (3DVAR) assimilation component to be used as a proxy for the individual profiles.

Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Walker, Gregory K.; Mahanama, Sarith P.; Reichle, Rolf H.

2013-01-01

Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, e.g., through satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale streamflow forecasts only when evaporation variance is significant relative to the precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface modeling system as a tool for addressing the science of hydrological prediction.

An operational earth resources satellite system: The LANDSAT follow-on program

NASA Technical Reports Server (NTRS)

Stroud, W. G.

1977-01-01

The LANDSATS 1 and 2 have demonstrated the role of remote sensing from satellite in research, development, and operational activities essential to the better management of our resources. Hundreds of agricultural, geological, hydrological, urban land use, and other investigations have raised the question of the development of an operational system providing continuous, timely data. The LANDSAT Follow-on Study addressed the economics, technological performance, and design of a system in transition from R and D to operations. Economic benefits were identified; and a complete system from sensors to the ultilization in forecasting crop production, oil and mineral exploration, and water resources management was designed.

Current Usage and Future Prospects of Multispectral (RGB) Satellite Imagery in Support of NWS Forecast Offices and National Centers

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Fuell, Kevin K.; Knaff, John; Lee, Thomas

2012-01-01

Current and future satellite sensors provide remotely sensed quantities from a variety of wavelengths ranging from the visible to the passive microwave, from both geostationary and low-Earth orbits. The NASA Short-term Prediction Research and Transition (SPoRT) Center has a long history of providing multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA s Terra and Aqua satellites in support of NWS forecast office activities. Products from MODIS have recently been extended to include a broader suite of multispectral imagery similar to those developed by EUMETSAT, based upon the spectral channel s available from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard METEOSAT-9. This broader suite includes products that discriminate between air mass types associated with synoptic-scale features, assists in the identification of dust, and improves upon paired channel difference detection of fog and low cloud events. Similarly, researchers at NOAA/NESDIS and CIRA have developed air mass discrimination capabilities using channels available from the current GOES Sounders. Other applications of multispectral composites include combinations of high and low frequency, horizontal and vertically polarized passive microwave brightness temperatures to discriminate tropical cyclone structures and other synoptic-scale features. Many of these capabilities have been transitioned for evaluation and operational use at NWS Weather Forecast Offices and National Centers through collaborations with SPoRT and CIRA. Future instruments will continue the availability of these products and also expand upon current capabilities. The Advanced Baseline Imager (ABI) on GOES-R will improve the spectral, spatial, and temporal resolution of our current geostationary capabilities, and the recent launch of the Suomi National Polar-Orbiting Partnership (S-NPP) carries instruments such as the Visible Infrared Imager Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), and the Advanced Technology Microwave Sounder (ATMS), which have unrivaled spectral and spatial resolution, as precursors to the JPSS era (i.e., the next generation of polar orbiting satellites). At the same time, new image manipulation and display capabilities are available within AWIPS II, the next generation of the NWS forecaster decision support system. This presentation will present a review of SPoRT, CIRA, and NRL collaborations regarding multispectral satellite imagery and articulate an integrated and collaborative path forward with Raytheon AWIPS II development staff for integrating current and future capabilities that support new satellite instrumentation and the AWIPS II decision support system.

Near-real-time Estimation and Forecast of Total Precipitable Water in Europe

NASA Astrophysics Data System (ADS)

Bartholy, J.; Kern, A.; Barcza, Z.; Pongracz, R.; Ihasz, I.; Kovacs, R.; Ferencz, C.

2013-12-01

Information about the amount and spatial distribution of atmospheric water vapor (or total precipitable water) is essential for understanding weather and the environment including the greenhouse effect, the climate system with its feedbacks and the hydrological cycle. Numerical weather prediction (NWP) models need accurate estimations of water vapor content to provide realistic forecasts including representation of clouds and precipitation. In the present study we introduce our research activity for the estimation and forecast of atmospheric water vapor in Central Europe using both observations and models. The Eötvös Loránd University (Hungary) operates a polar orbiting satellite receiving station in Budapest since 2002. This station receives Earth observation data from polar orbiting satellites including MODerate resolution Imaging Spectroradiometer (MODIS) Direct Broadcast (DB) data stream from satellites Terra and Aqua. The received DB MODIS data are automatically processed using freely distributed software packages. Using the IMAPP Level2 software total precipitable water is calculated operationally using two different methods. Quality of the TPW estimations is a crucial question for further application of the results, thus validation of the remotely sensed total precipitable water fields is presented using radiosonde data. In a current research project in Hungary we aim to compare different estimations of atmospheric water vapor content. Within the frame of the project we use a NWP model (DBCRAS; Direct Broadcast CIMSS Regional Assimilation System numerical weather prediction software developed by the University of Wisconsin, Madison) to forecast TPW. DBCRAS uses near real time Level2 products from the MODIS data processing chain. From the wide range of the derived Level2 products the MODIS TPW parameter found within the so-called mod07 results (Atmospheric Profiles Product) and the cloud top pressure and cloud effective emissivity parameters from the so-called mod06 results (Cloud Product) are assimilated twice a day (at 00 and 12 UTC) by DBCRAS. DBCRAS creates 72 hours long weather forecasts with 48 km horizontal resolution. DBCRAS is operational at the University since 2009 which means that by now sufficient data is available for the verification of the model. In the present study verification results for the DBCRAS total precipitable water forecasts are presented based on analysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Numerical indices are calculated to quantify the performance of DBCRAS. During a limited time period DBCRAS was also ran without assimilating MODIS products which means that there is possibility to quantify the effect of assimilating MODIS physical products on the quality of the forecasts. For this limited time period verification indices are compared to decide whether MODIS data improves forecast quality or not.

Assessment of Two Types of Observations (SATWND and GPSRO) for the Operational Global 4DVAR System

NASA Astrophysics Data System (ADS)

Leng, H.

2017-12-01

The performance of a data assimilation system is significantly dependent on the quality and quantity of observations assimilated. In these years, more and more satellite observations have been applied in many operational assimilation systems. In this paper, the assessment of satellite-derived winds (SATWND) and GPS radio occultation (GPSRO) bending angles has been performed using a range of diagnostics. The main positive impacts are made when satellite-derived cloud data (GOES cloud data and MODIS cloud data) is assimilated, but benefit is hardly obtained from GPSRO data in the Operational Global 4DVAR System. In a full system configuration, the assimilation of satellite-derived observations is globally beneficial on the analysis, and the benefit can be well propagated into the forecast. The assimilation of the GPSRO observations has a slightly positive impact in the Tropics, but is neutral in the Northern Hemisphere and in the Southern Hemisphere. To assess the synergies of satellite-derived observations with other types of observation, experiments assimilating satellite-derived data and AMSU-A and AMSU-B observations were run. The results show that the analysis increments structure is not modified when AMSU-A and AMSU-B observations are also assimilated. This suggests that the impact of satellite-derived observations is not limited by the large impact of satellite radiance observations.

Using Sensor Web Processes and Protocols to Assimilate Satellite Data into a Forecast Model

NASA Technical Reports Server (NTRS)

Goodman, H. Michael; Conover, Helen; Zavodsky, Bradley; Maskey, Manil; Jedlovec, Gary; Regner, Kathryn; Li, Xiang; Lu, Jessica; Botts, Mike; Berthiau, Gregoire

2008-01-01

The goal of the Sensor Management Applied Research Technologies (SMART) On-Demand Modeling project is to develop and demonstrate the readiness of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) capabilities to integrate both space-based Earth observations and forecast model output into new data acquisition and assimilation strategies. The project is developing sensor web-enabled processing plans to assimilate Atmospheric Infrared Sounding (AIRS) satellite temperature and moisture retrievals into a regional Weather Research and Forecast (WRF) model over the southeastern United States.

Impact of improved information on the structure of world grain trade. [wheat

NASA Technical Reports Server (NTRS)

1979-01-01

The benefits to be derived by the United States from improvements in global grain crop forecasting capability are discussed. The improvements in forecasting accuracy, which are a result of the use of satellite technology in conjunction with existing ground based estimating procedures are described. The degree of forecasting accuracy to be obtained from satellite technology is also examined. Specific emphasis is placed on wheat production in seven countries/regions: the United States; Canada; Argentina; Australia; Western Europe; the USSR; and all other countries in a group.

Tethered Satellites as an Enabling Platform for Operational Space Weather Monitoring Systems

NASA Technical Reports Server (NTRS)

Gilchrist, Brian E.; Krause, Linda Habash; Gallagher, Dennis Lee; Bilen, Sven Gunnar; Fuhrhop, Keith; Hoegy, Walt R.; Inderesan, Rohini; Johnson, Charles; Owens, Jerry Keith; Powers, Joseph;

Future satellite systems - Market demand assessment

NASA Technical Reports Server (NTRS)

Reiner, P. S.

1981-01-01

During 1979-80, a market study was performed regarding the future total demand for communications services, and satellite transmission service at the 4/6 GHz, 12/14 GHz, and 20/30 GHz frequencies. Included in the study were a variety of communications traffic characteristics as well as projections of the cost of C and Ku band satellite systems through the year 2000. In connection with the considered study, a total of 15 major study tasks and subtasks were undertaken and were all interrelated in various ways. The telecommunications service forecasts were concerned with a total of 21 data services, 5 voice services, and 5 video services. The traffic volumes within the U.S. for the three basic services were projected for three time periods. It is found that the fixed frequency allocation for domestic satellites combined with potential interference from adjacent satellites means a near term lack of orbital positions above the U.S.

An online tool for Operational Probabilistic Drought Forecasting System (OPDFS): a Statistical-Dynamical Framework

NASA Astrophysics Data System (ADS)

Zarekarizi, M.; Moradkhani, H.; Yan, H.

2017-12-01

The Operational Probabilistic Drought Forecasting System (OPDFS) is an online tool recently developed at Portland State University for operational agricultural drought forecasting. This is an integrated statistical-dynamical framework issuing probabilistic drought forecasts monthly for the lead times of 1, 2, and 3 months. The statistical drought forecasting method utilizes copula functions in order to condition the future soil moisture values on the antecedent states. Due to stochastic nature of land surface properties, the antecedent soil moisture states are uncertain; therefore, data assimilation system based on Particle Filtering (PF) is employed to quantify the uncertainties associated with the initial condition of the land state, i.e. soil moisture. PF assimilates the satellite soil moisture data to Variable Infiltration Capacity (VIC) land surface model and ultimately updates the simulated soil moisture. The OPDFS builds on the NOAA's seasonal drought outlook by offering drought probabilities instead of qualitative ordinal categories and provides the user with the probability maps associated with a particular drought category. A retrospective assessment of the OPDFS showed that the forecasting of the 2012 Great Plains and 2014 California droughts were possible at least one month in advance. The OPDFS offers a timely assistance to water managers, stakeholders and decision-makers to develop resilience against uncertain upcoming droughts.

Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, Franklin R.; Bosilovich, Michael; Lyon, Bradfield; Funk, Chris

2013-01-01

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period

Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Roberts, J. Brent; Bosilovich, Michael; Lyon, Bradfield

2013-01-01

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.

Cross-impact study of foreign satellite communications on NASA's 30/20 GHz program

NASA Technical Reports Server (NTRS)

1980-01-01

A comprehensive traffic demand forecast and a scenario for the transition process from current satellite systems to more advanced systems of the 1990's are presented. Systems configurations with and without the use of 30/20 GHz are described and these two alternatives are compared. It is concluded that: (1) the use of 30/20 GHz will result in increased satellite capacity, which will be needed to satisfy demand; (2) the use of 30/20 GHz will decrease the transmission cost, especially for broadband communications; (3) in some areas, particularly Europe and Japan but also the U.S., 30/20 GHz is the only available frequency band for customer premise Earth stations because of the dense terrestrial microwave networks; and (4) the development of 30/20 GHz technology will improve U.S. markets for equipment and satellites in many world regions.

Cross-impact study of foreign satellite communications on NASA's 30/20 GHz program

NASA Astrophysics Data System (ADS)

1980-08-01

A comprehensive traffic demand forecast and a scenario for the transition process from current satellite systems to more advanced systems of the 1990's are presented. Systems configurations with and without the use of 30/20 GHz are described and these two alternatives are compared. It is concluded that: (1) the use of 30/20 GHz will result in increased satellite capacity, which will be needed to satisfy demand; (2) the use of 30/20 GHz will decrease the transmission cost, especially for broadband communications; (3) in some areas, particularly Europe and Japan but also the U.S., 30/20 GHz is the only available frequency band for customer premise Earth stations because of the dense terrestrial microwave networks; and (4) the development of 30/20 GHz technology will improve U.S. markets for equipment and satellites in many world regions.

Tracking Cloud Motion and Deformation for Short-Term Photovoltaic Power Forecasting

NASA Astrophysics Data System (ADS)

Good, Garrett; Siefert, Malte; Fritz, Rafael; Saint-Drenan, Yves-Marie; Dobschinski, Jan

2016-04-01

With the increasing role of photovoltaic power production, the need to accurately forecast and anticipate weather-driven elements like cloud cover has become ever more important. Of particular concern is forecasting on the short-term (up to several hours), for which the most recent full weather simulation may no longer provide the most accurate information in light of real-time satellite measurements. We discuss the application of the image correlation velocimetry technique described by Tokumaru & Dimotakis (1995) (for calculating flow fields from images) to measure deformations of various orders based on recent satellite imagery, with the goal of not only more accurately forecasting the advection of cloud structures, but their continued deformation as well.

An Experimental Real-Time Ocean Nowcast/Forecast System for Intra America Seas

NASA Astrophysics Data System (ADS)

Ko, D. S.; Preller, R. H.; Martin, P. J.

2003-04-01

An experimental real-time Ocean Nowcast/Forecast System has been developed for the Intra America Seas (IASNFS). The area of coverage includes the Caribbean Sea, the Gulf of Mexico and the Straits of Florida. The system produces nowcast and up to 72 hours forecast the sea level variation, 3D ocean current, temperature and salinity fields. IASNFS consists an 1/24 degree (~5 km), 41-level sigma-z data-assimilating ocean model based on NCOM. For daily nowcast/forecast the model is restarted from previous nowcast. Once model is restarted it continuously assimilates the synthetic temperature/salinity profiles generated by a data analysis model called MODAS to produce nowcast. Real-time data come from satellite altimeter (GFO, TOPEX/Poseidon, ERS-2) sea surface height anomaly and AVHRR sea surface temperature. Three hourly surface heat fluxes, including solar radiation, wind stresses and sea level air pressure from NOGAPS/FNMOC are applied for surface forcing. Forecasts are produced with available NOGAPS forecasts. Once the nowcast/forecast are produced they are distributed through the Internet via the updated web pages. The open boundary conditions including sea surface elevation, transport, temperature, salinity and currents are provided by the NRL 1/8 degree Global NCOM which is operated daily. An one way coupling scheme is used to ingest those boundary conditions into the IAS model. There are 41 rivers with monthly discharges included in the IASNFS.

Predictability of Solar Radiation for Photovoltaics systems over Europe: from short-term to seasonal time-scales

NASA Astrophysics Data System (ADS)

De Felice, Matteo; Petitta, Marcello; Ruti, Paolo

2014-05-01

Photovoltaic diffusion is steadily growing on Europe, passing from a capacity of almost 14 GWp in 2011 to 21.5 GWp in 2012 [1]. Having accurate forecast is needed for planning and operational purposes, with the possibility to model and predict solar variability at different time-scales. This study examines the predictability of daily surface solar radiation comparing ECMWF operational forecasts with CM-SAF satellite measurements on the Meteosat (MSG) full disk domain. Operational forecasts used are the IFS system up to 10 days and the System4 seasonal forecast up to three months. Forecast are analysed considering average and variance of errors, showing error maps and average on specific domains with respect to prediction lead times. In all the cases, forecasts are compared with predictions obtained using persistence and state-of-art time-series models. We can observe a wide range of errors, with the performance of forecasts dramatically affected by orography and season. Lower errors are on southern Italy and Spain, with errors on some areas consistently under 10% up to ten days during summer (JJA). Finally, we conclude the study with some insight on how to "translate" the error on solar radiation to error on solar power production using available production data from solar power plants. [1] EurObserver, "Baromètre Photovoltaïque, Le journal des énergies renouvables, April 2012."

The 30/20 GHz fixed communications systems service demand assessment. Volume 1: Executive summary

NASA Technical Reports Server (NTRS)

Gamble, R. B.; Seltzer, H. R.; Speter, K. M.; Westheimer, M.

1979-01-01

Demand for telecommunications services is forecasted for the period 1980-2000, with particular reference to that portion of the demand associated with satellite communications. Overall demand for telecommunications is predicted to increase by a factor of five over the period studied and the satellite portion of demand will increase even more rapidly. Traffic demand is separately estimated for voice, video, and data services and is also described as a function of distance traveled and city size. The satellite component of projected demand is compared with the capacity available in the C and Ku satellite bands and it is projected that new satellite technology and the implementation of Ka band transmission will be needed in the decade of the 1990's.

First SNPP Cal/Val Campaign: Satellite and Aircraft Sounding Retrieval Intercomparison

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Liu, Xu; Larar, Allen M.; Tian, Jialin; Smith, William L.; Wu, Wan; Kizer, Susan; Goldberg, Mitch; Liu, Q.

2015-01-01

Satellite ultraspectral infrared sensors provide key data records essential for weather forecasting and climate change science. The Suomi National Polar-orbiting Partnership (SNPP) satellite Environmental Data Record (EDR) is retrieved from calibrated ultraspectral radiance so called Sensor Data Record (SDR). It is critical to understand the accuracy of retrieved EDRs, which mainly depends on SDR accuracy (e.g., instrument random noise and absolute accuracy), an ill-posed retrieval system, and radiative transfer model errors. There are few approaches to validate EDR products, e.g., some common methods are to rely on radiosonde measurements, ground-based measurements, and dedicated aircraft campaign providing in-situ measurements of atmosphere and/or employing similar ultraspectral interferometer sounders. Ultraspectral interferometer sounder aboard aircraft measures SDR to retrieve EDR, which is often used to validate satellite measurements of SDR and EDR. The SNPP Calibration/Validation Campaign was conducted during May 2013. The NASA high-altitude aircraft ER-2 that carried ultraspectral interferometer sounders such as the NASA Atmospheric Sounder Testbed-Interferometer (NAST-I) flew under the SNPP satellite that carries the Cross-track Infrared Sounder (CrIS). Here we inter-compare the EDRs produced with different retrieval algorithms from SDRs measured by the sensors from satellite and aircraft. The available dropsonde and radiosonde measurements together with the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis were also used to draw the conclusion from this experiment.

Development of Radar-Satellite Blended QPF (Quantitative Precipitation Forecast) Technique for heavy rainfall

NASA Astrophysics Data System (ADS)

Jang, Sangmin; Yoon, Sunkwon; Rhee, Jinyoung; Park, Kyungwon

2016-04-01

Due to the recent extreme weather and climate change, a frequency and size of localized heavy rainfall increases and it may bring various hazards including sediment-related disasters, flooding and inundation. To prevent and mitigate damage from such disasters, very short range forecasting and nowcasting of precipitation amounts are very important. Weather radar data very useful in monitoring and forecasting because weather radar has high resolution in spatial and temporal. Generally, extrapolation based on the motion vector is the best method of precipitation forecasting using radar rainfall data for a time frame within a few hours from the present. However, there is a need for improvement due to the radar rainfall being less accurate than rain-gauge on surface. To improve the radar rainfall and to take advantage of the COMS (Communication, Ocean and Meteorological Satellite) data, a technique to blend the different data types for very short range forecasting purposes was developed in the present study. The motion vector of precipitation systems are estimated using 1.5km CAPPI (Constant Altitude Plan Position Indicator) reflectivity by pattern matching method, which indicates the systems' direction and speed of movement and blended radar-COMS rain field is used for initial data. Since the original horizontal resolution of COMS is 4 km while that of radar is about 1 km, spatial downscaling technique is used to downscale the COMS data from 4 to 1 km pixels in order to match with the radar data. The accuracies of rainfall forecasting data were verified utilizing AWS (Automatic Weather System) observed data for an extreme rainfall occurred in the southern part of Korean Peninsula on 25 August 2014. The results of this study will be used as input data for an urban stream real-time flood early warning system and a prediction model of landslide. Acknowledgement This research was supported by a grant (13SCIPS04) from Smart Civil Infrastructure Research Program funded by Ministry of Land, Infrastructure and Transport (MOLIT) of Korea government and Korea Agency for Infrastructure Technology Advancement (KAIA).

Presenting Critical Space Weather Information to Customers and Stakeholders (Invited)

NASA Astrophysics Data System (ADS)

Viereck, R. A.; Singer, H. J.; Murtagh, W. J.; Rutledge, B.

2013-12-01

Space weather involves changes in the near-Earth space environment that impact technological systems such as electric power, radio communication, satellite navigation (GPS), and satellite opeartions. As with terrestrial weather, there are several different kinds of space weather and each presents unique challenges to the impacted technologies and industries. But unlike terrestrial weather, many customers are not fully aware of space weather or how it impacts their systems. This issue is further complicated by the fact that the largest space weather events occur very infrequently with years going by without severe storms. Recent reports have estimated very large potential costs to the economy and to society if a geomagnetic storm were to cause major damage to the electric power transmission system. This issue has come to the attention of emergency managers and federal agencies including the office of the president. However, when considering space weather impacts, it is essential to also consider uncertainties in the frequency of events and the predicted impacts. The unique nature of space weather storms, the specialized technologies that are impacted by them, and the disparate groups and agencies that respond to space weather forecasts and alerts create many challenges to the task of communicating space weather information to the public. Many customers that receive forecasts and alerts are highly technical and knowledgeable about the subtleties of the space environment. Others know very little and require ongoing education and explanation about how a space weather storm will affect their systems. In addition, the current knowledge and understanding of the space environment that goes into forecasting storms is quite immature. It has only been within the last five years that physics-based models of the space environment have played important roles in predictions. Thus, the uncertainties in the forecasts are quite large. There is much that we don't know about space weather and this influences our forecasts. In this presentation, I will discuss the unique challenges that space weather forecasters face when explaining what we know and what we don't know about space weather events to customers and policy makers.

Measuring and forecasting great tsunamis by GNSS-based vertical positioning of multiple ships

NASA Astrophysics Data System (ADS)

Inazu, D.; Waseda, T.; Hibiya, T.; Ohta, Y.

2016-12-01

Vertical ship positioning by the Global Navigation Satellite System (GNSS) was investigated for measuring and forecasting great tsunamis. We first examined existing GNSS vertical position data of a navigating vessel. The result indicated that by using the kinematic Precise Point Positioning (PPP) method, tsunamis greater than 10^-1 m can be detected from the vertical position of the ship. Based on Automatic Identification System (AIS) data, tens of cargo ships and tankers are regularly identified navigating over the Nankai Trough, southwest of Japan. We then assumed that a future Nankai Trough great earthquake tsunami will be observed by ships at locations based on AIS data. The tsunami forecast capability by these virtual offshore tsunami measurements was examined. A conventional Green's function based inversion was used to determine the initial tsunami height distribution. Tsunami forecast tests over the Nankai Trough were carried out using simulated tsunami data of the vertical positions of multiple cargo ships/tankers on a certain day, and of the currently operating observations by deep-sea pressure gauges and Global Positioning System (GPS) buoys. The forecast capability of ship-based tsunami height measurements alone was shown to be comparable to or better than that using the existing offshore observations.

Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xuejun; Tang, Qiuhong; Liu, Xingcai

Real-time monitoring and predicting drought development with several months in advance is of critical importance for drought risk adaptation and mitigation. In this paper, we present a drought monitoring and seasonal forecasting framework based on the Variable Infiltration Capacity (VIC) hydrologic model over Southwest China (SW). The satellite precipitation data are used to force VIC model for near real-time estimate of land surface hydrologic conditions. As initialized with satellite-aided monitoring, the climate model-based forecast (CFSv2_VIC) and ensemble streamflow prediction (ESP)-based forecast (ESP_VIC) are both performed and evaluated through their ability in reproducing the evolution of the 2009/2010 severe drought overmore » SW. The results show that the satellite-aided monitoring is able to provide reasonable estimate of forecast initial conditions (ICs) in a real-time manner. Both of CFSv2_VIC and ESP_VIC exhibit comparable performance against the observation-based estimates for the first month, whereas the predictive skill largely drops beyond 1-month. Compared to ESP_VIC, CFSv2_VIC shows better performance as indicated by the smaller ensemble range. This study highlights the value of this operational framework in generating near real-time ICs and giving a reliable prediction with 1-month ahead, which has great implications for drought risk assessment, preparation and relief.« less

Application research for 4D technology in flood forecasting and evaluation

NASA Astrophysics Data System (ADS)

Li, Ziwei; Liu, Yutong; Cao, Hongjie

1998-08-01

In order to monitor the region which disaster flood happened frequently in China, satisfy the great need of province governments for high accuracy monitoring and evaluated data for disaster and improve the efficiency for repelling disaster, under the Ninth Five-year National Key Technologies Programme, the method was researched for flood forecasting and evaluation using satellite and aerial remoted sensed image and land monitor data. The effective and practicable flood forecasting and evaluation system was established and DongTing Lake was selected as the test site. Modern Digital photogrammetry, remote sensing and GIS technology was used in this system, the disastrous flood could be forecasted and loss can be evaluated base on '4D' (DEM -- Digital Elevation Model, DOQ -- Digital OrthophotoQuads, DRG -- Digital Raster Graph, DTI -- Digital Thematic Information) disaster background database. The technology of gathering and establishing method for '4D' disaster environment background database, application technology for flood forecasting and evaluation based on '4D' background data and experimental results for DongTing Lake test site were introduced in detail in this paper.

Multi-platform operational validation of the Western Mediterranean SOCIB forecasting system

NASA Astrophysics Data System (ADS)

Juza, Mélanie; Mourre, Baptiste; Renault, Lionel; Tintoré, Joaquin

2014-05-01

The development of science-based ocean forecasting systems at global, regional, and local scales can support a better management of the marine environment (maritime security, environmental and resources protection, maritime and commercial operations, tourism, ...). In this context, SOCIB (the Balearic Islands Coastal Observing and Forecasting System, www.socib.es) has developed an operational ocean forecasting system in the Western Mediterranean Sea (WMOP). WMOP uses a regional configuration of the Regional Ocean Modelling System (ROMS, Shchepetkin and McWilliams, 2005) nested in the larger scale Mediterranean Forecasting System (MFS) with a spatial resolution of 1.5-2km. WMOP aims at reproducing both the basin-scale ocean circulation and the mesoscale variability which is known to play a crucial role due to its strong interaction with the large scale circulation in this region. An operational validation system has been developed to systematically assess the model outputs at daily, monthly and seasonal time scales. Multi-platform observations are used for this validation, including satellite products (Sea Surface Temperature, Sea Level Anomaly), in situ measurements (from gliders, Argo floats, drifters and fixed moorings) and High-Frequency radar data. The validation procedures allow to monitor and certify the general realism of the daily production of the ocean forecasting system before its distribution to users. Additionally, different indicators (Sea Surface Temperature and Salinity, Eddy Kinetic Energy, Mixed Layer Depth, Heat Content, transports in key sections) are computed every day both at the basin-scale and in several sub-regions (Alboran Sea, Balearic Sea, Gulf of Lion). The daily forecasts, validation diagnostics and indicators from the operational model over the last months are available at www.socib.es.

Forecasting E > 50-MeV proton events with the proton prediction system (PPS)

NASA Astrophysics Data System (ADS)

Kahler, Stephen W.; White, Stephen M.; Ling, Alan G.

2017-11-01

Forecasting solar energetic (E > 10-MeV) particle (SEP) events is an important element of space weather. While several models have been developed for use in forecasting such events, satellite operations are particularly vulnerable to higher-energy (≥50-MeV) SEP events. Here we validate one model, the proton prediction system (PPS), which extends to that energy range. We first develop a data base of E ≥ 50-MeV proton events >1.0 proton flux units (pfu) events observed on the GOES satellite over the period 1986-2016. We modify the PPS to forecast proton events at the reduced level of 1 pfu and run PPS for four different solar input parameters: (1) all ≥M5 solar X-ray flares; (2) all ≥200 sfu 8800-MHz bursts with associated ≥M5 flares; (3) all ≥500 sfu 8800-MHz bursts; and (4) all ≥5000 sfu 8800-MHz bursts. The validation contingency tables and skill scores are calculated for all groups and used as a guide to use of the PPS. We plot the false alarms and missed events as functions of solar source longitude, and argue that the longitude-dependence employed by PPS does not match modern observations. Use of the radio fluxes as the PPS driver tends to result in too many false alarms at the 500 sfu threshold, and misses more events than the soft X-ray predictor at the 5000 sfu threshold.

Multidisciplinary studies of the social, economic and political impact resulting from recent advances in satellite meteorology. Volume 6: Executive summary. [technological forecasting spacecraft control/attitude (inclination) -classical mechanics

NASA Technical Reports Server (NTRS)

1975-01-01

An assessment of the technological impact of modern satellite weather forecasting for the United States is presented. Topics discussed are: (1) television broadcasting of weather; (2) agriculture (crop production); (3) water resources; (4) urban development; (5) recreation; and (6) transportation.

Forecasting for a Remote Island: A Class Exercise.

NASA Astrophysics Data System (ADS)

Riordan, Allen J.

2003-06-01

Students enrolled in a satellite meteorology course at North Carolina State University, Raleigh, recently had an unusual opportunity to apply their forecast skills to predict wind and weather conditions for a remote site in the Southern Hemisphere. For about 40 days starting in early February 2001, students used satellite and model guidance to develop forecasts to support a research team stationed on Bouvet Island (54°26S, 3°24E). Internet products together with current output from NCEP's Aviation (AVN) model supported the activity. Wind forecasts were of particular interest to the Bouvet team because violent winds often developed unexpectedly and posed a safety hazard.Results were encouraging in that 24-h wind speed forecasts showed reasonable reliability over a wide range of wind speeds. Forecasts for 48 h showed only marginal skill, however. Two critical events were well forecasted-the major February storm with wind speeds of over 120 kt and a brief calm period following several days of strong winds in early March. The latter forecast proved instrumental in recovering the research team.

Monitoring and Modeling: The Future of Volcanic Eruption Forecasting

NASA Astrophysics Data System (ADS)

Poland, M. P.; Pritchard, M. E.; Anderson, K. R.; Furtney, M.; Carn, S. A.

2016-12-01

Eruption forecasting typically uses monitoring data from geology, gas geochemistry, geodesy, and seismology, to assess the likelihood of future eruptive activity. Occasionally, months to years of warning are possible from specific indicators (e.g., deep LP earthquakes, elevated CO2 emissions, and aseismic deformation) or a buildup in one or more monitoring parameters. More often, observable changes in unrest occur immediately before eruption, as magma is rising toward the surface. In some cases, little or no detectable unrest precedes eruptive activity. Eruption forecasts are usually based on the experience of volcanologists studying the activity, but two developing fields offer a potential leap beyond this practice. First, remote sensing data, which can track thermal, gas, and ash emissions, as well as surface deformation, are increasingly available, allowing statistically significant research into the characteristics of unrest. For example, analysis of hundreds of volcanoes indicates that deformation is a more common pre-eruptive phenomenon than thermal anomalies, and that most episodes of satellite-detected unrest are not immediately followed by eruption. Such robust datasets inform the second development—probabilistic models of eruption potential, especially those that are based on physical-chemical models of the dynamics of magma accumulation and ascent. Both developments are essential for refining forecasts and reducing false positives. For example, many caldera systems have not erupted but are characterized by unrest that, in another context, would elicit strong concern from volcanologists. More observations of this behavior and better understanding of the underlying physics of unrest will improve forecasts of such activity. While still many years from implementation as a forecasting tool, probabilistic physio-chemical models incorporating satellite data offer a complement to expert assessments that, together, can form a powerful forecasting approach.

Jet Stream Analysis and Forecast Errors Using GADS Aircraft Observations in the DAO, ECMWF, and NCEP Models

NASA Technical Reports Server (NTRS)

Cardinali, Carla; Rukhovets, Leonid; Tenenbaum, Joel

2003-01-01

We have utilized an extensive set of independent British Airways flight data recording wind vector and temperature observations (the Global Aircraft Data Set [GADS] archive) in three ways: (a) as an independent check of operational analyses; (b) as an analysis observing system experiment (OSE) as if the GADS observations were available in real time; and (c) as the corresponding forecast simulation experiment applicable to future operational forecasts. Using a 31 day sample (0000 UTC 20 December 2000 through 0000 UTC 20 January 2000) from Winter 2000, we conclude that over the data-dense continental U. S. analyzed jet streaks are too weak by -2% to -5%. Over nearby data-sparse regions of Canada, analyzed jet streaks are too weak by -5% to -9%. The second range provides a limit on the accuracy of current jet streak analyses over the portions of the -85% of the earth's surface that are poorly covered by non-satellite observations. The -5% to -9% range is relevant for the pre-third generation satellite (AIRS, IASI, GIFTS) era.

Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa

NASA Astrophysics Data System (ADS)

Vogel, Peter; Knippertz, Peter; Fink, Andreas H.; Schlueter, Andreas; Gneiting, Tilmann

2018-04-01

Accumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, we analyze the performance of nine operational global ensemble prediction systems (EPSs) relative to climatology-based forecasts for 1 to 5-day accumulated precipitation based on the monsoon seasons 2007-2014 for three regions within northern tropical Africa. To assess the full potential of raw ensemble forecasts across spatial scales, we apply state-of-the-art statistical postprocessing methods in form of Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS), and verify against station and spatially aggregated, satellite-based gridded observations. Raw ensemble forecasts are uncalibrated, unreliable, and underperform relative to climatology, independently of region, accumulation time, monsoon season, and ensemble. Differences between raw ensemble and climatological forecasts are large, and partly stem from poor prediction for low precipitation amounts. BMA and EMOS postprocessed forecasts are calibrated, reliable, and strongly improve on the raw ensembles, but - somewhat disappointingly - typically do not outperform climatology. Most EPSs exhibit slight improvements over the period 2007-2014, but overall have little added value compared to climatology. We suspect that the parametrization of convection is a potential cause for the sobering lack of ensemble forecast skill in a region dominated by mesoscale convective systems.

Probabilistic rainfall warning system with an interactive user interface

NASA Astrophysics Data System (ADS)

Koistinen, Jarmo; Hohti, Harri; Kauhanen, Janne; Kilpinen, Juha; Kurki, Vesa; Lauri, Tuomo; Nurmi, Pertti; Rossi, Pekka; Jokelainen, Miikka; Heinonen, Mari; Fred, Tommi; Moisseev, Dmitri; Mäkelä, Antti

2013-04-01

A real time 24/7 automatic alert system is in operational use at the Finnish Meteorological Institute (FMI). It consists of gridded forecasts of the exceedance probabilities of rainfall class thresholds in the continuous lead time range of 1 hour to 5 days. Nowcasting up to six hours applies ensemble member extrapolations of weather radar measurements. With 2.8 GHz processors using 8 threads it takes about 20 seconds to generate 51 radar based ensemble members in a grid of 760 x 1226 points. Nowcasting exploits also lightning density and satellite based pseudo rainfall estimates. The latter ones utilize convective rain rate (CRR) estimate from Meteosat Second Generation. The extrapolation technique applies atmospheric motion vectors (AMV) originally developed for upper wind estimation with satellite images. Exceedance probabilities of four rainfall accumulation categories are computed for the future 1 h and 6 h periods and they are updated every 15 minutes. For longer forecasts exceedance probabilities are calculated for future 6 and 24 h periods during the next 4 days. From approximately 1 hour to 2 days Poor man's Ensemble Prediction System (PEPS) is used applying e.g. the high resolution short range Numerical Weather Prediction models HIRLAM and AROME. The longest forecasts apply EPS data from the European Centre for Medium Range Weather Forecasts (ECMWF). The blending of the ensemble sets from the various forecast sources is performed applying mixing of accumulations with equal exceedance probabilities. The blending system contains a real time adaptive estimator of the predictability of radar based extrapolations. The uncompressed output data are written to file for each member, having total size of 10 GB. Ensemble data from other sources (satellite, lightning, NWP) are converted to the same geometry as the radar data and blended as was explained above. A verification system utilizing telemetering rain gauges has been established. Alert dissemination e.g. for citizens and professional end users applies SMS messages and, in near future, smartphone maps. The present interactive user interface facilitates free selection of alert sites and two warning thresholds (any rain, heavy rain) at any location in Finland. The pilot service was tested by 1000-3000 users during summers 2010 and 2012. As an example of dedicated end-user services gridded exceedance scenarios (of probabilities 5 %, 50 % and 90 %) of hourly rainfall accumulations for the next 3 hours have been utilized as an online input data for the influent model at the Greater Helsinki Wastewater Treatment Plant.

KSC-2009-1623

NASA Image and Video Library

2009-02-06

VANDENBERG AIR FORCE BASE, Calif. -- The United Launch Alliance Delta II rocket carrying NASA's NOAA-N Prime satellite lifts off Space Launch Complex 2 at Vandenberg Air Force Base in California at 2:22 a.m. PST Feb. 6, 2009. The countdown and launch were managed by Kennedy Space Center’s Launch Services Program. Built for NASA by Lockheed Martin, the satellite will improve weather forecasting and monitor the world for environmental events, as well as for distress signals for the Search and Rescue Satellite-Aided Tracking System. NOAA-N Prime is the fifth and last in the National Oceanic and Atmospheric Administration’s current series of five polar-orbiting satellites with improved imaging and sounding capabilities. Photo credit: NASA/Carleton Bailie, VAFB-ULA

Applications systems verification and transfer project. Volume 3: Operational applications of satellite snow cover observations in California

NASA Technical Reports Server (NTRS)

Brown, A. J.; Hannaford, J. F.

1981-01-01

Five southern Sierra snowmelt basins and two northern Sierra-Southern Cascade snowmelt basins were used to evaluate the effect on operational water supply forecasting from satellite imagery. Manual photointerpretation techniques were used to obtain SCA and equivalent snow line for the years 1973 to 1979 for the seven test basins using LANDSAT imagery and GOES imagery. The use of SCA was tested operationally in 1977-79. Results indicate the addition of SCA improve the water supply forecasts during the snowmelt phase for these basins where there may be an unusual distribution of snowpack throughout the basin, or where there is a limited amount of real time data available. A high correlation to runoff was obtained when SCA was combined with snow water content data obtained from reporting snow sensors.

The Cooperative VAS Program with the Marshall Space Flight Center

NASA Technical Reports Server (NTRS)

Diak, George R.; Menzel, W. Paul

1988-01-01

Work was divided between the analysis/forecast model development and evaluation of the impact of satellite data in mesoscale numerical weather prediction (NWP), development of the Multispectral Atmospheric Mapping Sensor (MAMS), and other related research. The Cooperative Institute for Meteorological Satellite Studies (CIMSS) Synoptic Scale Model (SSM) has progressed from a relatively basic analysis/forecast system to a package which includes such features as nonlinear vertical mode initialization, comprehensive Planetary Boundary Layer (PBL) physics, and the core of a fully four-dimensional data assimilation package. The MAMS effort has produced a calibrated visible and infrared sensor that produces imager at high spatial resolution. The MAMS was developed in order to study small scale atmospheric moisture variability, to monitor and classify clouds, and to investigate the role of surface characteristics in the production of clouds, precipitation, and severe storms.

WPC Winter Weather Forecasts

Science.gov Websites

Summaries Heat Index Tropical Products Daily Weather Map GIS Products Current Watches/ Warnings Satellite and Radar Imagery GOES-East Satellite GOES-West Satellite National Radar Product Archive WPC

WPC Excessive Rainfall Forecasts

Science.gov Websites

Summaries Heat Index Tropical Products Daily Weather Map GIS Products Current Watches/ Warnings Satellite and Radar Imagery GOES-East Satellite GOES-West Satellite National Radar Product Archive WPC

Evaluation of NWP-based Satellite Precipitation Error Correction with Near-Real-Time Model Products and Flood-inducing Storms

NASA Astrophysics Data System (ADS)

Zhang, X.; Anagnostou, E. N.; Schwartz, C. S.

2017-12-01

Satellite precipitation products tend to have significant biases over complex terrain. Our research investigates a statistical approach for satellite precipitation adjustment based solely on numerical weather simulations. This approach has been evaluated in two mid-latitude (Zhang et al. 2013*1, Zhang et al. 2016*2) and three topical mountainous regions by using the WRF model to adjust two high-resolution satellite products i) National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center morphing technique (CMORPH) and ii) Global Satellite Mapping of Precipitation (GSMaP). Results show the adjustment effectively reduces the satellite underestimation of high rain rates, which provides a solid proof-of-concept for continuing research of NWP-based satellite correction. In this study we investigate the feasibility of using NCAR Real-time Ensemble Forecasts*3 for adjusting near-real-time satellite precipitation datasets over complex terrain areas in the Continental United States (CONUS) such as Olympic Peninsula, California coastal mountain ranges, Rocky Mountains and South Appalachians. The research will focus on flood-inducing storms occurred from May 2015 to December 2016 and four satellite precipitation products (CMORPH, GSMaP, PERSIANN-CCS and IMERG). The error correction performance evaluation will be based on comparisons against the gauge-adjusted Stage IV precipitation data. *1 Zhang, Xinxuan, et al. "Using NWP simulations in satellite rainfall estimation of heavy precipitation events over mountainous areas." Journal of Hydrometeorology 14.6 (2013): 1844-1858. *2 Zhang, Xinxuan, et al. "Hydrologic Evaluation of NWP-Adjusted CMORPH Estimates of Hurricane-Induced Precipitation in the Southern Appalachians." Journal of Hydrometeorology 17.4 (2016): 1087-1099. *3 Schwartz, Craig S., et al. "NCAR's experimental real-time convection-allowing ensemble prediction system." Weather and Forecasting 30.6 (2015): 1645-1654.

Research Opportunities from Emerging Atmospheric Observing and Modeling Capabilities.

NASA Astrophysics Data System (ADS)

Dabberdt, Walter F.; Schlatter, Thomas W.

1996-02-01

The Second Prospectus Development Team (PDT-2) of the U.S. Weather Research Program was charged with identifying research opportunities that are best matched to emerging operational and experimental measurement and modeling methods. The overarching recommendation of PDT-2 is that inputs for weather forecast models can best be obtained through the use of composite observing systems together with adaptive (or targeted) observing strategies employing both in situ and remote sensing. Optimal observing systems and strategies are best determined through a three-part process: observing system simulation experiments, pilot field measurement programs, and model-assisted data sensitivity experiments. Furthermore, the mesoscale research community needs easy and timely access to the new operational and research datasets in a form that can readily be reformatted into existing software packages for analysis and display. The value of these data is diminished to the extent that they remain inaccessible.The composite observing system of the future must combine synoptic observations, routine mobile observations, and targeted observations, as the current or forecast situation dictates. High costs demand fuller exploitation of commercial aircraft, meteorological and navigation [Global Positioning System (GPS)] satellites, and Doppler radar. Single observing systems must be assessed in the context of a composite system that provides complementary information. Maintenance of the current North American rawinsonde network is critical for progress in both research-oriented and operational weather forecasting.Adaptive sampling strategies are designed to improve large-scale and regional weather prediction but they will also improve diagnosis and prediction of flash flooding, air pollution, forest fire management, and other environmental emergencies. Adaptive measurements can be made by piloted or unpiloted aircraft. Rawinsondes can be launched and satellites can be programmed to make adaptive observations at special times or in specific regions. PDT-2 specifically recommends the following forms of data gathering: a pilot field and modeling study should be designed and executed to assess the benefit of adaptive observations over the eastern Pacific for mesoscale forecasts over the contiguous United

The Simulations of Wildland Fire Smoke PM25 in the NWS Air Quality Forecasting Systems

NASA Astrophysics Data System (ADS)

Huang, H. C.; Pan, L.; McQueen, J.; Lee, P.; ONeill, S. M.; Ruminski, M.; Shafran, P.; Huang, J.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

2017-12-01

The increase of wildland fire intensity and frequency in the United States (U.S.) has led to property loss, human fatality, and poor air quality due to elevated particulate matters and surface ozone concentrations. The NOAA/National Weather Service (NWS) built the National Air Quality Forecast Capability (NAQFC) based on the U.S. Environmental Protection Agency (EPA) Community Multi-scale Air Quality (CMAQ) Modeling System driven by the NCEP North American Mesoscale Forecast System meteorology to provide ozone and fine particulate matter (PM2.5) forecast guidance publicly. State and local forecasters use the NWS air quality forecast guidance to issue air quality alerts in their area. The NAQFC PM2.5 predictions include emissions from anthropogenic and biogenic sources, as well as natural sources such as dust storms and wildland fires. The wildland fire emission inputs to the NAQFC is derived from the NOAA National Environmental Satellite, Data, and Information Service Hazard Mapping System fire and smoke detection product and the emission module of the U.S. Forest Service (USFS) BlueSky Smoke Modeling Framework. Wildland fires are unpredictable and can be ignited by natural causes such as lightning or be human-caused. It is extremely difficult to predict future occurrences and behavior of wildland fires, as is the available bio-fuel to be burned for real-time air quality predictions. Assumptions of future day's wildland fire behavior often have to be made from older observed wildland fire information. The comparisons between the NAQFC modeled PM2.5 and the EPA AirNow surface observation show that large errors in PM2.5 prediction can occur if fire smoke emissions are sometimes placed at the wrong location and/or time. A configuration of NAQFC CMAQ-system to re-run previous 24 hours, during which wildland fires were observed from satellites has been included recently. This study focuses on the effort performed to minimize the error in NAQFC PM2.5 predictions resulting from incorporating fire smoke emissions into the NAQFC from a recently updated newer version of USFS BlueSky system. This study will show how new approaches has improved the PM2.5 predictions at both nearby and downstream areas from fire sources. Furthermore, Environment and Climate Change Canada (ECCC) fire emissions data are being tested.

The NASA Advanced Communications Technology Satellite (ACTS)

NASA Astrophysics Data System (ADS)

Beck, G. A.

1984-10-01

Forecasts indicate that a saturation of the capacity of the satellite communications service will occur in the U.S. domestic market by the early 1990s. In order to prevent this from happening, advanced technologies must be developed. NASA has been concerned with such a development. One key is the exploitation of the Ka-band (30/20 GHz), which is much wider than C- and Ku-bands together. Another is the use of multiple narrow antenna beams in the satellite to achieve large frequency reuse factors with very high antenna gains. NASA has developed proof-of-concept hardware components which form the basis for a flight demonstration. The Advanced Communications Technology Satellite (ACTS) system will provide this demonstration. Attention is given to the ACTS Program definition, the ACTS Flight System, the Multibeam Communications Package, and the spacecraft bus.

Integrating Windblown Dust Forecasts with Public Safety and Health Systems

NASA Astrophysics Data System (ADS)

Sprigg, W. A.

2014-12-01

Experiments in real-time prediction of desert dust emissions and downstream plume concentrations (~ 3.5 km near-surface spatial resolution) succeed to the point of challenging public safety and public health services to beta test a dust storm warning and advisory system in lowering risks of highway and airline accidents and illnesses such as asthma and valley fever. Key beta test components are: high-resolution models of dust emission, entrainment and diffusion, integrated with synoptic weather observations and forecasts; satellite-based detection and monitoring of soil properties on the ground and elevated above; high space and time resolution for health surveillance and transportation advisories.

The GOES-R Proving Ground: 2012 Update

NASA Astrophysics Data System (ADS)

Gurka, J.; Goodman, S. J.; Schmit, T.; Demaria, M.; Mostek, A.; Siewert, C.; Reed, B.

2011-12-01

The Geostationary Operational Environmental Satellite (GOES)-R will provide a great leap forward in observing capabilities, but will also offer a significant challenge to ensure that users are ready to exploit the vast improvements in spatial, spectral, and temporal resolutions. To ensure user readiness, forecasters and other users must have access to prototype advanced products well before launch, and have the opportunity to provide feedback to product developers and computing and communications managers. The operational assessment is critical to ensure that the end products and NOAA's computing and communications systems truly meet their needs in a rapidly evolving environment. The GOES-R Proving Ground (PG) engages the National Weather Service (NWS) forecast, watch and warning community and other agency users in pre-operational demonstrations of select products with GOES-R attributes (enhanced spectral, spatial, and temporal resolution). In the PG, developers and forecasters test and apply algorithms for new GOES-R satellite data and products using proxy and simulated data sets, including observations from current and future satellite instruments (MODIS, AIRS, IASI, SEVIRI, NAST-I, NPP/VIIRS/CrIS, LIS), lightning networks, and computer simulated products. The complete list of products to be evaluated in 2012 will be determined after evaluating results from experiments in 2011 at the NWS' Storm Prediction Center, National Hurricane Center, Aviation Weather Center, Ocean Prediction Center, Hydrometeorological Prediction Center, and from the six NWS regions. In 2012 and beyond, the PG will test and validate data processing and distribution systems and the applications of these products in operational settings. Additionally developers and forecasters will test and apply display techniques and decision aid tools in operational environments. The PG is both a recipient and a source of training. Training materials are developed using various distance training tools in close collaboration with NWS Training Division and its partners at COMET, CIMSS, CIRA and other offices. The training is used to prepare the participants of PG activities, such as the Hazardous Weather Testbed's Spring Experiment and other locations listed above. A key component of the proving ground is two-way interaction, where researchers introduce new products and techniques to forecasters and other scientists. The forecasters and other users then provide feedback and ideas for improved or new products and how to best incorporate these into NOAA's integrated observing and analysis operations. This presentation will provide examples of GOES-R proxy products and forecaster evaluations from experiments at the Storm Prediction Center (SPC), the National Hurricane Center (NHC), the Aviation Weather Center (AWC), and the Alaska Region.

The NASA Seasonal-to-Interannual Prediction Project (NSIPP). [Annual Report for 2000

NASA Technical Reports Server (NTRS)

Rienecker, Michele; Suarez, Max; Adamec, David; Koster, Randal; Schubert, Siegfried; Hansen, James; Koblinsky, Chester (Technical Monitor)

2001-01-01

The goal of the project is to develop an assimilation and forecast system based on a coupled atmosphere-ocean-land-surface-sea-ice model capable of using a combination of satellite and in situ data sources to improve the prediction of ENSO and other major S-I signals and their global teleconnections. The objectives of this annual report are to: (1) demonstrate the utility of satellite data, especially surface height surface winds, air-sea fluxes and soil moisture, in a coupled model prediction system; and (2) aid in the design of the observing system for short-term climate prediction by conducting OSSE's and predictability studies.

Toward quantitative forecasts of volcanic ash dispersal: Using satellite retrievals for optimal estimation of source terms

NASA Astrophysics Data System (ADS)

Zidikheri, Meelis J.; Lucas, Christopher; Potts, Rodney J.

2017-08-01

Airborne volcanic ash is a hazard to aviation. There is an increasing demand for quantitative forecasts of ash properties such as ash mass load to allow airline operators to better manage the risks of flying through airspace likely to be contaminated by ash. In this paper we show how satellite-derived mass load information at times prior to the issuance of the latest forecast can be used to estimate various model parameters that are not easily obtained by other means such as the distribution of mass of the ash column at the volcano. This in turn leads to better forecasts of ash mass load. We demonstrate the efficacy of this approach using several case studies.

Forecasting distributions of large federal-lands fires utilizing satellite and gridded weather information

USGS Publications Warehouse

Preisler, H.K.; Burgan, R.E.; Eidenshink, J.C.; Klaver, Jacqueline M.; Klaver, R.W.

2009-01-01

The current study presents a statistical model for assessing the skill of fire danger indices and for forecasting the distribution of the expected numbers of large fires over a given region and for the upcoming week. The procedure permits development of daily maps that forecast, for the forthcoming week and within federal lands, percentiles of the distributions of (i) number of ignitions; (ii) number of fires above a given size; (iii) conditional probabilities of fires greater than a specified size, given ignition. As an illustration, we used the methods to study the skill of the Fire Potential Index an index that incorporates satellite and surface observations to map fire potential at a national scale in forecasting distributions of large fires. ?? 2009 IAWF.

Application of Satellite Frost Forecast Technology to Other Parts of the United States

NASA Technical Reports Server (NTRS)

Martsolf, J. D.; Chen, E. (Principal Investigator)

1981-01-01

Thermal infrared data taken from the GOES satellite over a period of several hours was color enhanced by computer according to temperature. The varying temperatures were then used to assist in frost forecasting. Input from Michigan and Pennsylvania to the cold climate mapping project is emphasized in the report of the second year's activities of a two year effort.

Satellite-driven modeling approach for monitoring lava flow hazards during the 2017 Etna eruption

NASA Astrophysics Data System (ADS)

Del Negro, C.; Bilotta, G.; Cappello, A.; Ganci, G.; Herault, A.; Zago, V.

2017-12-01

The integration of satellite data and modeling represents an efficient strategy that may provide immediate answers to the main issues raised at the onset of a new effusive eruption. Satellite-based thermal remote sensing of hotspots related to effusive activity can effectively provide a variety of products suited to timing, locating, and tracking the radiant character of lava flows. Hotspots show the location and occurrence of eruptive events (vents). Discharge rate estimates may indicate the current intensity (effusion rate) and potential magnitude (volume). High-spatial resolution multispectral satellite data can complement field observations for monitoring the front position (length) and extension of flows (area). Physics-based models driven, or validated, by satellite-derived parameters are now capable of fast and accurate forecast of lava flow inundation scenarios (hazard). Here, we demonstrate the potential of the integrated application of satellite remote-sensing techniques and lava flow models during the 2017 effusive eruption at Mount Etna in Italy. This combined approach provided insights into lava flow field evolution by supplying detailed views of flow field construction (e.g., the opening of ephemeral vents) that were useful for more accurate and reliable forecasts of eruptive activity. Moreover, we gave a detailed chronology of the lava flow activity based on field observations and satellite images, assessed the potential extent of impacted areas, mapped the evolution of lava flow field, and executed hazard projections. The underside of this combination is the high sensitivity of lava flow inundation scenarios to uncertainties in vent location, discharge rate, and other parameters, which can make interpreting hazard forecasts difficult during an effusive crisis. However, such integration at last makes timely forecasts of lava flow hazards during effusive crises possible at the great majority of volcanoes for which no monitoring exists.

Nowcasting Aircraft Icing Conditions in Moscow Region Using Geostationary Meteorological Satellite Data

NASA Astrophysics Data System (ADS)

Barabanova, Olga

2013-04-01

Nowadays the Main Aviation Meteorological Centre in Moscow (MAMC) provides forecasts of icing conditions in Moscow Region airports using information of surface observation network, weather radars and atmospheric sounding. Unfortunately, satellite information is not used properly in aviation meteorological offices in Moscow Region: weather forecasters deal with satellites images of cloudiness only. The main forecasters of MAMC realise that it is necessary to employ meteorological satellite numerical data from different channels in aviation forecasting and especially in nowcasting. Algorithm of nowcasting aircraft in-flight icing conditions has been developed using data from geostationary meteorological satellites "Meteosat-7" and "Meteosat-9". The algorithm is based on the brightness temperature differences. Calculation of brightness temperature differences help to discriminate clouds with supercooled large drops where severe icing conditions are most likely. Due to the lack of visible channel data, the satellite icing detection methods will be less accurate at night. Besides this method is limited by optically thick ice clouds where it is not possible to determine the extent to which supercooled large drops exists within the underlying clouds. However, we determined that most of the optically thick cases are associated with convection or mid-latitude cyclones and they will nearly always have a layer where which supercooled large drops exists with an icing threat. This product is created hourly for the Moscow Air Space and mark zones with moderate or severe icing hazards. The results were compared with mesoscale numerical atmospheric model COSMO-RU output. Verification of the algorithms results using aircraft pilot reports shows that this algorithm is a good instrument for the operational practise in aviation meteorological offices in Moscow Region. The satellite-based algorithms presented here can be used in real time to diagnose areas of icing for pilots to avoid.

NASA's Earth Science Research and Environmental Predictions

NASA Technical Reports Server (NTRS)

Hilsenrath, E.

2004-01-01

NASA Earth Science program began in the 1960s with cloud imaging satellites used for weather observations. A fleet of satellites are now in orbit to investigate the Earth Science System to uncover the connections between land, Oceans and the atmosphere. Satellite systems using an array of active and passive remote sensors are used to search for answers on how is the Earth changing and what are the consequences for life on Earth? The answer to these questions can be used for applications to serve societal needs and contribute to decision support systems for weather, hazard, and air quality predictions and mitigation of adverse effects. Partnerships with operational agencies using NASA's observational capabilities are now being explored. The system of the future will require new technology, data assimilation systems which includes data and models that will be used for forecasts that respond to user needs.

WPC 48-Hour Surface Weather Forecast

Science.gov Websites

Summaries Heat Index Tropical Products Daily Weather Map GIS Products Current Watches/ Warnings Satellite and Radar Imagery GOES-East Satellite GOES-West Satellite National Radar Product Archive WPC

WPC 12-Hour Surface Weather Forecast

Science.gov Websites

Summaries Heat Index Tropical Products Daily Weather Map GIS Products Current Watches/ Warnings Satellite and Radar Imagery GOES-East Satellite GOES-West Satellite National Radar Product Archive WPC

WPC 36-Hour Surface Weather Forecast

Science.gov Websites

Summaries Heat Index Tropical Products Daily Weather Map GIS Products Current Watches/ Warnings Satellite and Radar Imagery GOES-East Satellite GOES-West Satellite National Radar Product Archive WPC

WPC Excessive Rainfall and Winter Weather Forecasts

Science.gov Websites

Summaries Heat Index Tropical Products Daily Weather Map GIS Products Current Watches/ Warnings Satellite and Radar Imagery GOES-East Satellite GOES-West Satellite National Radar Product Archive WPC

WPC 24-Hour Surface Weather Forecast

Science.gov Websites

Summaries Heat Index Tropical Products Daily Weather Map GIS Products Current Watches/ Warnings Satellite and Radar Imagery GOES-East Satellite GOES-West Satellite National Radar Product Archive WPC

Assessing the Impact of Observations on Numerical Weather Forecasts Using the Adjoint Method

NASA Technical Reports Server (NTRS)

Gelaro, Ronald

2012-01-01

The adjoint of a data assimilation system provides a flexible and efficient tool for estimating observation impacts on short-range weather forecasts. The impacts of any or all observations can be estimated simultaneously based on a single execution of the adjoint system. The results can be easily aggregated according to data type, location, channel, etc., making this technique especially attractive for examining the impacts of new hyper-spectral satellite instruments and for conducting regular, even near-real time, monitoring of the entire observing system. This talk provides a general overview of the adjoint method, including the theoretical basis and practical implementation of the technique. Results are presented from the adjoint-based observation impact monitoring tool in NASA's GEOS-5 global atmospheric data assimilation and forecast system. When performed in conjunction with standard observing system experiments (OSEs), the adjoint results reveal both redundancies and dependencies between observing system impacts as observations are added or removed from the assimilation system. Understanding these dependencies may be important for optimizing the use of the current observational network and defining requirements for future observing systems

(?) The Air Force Geophysics Laboratory: Aeronomy, aerospace instrumentation, space physics, meteorology, terrestrial sciences and optical physics

NASA Astrophysics Data System (ADS)

McGinty, A. B.

1982-04-01

Contents: The Air Force Geophysics Laboratory; Aeronomy Division--Upper Atmosphere Composition, Middle Atmosphere Effects, Atmospheric UV Radiation, Satellite Accelerometer Density Measurement, Theoretical Density Studies, Chemical Transport Models, Turbulence and Forcing Functions, Atmospheric Ion Chemistry, Energy Budget Campaign, Kwajalein Reference Atmospheres, 1979, Satellite Studies of the Neutral Atmosphere, Satellite Studies of the Ionosphere, Aerospace Instrumentation Division--Sounding Rocket Program, Satellite Support, Rocket and Satellite Instrumentation; Space Physics Division--Solar Research, Solar Radio Research, Environmental Effects on Space Systems, Solar Proton Event Studies, Defense Meteorological Satellite Program, Ionospheric Effects Research, Spacecraft Charging Technology; Meteorology Division--Cloud Physics, Ground-Based Remote-Sensing Techniques, Mesoscale Observing and Forecasting, Design Climatology, Aircraft Icing Program, Atmospheric Dynamics; Terrestrial Sciences Division--Geodesy and Gravity, Geokinetics; Optical Physics Division--Atmospheric Transmission, Remote Sensing, INfrared Background; and Appendices.

Transition, Training, and Assessment of Multispectral Composite Imagery in Support of the NWS Aviation Forecast Mission

NASA Technical Reports Server (NTRS)

Fuell, Kevin; Jedlovec, Gary; Leroy, Anita; Schultz, Lori

2015-01-01

The NASA/Short-term Prediction, Research, and Transition (SPoRT) Program works closely with NOAA/NWS weather forecasters to transition unique satellite data and capabilities into operations in order to assist with nowcasting and short-term forecasting issues. Several multispectral composite imagery (i.e. RGB) products were introduced to users in the early 2000s to support hydrometeorology and aviation challenges as well as incident support. These activities lead to SPoRT collaboration with the GOES-R Proving Ground efforts where instruments such as MODIS (Aqua, Terra) and S-NPP/VIIRS imagers began to be used as near-realtime proxies to future capabilities of the Advanced Baseline Imager (ABI). One of the composite imagery products introduced to users was the Night-time Microphysics RGB, originally developed by EUMETSAT. SPoRT worked to transition this imagery to NWS users, provide region-specific training, and assess the impact of the imagery to aviation forecast needs. This presentation discusses the method used to interact with users to address specific aviation forecast challenges, including training activities undertaken to prepare for a product assessment. Users who assessed the multispectral imagery ranged from southern U.S. inland and coastal NWS weather forecast offices (WFOs), to those in the Rocky Mountain Front Range region and West Coast, as well as highlatitude forecasters of Alaska. These user-based assessments were documented and shared with the satellite community to support product developers and the broad users of new generation satellite data.

An operational earth resources satellite system - The Landsat follow-on program

NASA Technical Reports Server (NTRS)

Stroud, W. G.

1977-01-01

The Landsats 1 and 2 have demonstrated the role of remote sensing from satellite in research, development, and operational activities essential to the better management of our resources. Hundreds of agricultural, geological, hydrological, urban land use, and other investigations have raised the question of the development of an operational system providing continuous, timely data. The Landsat follow-on study addressed the economics, technological performance, and design of a system in transition from R&D to operations. Economic benefits were identified; and a complete system from sensors to the utilization in forecasting crop production, oil and mineral exploration, water resources management was designed. Benefits-to-costs ratio in present-worth dollars is at least 4:1.

IMPROVING NATIONAL AIR QUALITY FORECASTS WITH SATELLITE AEROSOL OBSERVATIONS

EPA Science Inventory

Air quality forecasts for major US metropolitan areas have been provided to the public through a partnership between the US Environmental Protection Agency and state and local air agencies since 1997. Recent years have witnessed improvement in forecast skill and expansion of fore...

GEO/SAMS - The Geostationary Synthetic Aperture Microwave Sounder

NASA Technical Reports Server (NTRS)

Lambrigtsen, Bjorn H.

2008-01-01

The National Oceanic and Atmospheric Administration (NOAA) has for many years operated two weather satellite systems, the Polar-orbiting Operational Environmental Satellite system (POES), using low-earth orbiting (LEO) satellites, and the Geostationary Operational Environmental Satellite system (GOES), using geostationary earth orbiting (GEO) satellites. (Similar systems are also operated by other nations.) The POES satellites have been equipped with both infrared (IR) and microwave (MW) atmospheric sounders, which makes it possible to determine the vertical distribution of temperature and humidity in the troposphere even under cloudy conditions. Such satellite observations have had a significant impact on weather forecasting accuracy, especially in regions where in situ observations are sparse. In contrast, the GOES satellites have only been equipped with IR sounders, since it has not been feasible to build a large enough antenna to achieve sufficient spatial resolution for a MW sounder in GEO. As a result, GOES soundings can only be obtained in cloud free areas and in the less important upper atmosphere, above the cloud tops. This has hindered the effective use of GOES data in numerical weather prediction. Full sounding capabilities with the GOES system is highly desirable because of the advantageous spatial and temporal coverage that is possible from GEO. While POES satellites provide coverage in relatively narrow swaths, and with a revisit time of 12-24 hours or more, GOES satellites can provide continuous hemispheric coverage, making it possible to monitor highly dynamic phenomena such as hurricanes.

Quantifying the Value of Satellite Imagery in Agriculture and other Sectors

NASA Astrophysics Data System (ADS)

Brown, M. E.; Abbott, P. C.; Escobar, V. M.

2013-12-01

This study focused on quantifying the commercial value of satellite remote sensing for agriculture. Commercial value from satellite imagery arises when improved information leads to better economic decisions. We identified five areas of application of remote sensing to agriculture where there is this potential: crop management (precision agriculture), insurance, real estate assessment, crop forecasting, and environmental monitoring. These applications can be divided between public information (crop forecasting) and those that may generate private commercial value (crop management), with both public and private information dimensions in some categories. Public information applications of remote sensing have been more successful in the past, and are likely to generate more economic value in the future. It was found that several issues have limited realization of the potential to generate private value from remote sensing in agriculture. The scale of use is small to the high cost of acquiring and interpreting large images has limited the cost effectiveness to individual farmers. Insurance, environmental monitoring, and crop management services by cooperatives or consultants may be cases overcoming this limitation. The greatest opportunities for potential commercial value from agriculture are probably in the crop forecasting area, especially where agricultural statistics services are not as well developed, since public market information benefits a broad range of economic actors, not limited to countries where forecasts are made. We estimate here the value from components of USDA's World Agricultural Supply and Demand Estimates (WASDE) forecasts for corn, indicating potential value increasing in the range of 60 to 240 million if improved satellite based information enhances those forecasts. The research was conducted by agricultural economists at Purdue University, and will be the basis for further evaluation of the use of satellite data within the NASA Carbon Monitoring System (CMS). A general evaluation framework to determine the usefulness of the CMS products to various users and to the broader community interested in managing carbon is shown in Figure 2. The first step in conducting such an analysis is to develop an understanding of the history, institutions, behaviors and other factors setting the context of an application which CMS data products inform. Decision makers are identified (who may become early adopters), and the alternative decisions they might take are elaborated. Economic models informed by biophysical models would then predict the outcome of the engagement. The new information must then be linked to a revised decision, and that decision in turn must lead to better economic or social outcomes on average. The value of the information is estimated as the predicted increase in economic surplus (profit, cost, consumer welfare) or social outcome that is a direct result of that revised decision. Alternative Monte Carlo simulations would estimate averages of key outcomes under alternative circumstances, such as differing regulations or better data, hence capturing consequences of the changes induced. These approaches will be described in the context of NASA and satellite data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Woodroffe, Jesse; Jordanova, Vania; Toth, Gabor

Extreme weather happens worldwide and it takes place in the magnetosphere. The magnetosphere is the place where the majority of earth’s satellites reside. These satellites provide weather forecasting and serve as national defense. When solar storms take place, they can damage satellites.

Ensemble Data Assimilation of Photovoltaic Power Information in the Convection-permitting High-Resolution Model COSMO-DE

NASA Astrophysics Data System (ADS)

Declair, Stefan; Saint-Drenan, Yves-Marie; Potthast, Roland

2017-04-01

Determining the amount of weather dependent renewable energy is a demanding task for transmission system operators (TSOs) and wind and photovoltaic (PV) prediction errors require the use of reserve power, which generate costs and can - in extreme cases - endanger the security of supply. In the project EWeLiNE funded by the German government, the German Weather Service and the Fraunhofer Institute on Wind Energy and Energy System Technology develop innovative weather- and power forecasting models and tools for grid integration of weather dependent renewable energy. The key part in energy prediction process chains is the numerical weather prediction (NWP) system. Irradiation forecasts from NWP systems are however subject to several sources of error. For PV power prediction, weaknesses of the NWP model to correctly forecast i.e. low stratus, absorption of condensed water or aerosol optical depths are the main sources of errors. Inaccurate radiation schemes (i.e. the two-stream parametrization) are also known as a deficit of NWP systems with regard to irradiation forecast. To mitigate errors like these, latest observations can be used in a pre-processing technique called data assimilation (DA). In DA, not only the initial fields are provided, but the model is also synchronized with reality - the observations - and hence forecast errors are reduced. Besides conventional observation networks like radiosondes, synoptic observations or air reports of wind, pressure and humidity, the number of observations measuring meteorological information indirectly by means of remote sensing such as satellite radiances, radar reflectivities or GPS slant delays strongly increases. Numerous PV plants installed in Germany potentially represent a dense meteorological network assessing irradiation through their power measurements. Forecast accuracy may thus be enhanced by extending the observations in the assimilation by this new source of information. PV power plants can provide information on clouds, aerosol optical depth or low stratus in terms of remote sensing: the power output is strongly dependent on perturbations along the slant between sun position and PV panel. Since these data are not limited to the vertical column above or below the detector, it may thus complement satellite data and compensate weaknesses in the radiation scheme. In this contribution, the used DA technique (Local Ensemble Transform Kalman Filter, LETKF) is shortly sketched. Furthermore, the computation of the model power equivalents is described and first results are presented and discussed.

Tethered Satellites as Enabling Platforms for an Operational Space Weather Monitoring System

NASA Technical Reports Server (NTRS)

Krause, L. Habash; Gilchrist, B. E.; Bilen, S.; Owens, J.; Voronka, N.; Furhop, K.

2013-01-01

Space weather nowcasting and forecasting models require assimilation of near-real time (NRT) space environment data to improve the precision and accuracy of operational products. Typically, these models begin with a climatological model to provide "most probable distributions" of environmental parameters as a function of time and space. The process of NRT data assimilation gently pulls the climate model closer toward the observed state (e.g. via Kalman smoothing) for nowcasting, and forecasting is achieved through a set of iterative physics-based forward-prediction calculations. The issue of required space weather observatories to meet the spatial and temporal requirements of these models is a complex one, and we do not address that with this poster. Instead, we present some examples of how tethered satellites can be used to address the shortfalls in our ability to measure critical environmental parameters necessary to drive these space weather models. Examples include very long baseline electric field measurements, magnetized ionospheric conductivity measurements, and the ability to separate temporal from spatial irregularities in environmental parameters. Tethered satellite functional requirements will be presented for each space weather parameter considered in this study.

The framework of a UAS-aided flash flood modeling system for coastal regions

NASA Astrophysics Data System (ADS)

Zhang, H.; Xu, H.

2016-02-01

Flash floods cause severe economic damage and are one of the leading causes of fatalities connected with natural disasters in the Gulf Coast region. Current flash flood modeling systems rely on empirical hydrological models driven by precipitation estimates only. Although precipitation is the driving factor for flash floods, soil moisture, urban drainage system and impervious surface have been recognized to have significant impacts on the development of flash floods. We propose a new flash flooding modeling system that integrates 3-D hydrological simulation with satellite and multi-UAS observations. It will have three advantages over existing modeling systems. First, it will incorporate 1-km soil moisture data through integrating satellite images from European SMOS mission and NASA's SMAP mission. The utilization of high-resolution satellite images will provide essential information to determine antecedent soil moisture condition, which is an essential control on flood generation. Second, this system is able to adjust flood forecasting based on real-time inundation information collected by multi-UAS. A group of UAS will be deployed during storm events to capture the changing extent of flooded areas and water depth at multiple critical locations simultaneously. Such information will be transmitted to a hydrological model to validate and improve flood simulation. Third, the backbone of this system is a state-of-the-art 3-D hydrological model that assimilates the hydrological information from satellites and multi-UAS. The model is able to address surface water-groundwater interactions and reflect the effects of various infrastructures. Using Web-GIS technologies, the modeling results will be available online as interactive flood maps accessible to the public. To support the development and verification of this modeling system, surface and subsurface hydrological observations will be conducted in a number of small watersheds in the Coastal Bend region. We envision this system will provide an innovative means to benefit the forecasting, evaluation and mitigation of flash floods in costal regions.

Estimating and forecasting the precipitable water vapor from GOES satellite data at high altitude sites

NASA Astrophysics Data System (ADS)

Marín, Julio C.; Pozo, Diana; Curé, Michel

2015-01-01

In this work, we describe a method to estimate the precipitable water vapor (PWV) from Geostationary Observational Environmental Satellite (GOES) data at high altitude sites. The method was applied at Atacama Pathfinder Experiment (APEX) and Cerro Toco sites, located above 5000 m altitude in the Chajnantor plateau, in the north of Chile. It was validated using GOES-12 satellite data over the range 0-1.2 mm since submillimeter/millimeter astronomical observations are only useful within this PWV range. The PWV estimated from GOES and the Final Analyses (FNL) at APEX for 2007 and 2009 show root mean square error values of 0.23 mm and 0.36 mm over the ranges 0-0.4 mm and 0.4-1.2 mm, respectively. However, absolute relative errors of 51% and 33% were shown over these PWV ranges, respectively. We recommend using high-resolution thermodynamic profiles from the Global Forecast System (GFS) model to estimate the PWV from GOES data since they are available every three hours and at an earlier time than the FNL data. The estimated PWV from GOES/GFS agrees better with the observed PWV at both sites during night time. The largest errors are shown during daytime. Short-term PWV forecasts were implemented at both sites, applying a simple persistence method to the PWV estimated from GOES/GFS. The 12 h and 24 h PWV forecasts evaluated from August to October 2009 indicates that 25% of them show a very good agreement with observations whereas 50% of them show reasonably good agreement with observations. Transmission uncertainties calculated for PWV estimations and forecasts over the studied sites are larger over the range 0-0.4 mm than over the range 0.4-1.2 mm. Thus, the method can be used over the latter interval with more confidence.

A Near Real-time Decision Support System Improving Forest Management in the Tropics

NASA Astrophysics Data System (ADS)

Tabor, K.; Musinsky, J.; Ledezma, J.; Rasolohery, A.; Mendoza, E.; Kistler, H.; Steininger, M.; Morton, D. C.; Melton, F. S.; Manwell, J.; Koenig, K.

2013-12-01

Conservation International (CI) has a decade of experience developing near real-time fire and deforestation monitoring and forecasting systems that channel monitoring information from satellite observations directly to national and sub-national government agencies, Non-Government Organizations (NGOs), and local communities. These systems are used to strengthen forest surveillance and monitoring, fire management and prevention, protected areas management and sustainable land use planning. With support from a NASA Wildland Fires grant, in September 2013 CI will launch a brand new near real-time alert system (FIRECAST) to better meet the outstanding needs and challenges users face in addressing ecosystem degradation from wildland fire and illegal forest activities. Outreach efforts and user feedback have indicated the need for seasonal fire forecasts for effective land use planning, faster alert delivery to enhance response to illegal forest activities, and expanded forest monitoring capabilities that enable proactive responses and that strengthen forest conservation and sustainable development actions. The new FIRECAST system addresses these challenges by integrating the current fire alert and deforestation systems and adding improved ecological forecasting of fire risk; expanding data exchange capabilities with mobile technologies; and delivering a deforestation alert product that can inform policies related to land use management and Reduced Emissions from Deforestation and forest Degradation (REDD+). In addition to demonstrating the capabilities of this new real-time alert system, we also highlight how coordination with host-country institutions enhances the system's capacity to address the implementation needs of REDD+ forest carbon projects, improve tropical forest management, strengthen environmental law enforcement, and facilitate the uptake of near real-time satellite monitoring data into business practices of these national/sub-national institutions.

Foreword to the Special Issue on Remote Sensing and Modeling of Surface Properties

USDA-ARS?s Scientific Manuscript database

CURRENTLY, the Numerical Weather Prediction (NWP) community is striving for better ways to extract information on the lower layer using current and future satellite systems to improve short-term to medium-range forecasts. The surface emissivity is highly variable and may cause biases in the forward ...

Using Flow Charts to Visualize the Decision-Making Process in Space Weather Forecasting

NASA Astrophysics Data System (ADS)

Aung, M. T. Y.; Myat, T.; Zheng, Y.; Mays, M. L.; Ngwira, C.; Damas, M. C.

2016-12-01

Our society today relies heavily on technological systems such as satellites, navigation systems, power grids and aviation. These systems are very sensitive to space weather disturbances. When Earth-directed space weather driven by the Sun arrives at the Earth, it causes changes to the Earth's radiation environment and the magnetosphere. Strong disturbances in the magnetosphere of the Earth are responsible for geomagnetic storms that can last from hours to days depending on strength of storms. Geomagnetic storms can severely impact critical infrastructure on Earth, such as the electric power grid, and Solar Energetic Particles that can endanger life in outer space. How can we lessen these adverse effects? They can be lessened through the early warning signals sent by space weather forecasters before CME or high-speed stream arrives. A space weather forecaster's duty is to send predicted notifications to high-tech industries and NASA missions so that they could take extra measures for protection. NASA space weather forecasters make prediction decisions by following certain steps and processes from the time an event occurs at the sun all the way to the impact locations. However, there has never been a tool that helps these forecasters visualize the decision process until now. A flow chart is created to help forecasters visualize the decision process. This flow chart provides basic knowledge of space weather and can be used to train future space weather forecasters. It also helps to cut down the training period and increase consistency in forecasting. The flow chart is also a great reference for people who are already familiar with space weather.

Efficient ensemble forecasting of marine ecology with clustered 1D models and statistical lateral exchange: application to the Red Sea

NASA Astrophysics Data System (ADS)

Dreano, Denis; Tsiaras, Kostas; Triantafyllou, George; Hoteit, Ibrahim

2017-07-01

Forecasting the state of large marine ecosystems is important for many economic and public health applications. However, advanced three-dimensional (3D) ecosystem models, such as the European Regional Seas Ecosystem Model (ERSEM), are computationally expensive, especially when implemented within an ensemble data assimilation system requiring several parallel integrations. As an alternative to 3D ecological forecasting systems, we propose to implement a set of regional one-dimensional (1D) water-column ecological models that run at a fraction of the computational cost. The 1D model domains are determined using a Gaussian mixture model (GMM)-based clustering method and satellite chlorophyll-a (Chl-a) data. Regionally averaged Chl-a data is assimilated into the 1D models using the singular evolutive interpolated Kalman (SEIK) filter. To laterally exchange information between subregions and improve the forecasting skills, we introduce a new correction step to the assimilation scheme, in which we assimilate a statistical forecast of future Chl-a observations based on information from neighbouring regions. We apply this approach to the Red Sea and show that the assimilative 1D ecological models can forecast surface Chl-a concentration with high accuracy. The statistical assimilation step further improves the forecasting skill by as much as 50%. This general approach of clustering large marine areas and running several interacting 1D ecological models is very flexible. It allows many combinations of clustering, filtering and regression technics to be used and can be applied to build efficient forecasting systems in other large marine ecosystems.

Data-Driven Disease Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Generous, Nicholas

If disease outbreaks could be forecasted like the weather, communities could set up protective measures to mitigate their impact. At Los Alamos National Laboratory, scientists are improving disease-forecasting mathematical models by using clinical data--as well as internet data sources such as Wikipedia, Twitter, and Google--and coupling it with satellite imagery. The goal is to better understanding how diseases spread and, eventually, forecast disease outbreaks.

Comparison of Satellite Observations of Aerosol Optical Depth to Surface Monitor Fine Particle Concentration

NASA Technical Reports Server (NTRS)

Kleb, Mary M.; AlSaadi, Jassim A.; Neil, Doreen O.; Pierce, Robert B.; Pippin, Margartet R.; Roell, Marilee M.; Kittaka, Chieko; Szykman, James J.

2004-01-01

Under NASA's Earth Science Applications Program, the Infusing satellite Data into Environmental Applications (IDEA) project examined the relationship between satellite observations and surface monitors of air pollutants to facilitate a more capable and integrated observing network. This report provides a comparison of satellite aerosol optical depth to surface monitor fine particle concentration observations for the month of September 2003 at more than 300 individual locations in the continental US. During September 2003, IDEA provided prototype, near real-time data-fusion products to the Environmental Protection Agency (EPA) directed toward improving the accuracy of EPA s next-day Air Quality Index (AQI) forecasts. Researchers from NASA Langley Research Center and EPA used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument combined with EPA ground network data to create a NASA-data-enhanced Forecast Tool. Air quality forecasters used this tool to prepare their forecasts of particle pollution, or particulate matter less than 2.5 microns in diameter (PM2.5), for the next-day AQI. The archived data provide a rich resource for further studies and analysis. The IDEA project uses data sets and models developed for tropospheric chemistry research to assist federal, state, and local agencies in making decisions concerning air quality management to protect public health.

An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning forecast system

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.

2013-12-01

One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset hindcasts indicate that climate models increase drought detectability over ESP by 31%-81%. However, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with weak ENSO signals and lower potential predictability. Due to the high false alarms from climate models, the reliability is more important than sharpness for a skillful probabilistic drought onset forecast. Validations and skill assessments for agricultural and hydrologic drought forecasts are carried out using soil moisture and streamflow output from the VIC land surface model (LSM) forced by a global forcing data set. Given our previous drought forecasting experiences over USA and Africa, validating the hydrologic drought forecasting is a significant challenge for a global drought early warning system.

Satellite Data Assimilation within KIAPS-LETKF system

NASA Astrophysics Data System (ADS)

Jo, Y.; Lee, S., Sr.; Cho, K.

2016-12-01

Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing an ensemble data assimilation system using four-dimensional local ensemble transform kalman filter (LETKF; Hunt et al., 2007) within KIAPS Integrated Model (KIM), referred to as "KIAPS-LETKF". KIAPS-LETKF system was successfully evaluated with various Observing System Simulation Experiments (OSSEs) with NCAR Community Atmospheric Model - Spectral Element (Kang et al., 2013), which has fully unstructured quadrilateral meshes based on the cubed-sphere grid as the same grid system of KIM. Recently, assimilation of real observations has been conducted within the KIAPS-LETKF system with four-dimensional covariance functions over the 6-hr assimilation window. Then, conventional (e.g., sonde, aircraft, and surface) and satellite (e.g., AMSU-A, IASI, GPS-RO, and AMV) observations have been provided by the KIAPS Package for Observation Processing (KPOP). Wind speed prediction was found most beneficial due to ingestion of AMV and for the temperature prediction the improvement in assimilation is mostly due to ingestion of AMSU-A and IASI. However, some degradation in the simulation of the GPS-RO is presented in the upper stratosphere, even though GPS-RO leads positive impacts on the analysis and forecasts. We plan to test the bias correction method and several vertical localization strategies for radiance observations to improve analysis and forecast impacts.

BUFR TABLE A

Science.gov Websites

Surface data - sea 2 Vertical soundings (other than satellite) 3 Vertical soundings (satellite) 4 Single level upper-air data (other than satellite) 5 Single level upper-air data (satellite) 6 Radar data 7 tables, complete replacement or update 12 Surface data (satellite) 13 Forecasts 14 Warnings 15-19

Applicability of Satellite Freeze Forecasting and Cold Climate Mapping to the Other Parts of the United States

NASA Technical Reports Server (NTRS)

1981-01-01

Tasks performed to determine the value of using GOES satellite thermal imagery to enhance fruit crop production in Michigan are described. An overview is presented of the system developed for image processing and thermal image and surface environmental data bases prepared to assess the physical models developed in Florida. These data bases were used to identify correlations between satellite apparent temperatures patterns and Earth surface factors. Significant freeze events in 1981 and the physical models used to provide a perspective on how Florida models can be applied in the context of the Michigan environment are discussed.

Traffic model for the satellite component of UMTS

NASA Technical Reports Server (NTRS)

Hu, Y. F.; Sheriff, R. E.

1995-01-01

An algorithm for traffic volume estimation for satellite mobile communications systems has been developed. This algorithm makes use of worldwide databases for demographic and economic data. In order to provide for such an estimation, the effects of competing services have been considered so that likely market demand can be forecasted. Different user groups of the predicted market have been identified according to expectations in the quality of services and mobility requirement. The number of users for different user groups are calculated taking into account the gross potential market, the penetration rate of the identified services and the profitability to provide such services via satellite.

Cloud Impacts on Pavement Temperature in Energy Balance Models

NASA Astrophysics Data System (ADS)

Walker, C. L.

2013-12-01

Forecast systems provide decision support for end-users ranging from the solar energy industry to municipalities concerned with road safety. Pavement temperature is an important variable when considering vehicle response to various weather conditions. A complex, yet direct relationship exists between tire and pavement temperatures. Literature has shown that as tire temperature increases, friction decreases which affects vehicle performance. Many forecast systems suffer from inaccurate radiation forecasts resulting in part from the inability to model different types of clouds and their influence on radiation. This research focused on forecast improvement by determining how cloud type impacts the amount of shortwave radiation reaching the surface and subsequent pavement temperatures. The study region was the Great Plains where surface solar radiation data were obtained from the High Plains Regional Climate Center's Automated Weather Data Network stations. Road pavement temperature data were obtained from the Meteorological Assimilation Data Ingest System. Cloud properties and radiative transfer quantities were obtained from the Clouds and Earth's Radiant Energy System mission via Aqua and Terra Moderate Resolution Imaging Spectroradiometer satellite products. An additional cloud data set was incorporated from the Naval Research Laboratory Cloud Classification algorithm. Statistical analyses using a modified nearest neighbor approach were first performed relating shortwave radiation variability with road pavement temperature fluctuations. Then statistical associations were determined between the shortwave radiation and cloud property data sets. Preliminary results suggest that substantial pavement forecasting improvement is possible with the inclusion of cloud-specific information. Future model sensitivity testing seeks to quantify the magnitude of forecast improvement.

Satellite Proving Ground for the GOES-R Geostationary Lightning Mapper (GLM)

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Gurka, James; Bruning, E. C.; Blakeslee, J. R.; Rabin, Robert; Buechler, D.

2009-01-01

The key mission of the Satellite Proving Ground is to demonstrate new satellite observing data, products and capabilities in the operational environment to be ready on Day 1 to use the GOES-R suite of measurements. Algorithms, tools, and techniques must be tested, validated, and assessed by end users for their utility before they are finalized and incorporated into forecast operations. The GOES-R Proving Ground for the Geostationary Lightning Mapper (GLM) focuses on evaluating how the infusion of the new technology, algorithms, decision aids, or tailored products integrate with other available tools (weather radar and ground strike networks; nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing forecasts and warning products. Additionally, the testing concept fosters operation and development staff interactions which will improve training materials and support documentation development. Real-time proxy total lightning data from regional VHF lightning mapping arrays (LMA) in Northern Alabama, Central Oklahoma, Cape Canaveral Florida, and the Washington, DC Greater Metropolitan Area are the cornerstone for the GLM Proving Ground. The proxy data will simulate the 8 km Event, Group and Flash data that will be generated by GLM. Tailored products such as total flash density at 1-2 minute intervals will be provided for display in AWIPS-2 to select NWS forecast offices and national centers such as the Storm Prediction Center. Additional temporal / spatial combinations are being investigated in coordination with operational needs and case-study proxy data and prototype visualizations may also be generated from the NASA heritage Lightning Imaging Sensor and Optical Transient Detector data. End users will provide feedback on the utility of products in their operational environment, identify use cases and spatial/temporal scales of interest, and provide feedback to the developers for adjusted or new products.

Impact of Assimilated and Interactive Aerosol on Tropical Cyclogenesis

NASA Technical Reports Server (NTRS)

Reale, O.; Lau, K. M.; daSilva, A.; Matsui, T.

2014-01-01

This article investigates the impact 3 of Saharan dust on the development of tropical cyclones in the Atlantic. A global data assimilation and forecast system, the NASA GEOS-5, is used to assimilate all satellite and conventional data sets used operationally for numerical weather prediction. In addition, this new GEOS-5 version includes assimilation of aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer (MODIS). The analysis so obtained comprises atmospheric quantities and a realistic 3-d aerosol and cloud distribution, consistent with the meteorology and validated against Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat data. These improved analyses are used to initialize GEOS-5 forecasts, explicitly accounting for aerosol direct radiative effects and their impact on the atmospheric dynamics. Parallel simulations with/without aerosol radiative effects show that effects of dust on static stability increase with time, becoming highly significant after day 5 and producing an environment less favorable to tropical cyclogenesis.

Intra-seasonal NDVI change projections in semi-arid Africa

USGS Publications Warehouse

Funk, Christopher C.; Brown, Molly E.

2006-01-01

Early warning systems (EWS) tend to focus on the identification of slow onset disasters such famine and epidemic disease. Since hazardous environmental conditions often precede disastrous outcomes by many months, effective monitoring via satellite and in situ observations can successfully guide mitigation activities. Accurate short term forecasts of NDVI could increase lead times, making early warning earlier. This paper presents a simple empirical model for making 1 to 4 month NDVI projections. These statistical projections are based on parameterized satellite rainfall estimates (RFE) and relative humidity demand (RHD). A quasi-global, 1 month ahead, 1° study demonstrates reasonable accuracies in many semi-arid regions. In Africa, a 0.1° cross-validated skill assessment quantifies the technique's applicability at 1 to 4 month forecast intervals. These results suggest that useful projections can be made over many semi-arid, food insecure regions of Africa, with plausible extensions to drought prone areas of Asia, Australia and South America.

SEASAT economic assessment. Volume 9: Ports and harbors case study and generalization. [economic benefits of SEASAT satellites to harbors and shipping industries through improved weather forecasting

NASA Technical Reports Server (NTRS)

1975-01-01

This case study and generalization quantify benefits made possible through improved weather forecasting resulting from the integration of SEASAT data into local weather forecasts. The major source of avoidable economic losses to shipping from inadequate weather forecasting data is shown to be dependent on local precipitation forecasting. The ports of Philadelphia and Boston were selected for study.

Assimilation of ground and satellite snow observations in a distributed hydrologic model to improve water supply forecasts in the Upper Colorado River Basin

NASA Astrophysics Data System (ADS)

Micheletty, P. D.; Day, G. N.; Quebbeman, J.; Carney, S.; Park, G. H.

2016-12-01

The Upper Colorado River Basin above Lake Powell is a major source of water supply for 25 million people and provides irrigation water for 3.5 million acres. Approximately 85% of the annual runoff is produced from snowmelt. Water supply forecasts of the April-July runoff produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC), are critical to basin water management. This project leverages advanced distributed models, datasets, and snow data assimilation techniques to improve operational water supply forecasts made by CBRFC in the Upper Colorado River Basin. The current work will specifically focus on improving water supply forecasts through the implementation of a snow data assimilation process coupled with the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM). Three types of observations will be used in the snow data assimilation system: satellite Snow Covered Area (MODSCAG), satellite Dust Radiative Forcing in Snow (MODDRFS), and SNOTEL Snow Water Equivalent (SWE). SNOTEL SWE provides the main source of high elevation snowpack information during the snow season, however, these point measurement sites are carefully selected to provide consistent indices of snowpack, and may not be representative of the surrounding watershed. We address this problem by transforming the SWE observations to standardized deviates and interpolating the standardized deviates using a spatial regression model. The interpolation process will also take advantage of the MODIS Snow Covered Area and Grainsize (MODSCAG) product to inform the model on the spatial distribution of snow. The interpolated standardized deviates are back-transformed and used in an Ensemble Kalman Filter (EnKF) to update the model simulated SWE. The MODIS Dust Radiative Forcing in Snow (MODDRFS) product will be used more directly through temporary adjustments to model snowmelt parameters, which should improve melt estimates in areas affected by dust on snow. In order to assess the value of different data sources, reforecasts will be produced for a historical period and performance measures will be computed to assess forecast skill. The existing CBRFC Ensemble Streamflow Prediction (ESP) reforecasts will provide a baseline for comparison to determine the added-value of the data assimilation process.

Optimum employment of satellite indirect soundings as numerical model input

NASA Technical Reports Server (NTRS)

Horn, L. H.; Derber, J. C.; Koehler, T. L.; Schmidt, B. D.

1981-01-01

The characteristics of satellite-derived temperature soundings that would significantly affect their use as input for numerical weather prediction models were examined. Independent evaluations of satellite soundings were emphasized to better define error characteristics. Results of a Nimbus-6 sounding study reveal an underestimation of the strength of synoptic scale troughs and ridges, and associated gradients in isobaric height and temperature fields. The most significant errors occurred near the Earth's surface and the tropopause. Soundings from the TIROS-N and NOAA-6 satellites were also evaluated. Results again showed an underestimation of upper level trough amplitudes leading to weaker thermal gradient depictions in satellite-only fields. These errors show a definite correlation to the synoptic flow patterns. In a satellite-only analysis used to initialize a numerical model forecast, it was found that these synoptically correlated errors were retained in the forecast sequence.

Lognormal Assimilation of Water Vapor in a WRF-GSI Cycled System

NASA Astrophysics Data System (ADS)

Fletcher, S. J.; Kliewer, A.; Jones, A. S.; Forsythe, J. M.

2015-12-01

Recent publications have shown the viability of both detecting a lognormally-distributed signal for water vapor mixing ratio and the improved quality of satellite retrievals in a 1DVAR mixed lognormal-Gaussian assimilation scheme over a Gaussian-only system. This mixed scheme is incorporated into the Gridpoint Statistical Interpolation (GSI) assimilation scheme with the goal of improving forecasts from the Weather Research and Forecasting (WRF) Model in a cycled system. Results are presented of the impact of treating water vapor as a lognormal random variable. Included in the analysis are: 1) the evolution of Tropical Storm Chris from 2006, and 2) an analysis of a "Pineapple Express" water vapor event from 2005 where a lognormal signal has been previously detected.

Evaluation of the synoptic and mesoscale predictive capabilities of a mesoscale atmospheric simulation system

NASA Technical Reports Server (NTRS)

Koch, S. E.; Skillman, W. C.; Kocin, P. J.; Wetzel, P. J.; Brill, K.; Keyser, D. A.; Mccumber, M. C.

1983-01-01

The overall performance characteristics of a limited area, hydrostatic, fine (52 km) mesh, primitive equation, numerical weather prediction model are determined in anticipation of satellite data assimilations with the model. The synoptic and mesoscale predictive capabilities of version 2.0 of this model, the Mesoscale Atmospheric Simulation System (MASS 2.0), were evaluated. The two part study is based on a sample of approximately thirty 12h and 24h forecasts of atmospheric flow patterns during spring and early summer. The synoptic scale evaluation results benchmark the performance of MASS 2.0 against that of an operational, synoptic scale weather prediction model, the Limited area Fine Mesh (LFM). The large sample allows for the calculation of statistically significant measures of forecast accuracy and the determination of systematic model errors. The synoptic scale benchmark is required before unsmoothed mesoscale forecast fields can be seriously considered.

Ionospheric Data Assimilation and Targeted Observation Strategies: Proof of Concept Analysis in a Geomagnetic Storm Event

NASA Astrophysics Data System (ADS)

Kostelich, Eric; Durazo, Juan; Mahalov, Alex

2017-11-01

The dynamics of the ionosphere involve complex interactions between the atmosphere, solar wind, cosmic radiation, and Earth's magnetic field. Geomagnetic storms arising from solar activity can perturb these dynamics sufficiently to disrupt radio and satellite communications. Efforts to predict ``space weather,'' including ionospheric dynamics, require the development of a data assimilation system that combines observing systems with appropriate forecast models. This talk will outline a proof-of-concept targeted observation strategy, consisting of the Local Ensemble Transform Kalman Filter, coupled with the Thermosphere Ionosphere Electrodynamics Global Circulation Model, to select optimal locations where additional observations can be made to improve short-term ionospheric forecasts. Initial results using data and forecasts from the geomagnetic storm of 26-27 September 2011 will be described. Work supported by the Air Force Office of Scientific Research (Grant Number FA9550-15-1-0096) and by the National Science Foundation (Grant Number DMS-0940314).

The potential predictability of fire danger provided by ECMWF forecast

NASA Astrophysics Data System (ADS)

Di Giuseppe, Francesca

2017-04-01

The European Forest Fire Information System (EFFIS), is currently being developed in the framework of the Copernicus Emergency Management Services to monitor and forecast fire danger in Europe. The system provides timely information to civil protection authorities in 38 nations across Europe and mostly concentrates on flagging regions which might be at high danger of spontaneous ignition due to persistent drought. The daily predictions of fire danger conditions are based on the US Forest Service National Fire Danger Rating System (NFDRS), the Canadian forest service Fire Weather Index Rating System (FWI) and the Australian McArthur (MARK-5) rating systems. Weather forcings are provided in real time by the European Centre for Medium range Weather Forecasts (ECMWF) forecasting system. The global system's potential predictability is assessed using re-analysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 years of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire and low values correspond to non observed events. A more quantitative skill evaluation was performed using the Extremal Dependency Index which is a skill score specifically designed for rare events. It revealed that the three indices were more skilful on a global scale than the random forecast to detect large fires. The performance peaks in the boreal forests, in the Mediterranean, the Amazon rain-forests and southeast Asia. The skill-scores were then aggregated at country level to reveal which nations could potentiallty benefit from the system information in aid of decision making and fire control support. Overall we found that fire danger modelling based on weather forecasts, can provide reasonable predictability over large parts of the global landmass.

Understanding the influence of assimilating satellite-derived observations on mesoscale analyses and forecasts of tropical cyclone track and structure

NASA Astrophysics Data System (ADS)

Wu, Ting-Chi

This dissertation research explores the influence of assimilating satellite-derived observations on mesoscale numerical analyses and forecasts of tropical cyclones (TC). The ultimate goal is to provide more accurate mesoscale analyses of TC and its surrounding environment for superior TC track and intensity forecasts. High spatial and temporal resolution satellite-derived observations are prepared for two TC cases, Typhoon Sinlaku and Hurricane Ike (both 2008). The Advanced Research version of the Weather and Research Forecasting Model (ARW-WRF) is employed and data is assimilated using the Ensemble Adjustment Kalman Filter (EAKF) implemented in the Data Assimilation Research Testbed. In the first part of this research, the influence of assimilating enhanced atmospheric motion vectors (AMVs) derived from geostationary satellites is examined by comparing three parallel WRF/EnKF experiments. The control experiment assimilates the same AMV dataset assimilated in NCEP operational analysis along with conventional observations from radiosondes, aircraft, and advisory TC position data. During Sinlaku and Ike, the Cooperative Institute for Meteorological Satellite Studies (CIMSS) generates hourly AMVs along with Rapid-Scan (RS) AMVs when the satellite RS mode is activated. With an order of magnitude more AMV data assimilated, the assimilation of hourly CIMSS AMV dataset exhibit superior initial TC position, intensity and structure estimates to the control analyses and the subsequent short-range forecasts. When RS AMVs are processed and assimilated, the addition of RS AMVs offers additional modification to the TC and its environment and leads to Sinlaku's recurvature toward Japan, albeit prematurely. The results demonstrate the promise of assimilating enhanced AMV data into regional TC models. The second part of this research continues the work in the first part and further explores the influence of assimilating enhanced AMV datasets by conducting parallel data-denial WRF/EnKF experiments that assimilate AMVs subsetted horizontally by their distances to the TC center (interior and exterior) and vertically by their assigned heights (upper, middle, and lower layers). For both Sinlaku and Ike, it is found: 1) interior AMVs are important for accurate TC intensity, 2) excluding upper-layer AMVs generally results in larger track errors and ensemble spread, 3) exclusion of interior AMVs has the largest impact on the forecast of TC size than exclusively removing AMVs in particular tropospheric layers, 4) the largest ensemble spreads are found in track, intensity, and size forecasts when interior and upper-layer AMVs are not included, 5) withholding the middle-layer AMVs can improve the track forecasts. Findings from this study could influence future scenarios that involve the targeted acquisition and assimilation of high-density AMV observations in TC events. The last part of the research focuses on the assimilation of hyperspectral temperature and moisture soundings and microwave based vertically-integrated total precipitable water (TPW) products derived from polar-orbiting satellites. A comparison is made between the assimilation of soundings retrieved from the combined use of Advanced Microwave Scanning Radiometer and Atmospheric Infrared Sounder (AMSU-AIRS) and sounding products provided by CIMSS (CIMSS-AIRS). AMSU-AIRS soundings provide broad spatial coverage albeit coarse resolution, whilst CIMSS-AIRS is geared towards mesoscale applications and thus provide higher spatial resolution but restricted coverage due to the use of radiance in clear sky. The assimilation of bias-corrected CIMSS-AIRS soundings provides slightly more accurate TC structure than the control case. The assimilation of AMSU-AIRS improves the track forecasts but produces weaker and smaller storm. Preliminary results of assimilating TPW product derived from the Advanced Microwave Scanning Radiometer-EOS indicate improved TC structure over the control case. However, the short-range forecasts exhibit the largest TC track errors. In all, this study demonstrates the influence of assimilating high-resolution satellite data on mesoscale analyses and forecasts of TC track and structure. The results suggest the inclusion and assimilation of observations with high temporal resolution, broad spatial coverage, and greater proximity to TCs does indeed improve TC track and structure forecasts. Such findings are beneficial for future decisions on data collecting and retrievals that are essential for TC forecasts.

Potential influences of neglecting aerosol effects on the NCEP GFS precipitation forecast

NASA Astrophysics Data System (ADS)

Jiang, Mengjiao; Feng, Jinqin; Li, Zhanqing; Sun, Ruiyu; Hou, Yu-Tai; Zhu, Yuejian; Wan, Bingcheng; Guo, Jianping; Cribb, Maureen

2017-11-01

Aerosol-cloud interactions (ACIs) have been widely recognized as a factor affecting precipitation. However, they have not been considered in the operational National Centers for Environmental Predictions Global Forecast System model. We evaluated the potential impact of neglecting ACI on the operational rainfall forecast using ground-based and satellite observations and model reanalysis. The Climate Prediction Center unified gauge-based precipitation analysis and the Modern-Era Retrospective analysis for Research and Applications Version 2 aerosol reanalysis were used to evaluate the forecast in three countries for the year 2015. The overestimation of light rain (47.84 %) and underestimation of heavier rain (31.83, 52.94, and 65.74 % for moderate rain, heavy rain, and very heavy rain, respectively) from the model are qualitatively consistent with the potential errors arising from not accounting for ACI, although other factors cannot be totally ruled out. The standard deviation of the forecast bias was significantly correlated with aerosol optical depth in Australia, the US, and China. To gain further insight, we chose the province of Fujian in China to pursue a more insightful investigation using a suite of variables from gauge-based observations of precipitation, visibility, water vapor, convective available potential energy (CAPE), and satellite datasets. Similar forecast biases were found: over-forecasted light rain and under-forecasted heavy rain. Long-term analyses revealed an increasing trend in heavy rain in summer and a decreasing trend in light rain in other seasons, accompanied by a decreasing trend in visibility, no trend in water vapor, and a slight increasing trend in summertime CAPE. More aerosols decreased cloud effective radii for cases where the liquid water path was greater than 100 g m-2. All findings are consistent with the effects of ACI, i.e., where aerosols inhibit the development of shallow liquid clouds and invigorate warm-base mixed-phase clouds (especially in summertime), which in turn affects precipitation. While we cannot establish rigorous causal relations based on the analyses presented in this study, the significant rainfall forecast bias seen in operational weather forecast model simulations warrants consideration in future model improvements.

Downscaling, 2-way Nesting, and Data Assimilative Modeling in Coastal and Shelf Waters of the U.S. Mid-Atlantic Bight and Gulf of Maine

NASA Astrophysics Data System (ADS)

Wilkin, J.; Levin, J.; Lopez, A.; Arango, H.

2016-02-01

Coastal ocean models that downscale output from basin and global scale models are widely used to study regional circulation at enhanced resolution and locally important ecosystem, biogeochemical, and geomorphologic processes. When operated as now-cast or forecast systems, these models offer predictions that assist decision-making for numerous maritime applications. We describe such a system for shelf waters of the Mid-Atlantic Bight (MAB) and Gulf of Maine (GoM) where the MARACOOS and NERACOOS associations of U.S. IOOS operate coastal ocean observing systems that deliver a dense observation set using CODAR HF-radar, autonomous underwater glider vehicles (AUGV), telemetering moorings, and drifting buoys. Other U.S. national and global observing systems deliver further sustained observations from moorings, ships, profiling floats, and a constellation of satellites. Our MAB and GoM re-analysis and forecast system uses the Regional Ocean Modeling System (ROMS; myroms.org) with 4-dimensional Variational (4D-Var) data assimilation to adjust initial conditions, boundary conditions, and surface forcing in each analysis cycle. Data routinely assimilated include CODAR velocities, altimeter satellite sea surface height (with coastal corrections), satellite temperature, in situ CTD data from AUGV and ships (NMFS Ecosystem Monitoring voyages), and all in situ data reported via the WMO GTS network. A climatological data assimilative analysis of hydrographic and long-term mean velocity observations specifies the regional Mean Dynamic Topography that augments altimeter sea level anomaly data and is also used to adjust boundary condition biases that would otherwise be introduced in the process of downscaling from global models. System performance is described with respect to the impact of satellite, CODAR and in situ observations on analysis skill. Results from a 2-way nested modeling system that adds enhanced resolution over the NSF OOI Pioneer Array in the central MAB are also shown.

Forecasting the Impact of an 1859-calibre Superstorm on Satellite Resources

NASA Technical Reports Server (NTRS)

Odenwald, Sten; Green, James; Taylor, William

2005-01-01

We have assembled a database of operational satellites in orbit as of 2004, and have developed a series of simple models to assess the economic impacts to this resource caused by various scenarios of superstorm events possible during the next sunspot cycle between 2010 and 2014. Despite the apparent robustness of our satellite assets against the kinds of storms we have encountered during the satellite era, our models suggest a potential economic loss exceeding $10(exp 11) for satellite replacement and lost profitability caused by a once a century single storm similar to the 1859 superstorm. From a combination of power system and attitude control system (the most vulnerable) failures, we estimate that 80 satellites (LEO, MEO, GEO) may be disabled as a consequence of a superstorm event. Additional consequences may include the failure of many of the GPS, GLONASS and Galileo satellite systems in MEO. Approximately 98 LEO satellites that normally would not have re-entered for many decades, may prematurely de-orbit in ca 2021 as a result of the temporarily increased atmospheric drag caused by the superstorm event occurring in 2012. The $10(exp 11) International Space Station may lose at least 15 kilometers of altitude, placing it in critical need for re-boosting by an amount that is potentially outside the range of typical Space Shuttle operations during the previous solar maximum in ca 2000, and at a time when NASA plans to decommission the Space Shuttle. Several LEO satellites will unexpectedly be placed on orbits that enter the ISS zone of avoidance, requiring some action by ground personnel and ISS astronauts to avoid close encounters. Radiation effects on astronauts have also been considered and could include a range of possibilities from acute radiation sickness for astronauts inside spacecraft, to near-lethal doses during EVAs. The specifics depends very sensitively on the spectral hardness of the accompanying SPE event. Currently, the ability to forecast extreme particle events and coronal mass ejections, or predict their fluences and geo-severity in the 24-hrs prior to the event, appears to be no better than 50/50. If the events of the 1859 superstorm serve as a guide, the scope of a contemporary superstorm will most certainly be an awesome event, but one that the vast majority of our other satellite resources may reasonably be expected to survive.

Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

NASA Astrophysics Data System (ADS)

Su, X.

2017-12-01

A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

COMMERCIAL SERVICES PROVIDING MARINE FORECASTS VIA SATELLITE

Science.gov Websites

Tsunamis 406 EPIRB's National Weather Service Marine Forecasts COMMERCIAL SERVICES PROVIDING MARINE forecast seas? And may present an even greater danger near shore or any shallow waters? COMMERCIAL SERVICES commercial product or service does not imply any endorsement by the National Weather Service as to function

Short-range quantitative precipitation forecasting using Deep Learning approaches

NASA Astrophysics Data System (ADS)

Akbari Asanjan, A.; Yang, T.; Gao, X.; Hsu, K. L.; Sorooshian, S.

2017-12-01

Predicting short-range quantitative precipitation is very important for flood forecasting, early flood warning and other hydrometeorological purposes. This study aims to improve the precipitation forecasting skills using a recently developed and advanced machine learning technique named Long Short-Term Memory (LSTM). The proposed LSTM learns the changing patterns of clouds from Cloud-Top Brightness Temperature (CTBT) images, retrieved from the infrared channel of Geostationary Operational Environmental Satellite (GOES), using a sophisticated and effective learning method. After learning the dynamics of clouds, the LSTM model predicts the upcoming rainy CTBT events. The proposed model is then merged with a precipitation estimation algorithm termed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) to provide precipitation forecasts. The results of merged LSTM with PERSIANN are compared to the results of an Elman-type Recurrent Neural Network (RNN) merged with PERSIANN and Final Analysis of Global Forecast System model over the states of Oklahoma, Florida and Oregon. The performance of each model is investigated during 3 storm events each located over one of the study regions. The results indicate the outperformance of merged LSTM forecasts comparing to the numerical and statistical baselines in terms of Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), RMSE and correlation coefficient especially in convective systems. The proposed method shows superior capabilities in short-term forecasting over compared methods.

Technology development of the Space Transportation System mission and terrestrial applications of satellite technology

NASA Technical Reports Server (NTRS)

1981-01-01

The Space Transportation System (STS) is discussed, including the launch processing system, the thermal protection subsystem, meteorological research, sound supression water system, rotating service structure, improved hypergol or removal systems, fiber optics research, precision positioning, remote controlled solid rocket booster nozzle plugs, ground operations for Centaur orbital transfer vehicle, parachute drying, STS hazardous waste disposal and recycle, toxic waste technology and control concepts, fast analytical densitometry study, shuttle inventory management system, operational intercommunications system improvement, and protective garment ensemble. Terrestrial applications are also covered, including LANDSAT applications to water resources, satellite freeze forecast system, application of ground penetrating radar to soil survey, turtle tracking, evaluating computer drawn ground cover maps, sparkless load pulsar, and coupling a microcomputer and computing integrator with a gas chromatograph.

Improving Flood Forecasting in International River Basins

NASA Astrophysics Data System (ADS)

Hossain, Faisal; Katiyar, Nitin

2006-01-01

In flood-prone international river basins (IRBs), many riparian nations that are located close to a basin's outlet face a major problem in effectively forecasting flooding because they are unable to assimilate in situ rainfall data in real time across geopolitical boundaries. NASA's proposed Global Precipitation Measurement (GPM) mission, which is expected to begin in 2010, will comprise high-resolution passive microwave (PM) sensors (at resolution ~3-6 hours, 10 × 10 square kilometers) that may provide new opportunities to improve flood forecasting in these river basins. Research is now needed to realize the potential of GPM. With adequate research in the coming years, it may be possible to identify the specific IRBs that would benefit cost-effectively from a preprogrammed satellite-based forecasting system in anticipation of GPM. Acceleration of such a research initiative is worthwhile because it could reduce the risk of the cancellation of GPM [see Zielinski, 2005].

Arctic sea ice trends, variability and implications for seasonal ice forecasting

PubMed Central

Serreze, Mark C.; Stroeve, Julienne

2015-01-01

September Arctic sea ice extent over the period of satellite observations has a strong downward trend, accompanied by pronounced interannual variability with a detrended 1 year lag autocorrelation of essentially zero. We argue that through a combination of thinning and associated processes related to a warming climate (a stronger albedo feedback, a longer melt season, the lack of especially cold winters) the downward trend itself is steepening. The lack of autocorrelation manifests both the inherent large variability in summer atmospheric circulation patterns and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend. These findings have implications for seasonal ice forecasting. In particular, while advances in observing sea ice thickness and assimilating thickness into coupled forecast systems have improved forecast skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability. PMID:26032315

Streamlining On-Demand Access to Joint Polar Satellite System (JPSS) Data Products for Weather Forecasting

NASA Astrophysics Data System (ADS)

Evans, J. D.; Tislin, D.

2017-12-01

Observations from the Joint Polar Satellite System (JPSS) support National Weather Service (NWS) forecasters, whose Advanced Weather Interactive Processing System (AWIPS) Data Delivery (DD) will access JPSS data products on demand from the National Environmental Satellite, Data, and Information Service (NESDIS) Product Distribution and Access (PDA) service. Based on the Open Geospatial Consortium (OGC) Web Coverage Service, this on-demand service promises broad interoperability and frugal use of data networks by serving only the data that a user needs. But the volume, velocity, and variety of JPSS data products impose several challenges to such a service. It must be efficient to handle large volumes of complex, frequently updated data, and to fulfill many concurrent requests. It must offer flexible data handling and delivery, to work with a diverse and changing collection of data, and to tailor its outputs into products that users need, with minimal coordination between provider and user communities. It must support 24x7 operation, with no pauses in incoming data or user demand; and it must scale to rapid changes in data volume, variety, and demand as new satellites launch, more products come online, and users rely increasingly on the service. We are addressing these challenges in order to build an efficient and effective on-demand JPSS data service. For example, on-demand subsetting by many users at once may overload a server's processing capacity or its disk bandwidth - unless alleviated by spatial indexing, geolocation transforms, or pre-tiling and caching. Filtering by variable (/ band / layer) may also alleviate network loads, and provide fine-grained variable selection; to that end we are investigating how best to provide random access into the variety of spatiotemporal JPSS data products. Finally, producing tailored products (derivatives, aggregations) can boost flexibility for end users; but some tailoring operations may impose significant server loads. Operating this service in a cloud computing environment allows cost-effective scaling during the development and early deployment phases - and perhaps beyond. We will discuss how NESDIS and NWS are assessing and addressing these challenges to provide timely and effective access to JPSS data products for weather forecasters throughout the country.

Epidemic forecasting is messier than weather forecasting: The role of human behavior and internet data streams in epidemic forecast

DOE PAGES

Moran, Kelly Renee; Fairchild, Geoffrey; Generous, Nicholas; ...

2016-11-14

Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection andmore » Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. Here, we conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.« less

Epidemic forecasting is messier than weather forecasting: The role of human behavior and internet data streams in epidemic forecast

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moran, Kelly Renee; Fairchild, Geoffrey; Generous, Nicholas

Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection andmore » Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. Here, we conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.« less

Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast

PubMed Central

Moran, Kelly R.; Fairchild, Geoffrey; Generous, Nicholas; Hickmann, Kyle; Osthus, Dave; Priedhorsky, Reid; Hyman, James; Del Valle, Sara Y.

2016-01-01

Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection and Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. We conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting. PMID:28830111

Online Visualization and Analysis of Global Half-Hourly Infrared Satellite Data

NASA Technical Reports Server (NTRS)

Liu, Zhong; Ostrenga, Dana; Leptoukh, Gregory

2011-01-01

nfrared (IR) images (approximately 11-micron channel) recorded by satellite sensors have been widely used in weather forecasting, research, and classroom education since the Nimbus program. Unlike visible images, IR imagery can reveal cloud features without sunlight illumination; therefore, they can be used to monitor weather phenomena day and night. With geostationary satellites deployed around the globe, it is possible to monitor weather events 24/7 at a temporal resolution that polar-orbiting satellites cannot achieve at the present time. When IR data from multiple geostationary satellites are merged to form a single product--also known as a merged product--it allows for observing weather on a global scale. Its high temporal resolution (e.g., every half hour) also makes it an ideal ancillary dataset for supporting other satellite missions, such as the Tropical Rainfall Measuring Mission (TRMM), etc., by providing additional background information about weather system evolution.

Joint Center for Satellite Data Assimilation Overview and Research Activities

NASA Astrophysics Data System (ADS)

Auligne, T.

2017-12-01

In 2001 NOAA/NESDIS, NOAA/NWS, NOAA/OAR, and NASA, subsequently joined by the US Navy and Air Force, came together to form the Joint Center for Satellite Data Assimilation (JCSDA) for the common purpose of accelerating the use of satellite data in environmental numerical prediction modeling by developing, using, and anticipating advances in numerical modeling, satellite-based remote sensing, and data assimilation methods. The primary focus was to bring these advances together to improve operational numerical model-based forecasting, under the premise that these partners have common technical and logistical challenges assimilating satellite observations into their modeling enterprises that could be better addressed through cooperative action and/or common solutions. Over the last 15 years, the JCSDA has made and continues to make major contributions to operational assimilation of satellite data. The JCSDA is a multi-agency U.S. government-owned-and-operated organization that was conceived as a venue for the several agencies NOAA, NASA, USAF and USN to collaborate on advancing the development and operational use of satellite observations into numerical model-based environmental analysis and forecasting. The primary mission of the JCSDA is to "accelerate and improve the quantitative use of research and operational satellite data in weather, ocean, climate and environmental analysis and prediction systems." This mission is fulfilled through directed research targeting the following key science objectives: Improved radiative transfer modeling; new instrument assimilation; assimilation of humidity, clouds, and precipitation observations; assimilation of land surface observations; assimilation of ocean surface observations; atmospheric composition; and chemistry and aerosols. The goal of this presentation is to briefly introduce the JCSDA's mission and vision, and to describe recent research activities across various JCSDA partners.

WPC's Short Range Forecasts (Days 0.5 - 2.5) - Black and White

Science.gov Websites

Summaries Heat Index Tropical Products Daily Weather Map GIS Products Current Watches/ Warnings Satellite and Radar Imagery GOES-East Satellite GOES-West Satellite National Radar Product Archive WPC

A comparison of the domestic satellite communications forecast to the year 2000

NASA Technical Reports Server (NTRS)

Poley, W. A.; Lekan, J. F.; Salzman, J. A.; Stevenson, S. M.

1983-01-01

The methodologies and results of three NASA-sponsored market demand assessment studies are presented and compared. Forecasts of future satellite addressable traffic (both trunking and customer premises services) were developed for the three main service categories of voice, data and video and subcategories thereof for the benchmark years of 1980, 1990 and 2000. The contractor results are presented on a service by service basis in two formats: equivalent 36 MHz transponders and basic transmission units (voice: half-voice circuits, data: megabits per second and video: video channels). It is shown that while considerable differences exist at the service category level, the overall forecasts by the two contractors are quite similar. ITT estimates the total potential satellite market to be 3594 transponders in the year 2000 with data service comprising 54 percent of this total. The WU outlook for the same time period is 2779 transponders with voice services accounting for 66 percent of the total.

Surface Pressure Dependencies in the GEOS-Chem-Adjoint System and the Impact of the GEOS-5 Surface Pressure on CO2 Model Forecast

NASA Technical Reports Server (NTRS)

Lee, Meemong; Weidner, Richard

2016-01-01

In the GEOS-Chem Adjoint (GCA) system, the total (wet) surface pressure of the GEOS meteorology is employed as dry surface pressure, ignoring the presence of water vapor. The Jet Propulsion Laboratory (JPL) Carbon Monitoring System (CMS) research team has been evaluating the impact of the above discrepancy on the CO2 model forecast and the CO2 flux inversion. The JPL CMS research utilizes a multi-mission assimilation framework developed by the Multi-Mission Observation Operator (M2O2) research team at JPL extending the GCA system. The GCA-M2O2 framework facilitates mission-generic 3D and 4D-variational assimilations streamlining the interfaces to the satellite data products and prior emission inventories. The GCA-M2O2 framework currently integrates the GCA system version 35h and provides a dry surface pressure setup to allow the CO2 model forecast to be performed with the GEOS-5 surface pressure directly or after converting it to dry surface pressure.

Surface Pressure Dependencies in the Geos-Chem-Adjoint System and the Impact of the GEOS-5 Surface Pressure on CO2 Model Forecast

NASA Technical Reports Server (NTRS)

Lee, Meemong; Weidner, Richard

2016-01-01

In the GEOS-Chem Adjoint (GCA) system, the total (wet) surface pressure of the GEOS meteorology is employed as dry surface pressure, ignoring the presence of water vapor. The Jet Propulsion Laboratory (JPL) Carbon Monitoring System (CMS) research team has been evaluating the impact of the above discrepancy on the CO2 model forecast and the CO2 flux inversion. The JPL CMS research utilizes a multi-mission assimilation framework developed by the Multi-Mission Observation Operator (M2O2) research team at JPL extending the GCA system. The GCA-M2O2 framework facilitates mission-generic 3D and 4D-variational assimilations streamlining the interfaces to the satellite data products and prior emission inventories. The GCA-M2O2 framework currently integrates the GCA system version 35h and provides a dry surface pressure setup to allow the CO2 model forecast to be performed with the GEOS-5 surface pressure directly or after converting it to dry surface pressure.

Towards the Operational Ensemble-based Data Assimilation System for the Wave Field at the National Weather Service

NASA Astrophysics Data System (ADS)

Flampouris, Stylianos; Penny, Steve; Alves, Henrique

2017-04-01

The National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA) provides the operational wave forecast for the US National Weather Service (NWS). Given the continuous efforts to improve forecast, NCEP is developing an ensemble-based data assimilation system, based on the local ensemble transform Kalman filter (LETKF), the existing operational global wave ensemble system (GWES) and on satellite and in-situ observations. While the LETKF was designed for atmospheric applications (Hunt et al 2007), and has been adapted for several ocean models (e.g. Penny 2016), this is the first time applied for oceanic waves assimilation. This new wave assimilation system provides a global estimation of the surface sea state and its approximate uncertainty. It achieves this by analyzing the 21-member ensemble of the significant wave height provided by GWES every 6h. Observations from four altimeters and all the available in-situ measurements are used in this analysis. The analysis of the significant wave height is used for initializing the next forecasting cycle; the data assimilation system is currently being tested for operational use.

Assessing the Skill of Chlorophyll Forecasts: Latest Development and Challenges Ahead Using the Case of the Equatorial Pacific

NASA Technical Reports Server (NTRS)

Rousseaux, Cecile S.; Gregg, Watson W.

2018-01-01

Using a global ocean biogeochemical model combined with a forecast of physical oceanic and atmospheric variables from the NASA Global Modeling and Assimilation Office, we assess the skill of a chlorophyll concentrations forecast in the Equatorial Pacific for the period 2012-2015 with a focus on the forecast of the onset of the 2015 El Nino event. Using a series of retrospective 9-month hindcasts, we assess the uncertainties of the forecasted chlorophyll by comparing the monthly total chlorophyll concentration from the forecast with the corresponding monthly ocean chlorophyll data from the Suomi-National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (S-NPP VIIRS) satellite. The forecast was able to reproduce the phasing of the variability in chlorophyll concentration in the Equatorial Pacific, including the beginning of the 2015-2016 El Nino. The anomaly correlation coefficient (ACC) was significant (p less than 0.05) for forecast at 1-month (R=0.33), 8-month (R=0.42) and 9-month (R=0.41) lead times. The root mean square error (RMSE) increased from 0.0399 microgram chl L(exp -1) for the 1-month lead forecast to a maximum of 0.0472 microgram chl L(exp -1) for the 9-month lead forecast indicating that the forecast of the amplitude of chlorophyll concentration variability was getting worse. Forecasts with a 3-month lead time were on average the closest to the S-NPP VIIRS data (23% or 0.033 microgram chl L(exp -1)) while the forecast with a 9-month lead time were the furthest (31% or 0.042 microgram chl L(exp -1)). These results indicate the potential for forecasting chlorophyll concentration in this region but also highlights various deficiencies and suggestions for improvements to the current biogeochemical forecasting system. This system provides an initial basis for future applications including the effects of El Nino events on fisheries and other ocean resources given improvements identified in the analysis of these results.

Forecasting Ocean Chlorophyll in the Equatorial Pacific.

PubMed

Rousseaux, Cecile S; Gregg, Watson W

2017-01-01

Using a global ocean biogeochemical model combined with a forecast of physical oceanic and atmospheric variables from the NASA Global Modeling and Assimilation Office, we assess the skill of a chlorophyll concentrations forecast in the Equatorial Pacific for the period 2012-2015 with a focus on the forecast of the onset of the 2015 El Niño event. Using a series of retrospective 9-month hindcasts, we assess the uncertainties of the forecasted chlorophyll by comparing the monthly total chlorophyll concentration from the forecast with the corresponding monthly ocean chlorophyll data from the Suomi-National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (S-NPP VIIRS) satellite. The forecast was able to reproduce the phasing of the variability in chlorophyll concentration in the Equatorial Pacific, including the beginning of the 2015-2016 El Niño. The anomaly correlation coefficient (ACC) was significant ( p < 0.05) for forecast at 1-month ( R = 0.33), 8-month ( R = 0.42) and 9-month ( R = 0.41) lead times. The root mean square error (RMSE) increased from 0.0399 μg chl L -1 for the 1-month lead forecast to a maximum of 0.0472 μg chl L -1 for the 9-month lead forecast indicating that the forecast of the amplitude of chlorophyll concentration variability was getting worse. Forecasts with a 3-month lead time were on average the closest to the S-NPP VIIRS data (23% or 0.033 μg chl L -1 ) while the forecast with a 9-month lead time were the furthest (31% or 0.042 μg chl L -1 ). These results indicate the potential for forecasting chlorophyll concentration in this region but also highlights various deficiencies and suggestions for improvements to the current biogeochemical forecasting system. This system provides an initial basis for future applications including the effects of El Niño events on fisheries and other ocean resources given improvements identified in the analysis of these results.

Geodetic Space Weather Monitoring by means of Ionosphere Modelling

NASA Astrophysics Data System (ADS)

Schmidt, Michael

2017-04-01

The term space weather indicates physical processes and phenomena in space caused by radiation of energy mainly from the Sun. Manifestations of space weather are (1) variations of the Earth's magnetic field, (2) the polar lights in the northern and southern hemisphere, (3) variations within the ionosphere as part of the upper atmosphere characterized by the existence of free electrons and ions, (4) the solar wind, i.e. the permanent emission of electrons and photons, (5) the interplanetary magnetic field, and (6) electric currents, e.g. the van Allen radiation belt. It can be stated that ionosphere disturbances are often caused by so-called solar storms. A solar storm comprises solar events such as solar flares and coronal mass ejections (CMEs) which have different effects on the Earth. Solar flares may cause disturbances in positioning, navigation and communication. CMEs can effect severe disturbances and in extreme cases damages or even destructions of modern infrastructure. Examples are interruptions to satellite services including the global navigation satellite systems (GNSS), communication systems, Earth observation and imaging systems or a potential failure of power networks. Currently the measurements of solar satellite missions such as STEREO and SOHO are used to forecast solar events. Besides these measurements the Earth's ionosphere plays another key role in monitoring the space weather, because it responses to solar storms with an increase of the electron density. Space-geodetic observation techniques, such as terrestrial GNSS, satellite altimetry, space-borne GPS (radio occultation), DORIS and VLBI provide valuable global information about the state of the ionosphere. Additionally geodesy has a long history and large experience in developing and using sophisticated analysis and combination techniques as well as empirical and physical modelling approaches. Consequently, geodesy is predestinated for strongly supporting space weather monitoring via modelling the ionosphere and detecting and forecasting its disturbances. At present a couple of nations, such as the US, UK, Japan, Canada and China, are taken the threats from extreme space weather events seriously and support the development of observing strategies and fundamental research. However, (extreme) space weather events are in all their consequences on the modern highly technologized society, causative global problems which have to be treated globally and not regionally or even nationally. Consequently, space weather monitoring must include (1) all space-geodetic observation techniques and (2) geodetic evaluation methods such as data combination, real-time modelling and forecast. In other words, geodetic space weather monitoring comprises the basic ideas of GGOS and will provide products such as forecasts of severe solar events in order to initiate necessary activities to protect the infrastructure of modern society.

Study on Rainfall Forecasting by Using Weather Satellite Imagery in a Small Watershed Located at Mountainous Area of Central Taiwan

NASA Astrophysics Data System (ADS)

Wei, C.; Cheng, K. S.

Using meteorological radar and satellite imagery had become an efficient tool for rainfall forecasting However few studies were aimed to predict quantitative rainfall in small watersheds for flood forecasting by using remote sensing data Due to the terrain shelter and ground clutter effect of Central Mountain Ridges the application of meteorological radar data was limited in mountainous areas of central Taiwan This study devises a new scheme to predict rainfall of a small upstream watershed by combing GOES-9 geostationary weather satellite imagery and ground rainfall records which can be applied for local quantitative rainfall forecasting during periods of typhoon and heavy rainfall Imagery of two typhoon events in 2004 and five correspondent ground raingauges records of Chitou Forest Recreational Area which is located in upstream region of Bei-Shi river were analyzed in this study The watershed accounts for 12 7 square kilometers and altitudes ranging from 1000 m to 1800 m Basin-wide Average Rainfall BAR in study area were estimated by block kriging Cloud Top Temperature CTT from satellite imagery and ground hourly rainfall records were medium correlated The regression coefficient ranges from 0 5 to 0 7 and the value decreases as the altitude of the gauge site increases The regression coefficient of CCT and next 2 to 6 hour accumulated BAR decrease as the time scale increases The rainfall forecasting for BAR were analyzed by Kalman Filtering Technique The correlation coefficient and average hourly deviates between estimated and observed value of BAR for

Forecasting distribution of numbers of large fires

USGS Publications Warehouse

Eidenshink, Jeffery C.; Preisler, Haiganoush K.; Howard, Stephen; Burgan, Robert E.

2014-01-01

Systems to estimate forest fire potential commonly utilize one or more indexes that relate to expected fire behavior; however they indicate neither the chance that a large fire will occur, nor the expected number of large fires. That is, they do not quantify the probabilistic nature of fire danger. In this work we use large fire occurrence information from the Monitoring Trends in Burn Severity project, and satellite and surface observations of fuel conditions in the form of the Fire Potential Index, to estimate two aspects of fire danger: 1) the probability that a 1 acre ignition will result in a 100+ acre fire, and 2) the probabilities of having at least 1, 2, 3, or 4 large fires within a Predictive Services Area in the forthcoming week. These statistical processes are the main thrust of the paper and are used to produce two daily national forecasts that are available from the U.S. Geological Survey, Earth Resources Observation and Science Center and via the Wildland Fire Assessment System. A validation study of our forecasts for the 2013 fire season demonstrated good agreement between observed and forecasted values.

Application of Hydrometeorological Information for Short-term and Long-term Water Resources Management over Ungauged Basin in Korea

NASA Astrophysics Data System (ADS)

Kim, Ji-in; Ryu, Kyongsik; Suh, Ae-sook

2016-04-01

In 2014, three major governmental organizations that are Korea Meteorological Administration (KMA), K-water, and Korea Rural Community Corporation have been established the Hydrometeorological Cooperation Center (HCC) to accomplish more effective water management for scarcely gauged river basins, where data are uncertain or non-consistent. To manage the optimal drought and flood control over the ungauged river, HCC aims to interconnect between weather observations and forecasting information, and hydrological model over sparse regions with limited observations sites in Korean peninsula. In this study, long-term forecasting ensemble models so called Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, provided by KMA was used in order to produce drought outlook. Glosea5 ensemble model prediction provides predicted drought information for 1 and 3 months ahead with drought index including Standardized Precipitation Index (SPI3) and Palmer Drought Severity Index (PDSI). Also, Global Precipitation Measurement and Global Climate Observation Measurement - Water1 satellites data products are used to estimate rainfall and soil moisture contents over the ungauged region.

Transition of Suomi National Polar-Orbiting Partnership (S-NPP) Data Products for Operational Weather Forecasting Applications

NASA Technical Reports Server (NTRS)

Smith, Matthew R.; Molthan, Andrew L.; Fuell, Kevin K.; Jedlovec, Gary J.

2012-01-01

SPoRT is a team of NASA/NOAA scientists focused on demonstrating the utility of NASA and future NOAA data and derived products on improving short-term weather forecasts. Work collaboratively with a suite of unique products and selected WFOs in an end-to-end transition activity. Stable funding from NASA and NOAA. Recognized by the science community as the "go to" place for transitioning experimental and research data to the operational weather community. Endorsed by NWS ESSD/SSD chiefs. Proven paradigm for transitioning satellite observations and modeling capabilities to operations (R2O). SPoRT s transition of NASA satellite instruments provides unique or higher resolution data products to complement the baseline suite of geostationary data available to forecasters. SPoRT s partnership with NWS WFOs provides them with unique imagery to support disaster response and local forecast challenges. SPoRT has years of proven experience in developing and transitioning research products to the operational weather community. SPoRT has begun work with CONUS and OCONUS WFOs to determine the best products for maximum benefit to forecasters. VIIRS has already proven to be another extremely powerful tool, enhancing forecasters ability to handle difficult forecasting situations.

Verification of Space Weather Forecasts Issued by the Met Office Space Weather Operations Centre

NASA Astrophysics Data System (ADS)

Sharpe, M. A.; Murray, S. A.

2017-10-01

The Met Office Space Weather Operations Centre was founded in 2014 and part of its remit is a daily Space Weather Technical Forecast to help the UK build resilience to space weather impacts; guidance includes 4 day geomagnetic storm forecasts (GMSF) and X-ray flare forecasts (XRFF). It is crucial for forecasters, users, modelers, and stakeholders to understand the strengths and weaknesses of these forecasts; therefore, it is important to verify against the most reliable truth data source available. The present study contains verification results for XRFFs using Geo-Orbiting Earth Satellite 15 satellite data and GMSF using planetary K-index (Kp) values from the GFZ Helmholtz Centre. To assess the value of the verification results, it is helpful to compare them against a reference forecast and the frequency of occurrence during a rolling prediction period is used for this purpose. An analysis of the rolling 12 month performance over a 19 month period suggests that both the XRFF and GMSF struggle to provide a better prediction than the reference. However, a relative operating characteristic and reliability analysis of the full 19 month period reveals that although the GMSF and XRFF possess discriminatory skill, events tend to be overforecast.

Sea level forecasts for Pacific Islands based on Satellite Altimetry

NASA Astrophysics Data System (ADS)

Yoon, H.; Merrifield, M. A.; Thompson, P. R.; Widlansky, M. J.; Marra, J. J.

2017-12-01

Coastal flooding at tropical Pacific Islands often occurs when positive sea level anomalies coincide with high tides. To help mitigate this risk, a forecast tool for daily-averaged sea level anomalies is developed that can be added to predicted tides at tropical Pacific Island sites. The forecast takes advantage of the observed westward propagation that sea level anomalies exhibit over a range of time scales. The daily near-real time altimetry gridded data from Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) is used to specify upstream sea level at each site, with lead times computed based on mode-one baroclinic Rossby wave speeds. To validate the forecast, hindcasts are compared to tide gauge and nearby AVISO gridded time series. The forecast skills exceed persistence at most stations out to a month or more lead time. The skill is highest at stations where eddy variability is relatively weak. The impacts on the forecasts due to varying propagation speed, decay time, and smoothing of the AVISO data are examined. In addition, the inclusion of forecast winds in a forced wave equation is compared to the freely propagating results. Case studies are presented for seasonally high tide events throughout the Pacific Island region.

The use of snowcovered area in runoff forecasts

NASA Technical Reports Server (NTRS)

Rango, A.; Hannaford, J. F.; Hall, R. L.; Rosenzweig, M.; Brown, A. J.

1977-01-01

Long-term snowcovered area data from aircraft and satellite observations have proven useful in reducing seasonal runoff forecast error on the Kern river watershed. Similar use of snowcovered area on the Kings river watershed produced results that were about equivalent to methods based solely on conventional data. Snowcovered area will be most effective in reducing forecast procedural error on watersheds with: (1) a substantial amount of area within a limited elevation range; (2) an erratic precipitation and/or snowpack accumulation pattern not strongly related to elevation; and (3) poor coverage by precipitation stations or snow courses restricting adequate indexing of water supply conditions. When satellite data acquisition and delivery problems are resolved, the derived snowcover information should provide a means for enhancing operational streamflow forecasts for areas that depend primarily on snowmelt for their water supply.

Monitoring and forecasting the 2009-2010 severe drought in Southwest China

NASA Astrophysics Data System (ADS)

Zhang, X.; Tang, Q.; Liu, X.; Leng, G.; Li, Z.; Cui, H.

2015-12-01

From the fall of 2009 to the spring of 2010, an unprecedented drought swept across southwest China (SW) and led to a severe shortage in drinking water and a huge loss to regional economy. Monitoring and predicting the severe drought with several months in advance is of critical importance for such hydrological disaster assessment, preparation and mitigation. In this study, we attempted to carry out a model-based hydrological monitoring and seasonal forecasting framework, and assessed its skill in capturing the evolution of the SW drought in 2009-2010. Using the satellite-based meteorological forcings and the Variable Infiltration Capacity (VIC) hydrologic model, the drought conditions were assessed in a near-real-time manner based on a 62-year (1952-2013) retrospective simulation, wherein the satellite data was adjusted by a gauge-based forcing to remove systematic biases. Bias-corrected seasonal forecasting outputs from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) was tentatively applied for a seasonal hydrologic prediction and its predictive skill was overall evaluated relative to a traditional Ensemble Streamflow Prediction (ESP) method with lead time varying from 1 to 6 months. The results show that the climate model-driven hydrologic predictability is generally limited to 1-month lead time and exhibits negligible skill improvement relative to ESP during this drought event, suggesting the initial hydrologic conditions (IHCs) play a dominant role in forecasting performance. The research highlights the value of the framework in providing accurate IHCs in a real-time manner which will greatly benefit drought early-warning.

Implementation of the Land, Atmosphere Near Real-Time Capability for EOS (LANCE)

NASA Technical Reports Server (NTRS)

Michael, Karen; Murphy, Kevin; Lowe, Dawn; Masuoka, Edward; Vollmer, Bruce; Tilmes, Curt; Teague, Michael; Ye, Gang; Maiden, Martha; Goodman, H. Michael;

Solar power satellite

NASA Technical Reports Server (NTRS)

Davis, H. P.

1978-01-01

The solar power satellite (SPS) concept, under evaluation by NASA since 1974, is discussed. A typical system providing a total of 10,000 MW of electrical power to the ground receiving stations is considered. Energy conversion systems, including the photovoltaic device category using single-crystal silicon cells, are taken into account, as are the 2.45-GHz microwave power-transmission link and the ground receiver (or rectenna). Concepts involving space construction of the satellite's large structures (5 x 25 km) are described, noting that a process similar to the familiar roll-forming of light sheet metal parts has been adapted to the space environment. Transportation vehicles are discussed, including the Space Shuttle planned to reach 60 flights per year by the mid 1980's. Electrical power forecasts and advanced systems cost projections are analyzed, together with a description of costs estimates. The indirect economics of energy research and development, and the present NASA/DOE SPS program are noted.

Downscaling Satellite Data for Predicting Catchment-scale Root Zone Soil Moisture with Ground-based Sensors and an Ensemble Kalman Filter

NASA Astrophysics Data System (ADS)

Lin, H.; Baldwin, D. C.; Smithwick, E. A. H.

2015-12-01

Predicting root zone (0-100 cm) soil moisture (RZSM) content at a catchment-scale is essential for drought and flood predictions, irrigation planning, weather forecasting, and many other applications. Satellites, such as the NASA Soil Moisture Active Passive (SMAP), can estimate near-surface (0-5 cm) soil moisture content globally at coarse spatial resolutions. We develop a hierarchical Ensemble Kalman Filter (EnKF) data assimilation modeling system to downscale satellite-based near-surface soil moisture and to estimate RZSM content across the Shale Hills Critical Zone Observatory at a 1-m resolution in combination with ground-based soil moisture sensor data. In this example, a simple infiltration model within the EnKF-model has been parameterized for 6 soil-terrain units to forecast daily RZSM content in the catchment from 2009 - 2012 based on AMSRE. LiDAR-derived terrain variables define intra-unit RZSM variability using a novel covariance localization technique. This method also allows the mapping of uncertainty with our RZSM estimates for each time-step. A catchment-wide satellite-to-surface downscaling parameter, which nudges the satellite measurement closer to in situ near-surface data, is also calculated for each time-step. We find significant differences in predicted root zone moisture storage for different terrain units across the experimental time-period. Root mean square error from a cross-validation analysis of RZSM predictions using an independent dataset of catchment-wide in situ Time-Domain Reflectometry (TDR) measurements ranges from 0.060-0.096 cm3 cm-3, and the RZSM predictions are significantly (p < 0.05) correlated with TDR measurements [r = 0.47-0.68]. The predictive skill of this data assimilation system is similar to the Penn State Integrated Hydrologic Modeling (PIHM) system. Uncertainty estimates are significantly (p < 0.05) correlated to cross validation error during wet and dry conditions, but more so in dry summer seasons. Developing an EnKF-model system that downscales satellite data and predicts catchment-scale RZSM content is especially timely, given the anticipated release of SMAP surface moisture data in 2015.

A Real-Time California Coastal Ocean Nowcast/Forecast System: Skill Assessment, User Products, and Transition from Research to Operations

NASA Astrophysics Data System (ADS)

Farrara, J. D.; Chao, Y.; Chai, F.; Zhang, H.

2016-02-01

The real-time California coastal ocean nowcast/forecast system is described. The model is based on the Regional Ocean Modeling System (ROMS) and covers the entire California coastal ocean with a horizontal resolution of 3 km and 40 vertical layers. The atmospheric forcing is derived from the operational regional atmospheric model forecasts. The lateral boundary conditions are provided by the operational ocean model forecasts. A multi-scale 3-dimensional variational (3DVAR) data assimilation scheme is used to assimilate both in situ (e.g., vertical profiles of temperature and salinity) and remotely sensed data from both satellite (e.g., sea surface temperature and sea surface height) and land-based platforms (e.g., surface current). The performance of our nowcast/forecast system is evaluated in real-time by a number of metrics that are published as soon as they become available. User tools and products have been developed for both general users and super-users (e.g., NOAA Office of Response and Restoration and USCG). Recent results comparing the 3DVAR with the ensemble Kalman Filter (EnKF) using Data Assimilation Research Testbed (DART) will be presented. Preliminary results coupling the ROMS circulation model with a biogeochemistry/ecosystem model (i.e., CoSiNE) will also discussed. Cloud computing services (e.g., Microsoft, Google) are now being tested to increase the reliability and timeliness in order to be accepted as a truly operational system in the near future.

Intercomparison of Operational Ocean Forecasting Systems in the framework of GODAE

NASA Astrophysics Data System (ADS)

Hernandez, F.

2009-04-01

One of the main benefits of the GODAE 10-year activity is the implementation of ocean forecasting systems in several countries. In 2008, several systems are operated routinely, at global or basin scale. Among them, the BLUElink (Australia), HYCOM (USA), MOVE/MRI.COM (Japan), Mercator (France), FOAM (United Kingdom), TOPAZ (Norway) and C-NOOFS (Canada) systems offered to demonstrate their operational feasibility by performing an intercomparison exercise during a three months period (February to April 2008). The objectives were: a) to show that operational ocean forecasting systems are operated routinely in different countries, and that they can interact; b) to perform in a similar way a scientific validation aimed to assess the quality of the ocean estimates, the performance, and forecasting capabilities of each system; and c) to learn from this intercomparison exercise to increase inter-operability and collaboration in real time. The intercomparison relies on the assessment strategy developed for the EU MERSEA project, where diagnostics over the global ocean have been revisited by the GODAE contributors. This approach, based on metrics, allow for each system: a) to verify if ocean estimates are consistent with the current general knowledge of the dynamics; and b) to evaluate the accuracy of delivered products, compared to space and in-situ observations. Using the same diagnostics also allows one to intercompare the results from each system consistently. Water masses and general circulation description by the different systems are consistent with WOA05 Levitus climatology. The large scale dynamics (tropical, subtropical and subpolar gyres ) are also correctly reproduced. At short scales, benefit of high resolution systems can be evidenced on the turbulent eddy field, in particular when compared to eddy kinetic energy deduced from satellite altimetry of drifter observations. Comparisons to high resolution SST products show some discrepancies on ocean surface representation, either due to model and forcing fields errors, or assimilation scheme efficiency. Comparisons to sea-ice satellite products also evidence discrepancies linked to model, forcing and assimilation strategies of each forecasting system. Key words: Intercomparison, ocean analysis, operational oceanography, system assessment, metrics, validation GODAE Intercomparison Team: L. Bertino (NERSC/Norway), G. Brassington (BMRC/Australia), E. Chassignet (FSU/USA), J. Cummings (NRL/USA), F. Davidson (DFO/Canda), M. Drévillon (CERFACS/France), P. Hacker (IPRC/USA), M. Kamachi (MRI/Japan), J.-M. Lellouche (CERFACS/France), K. A. Lisæter (NERSC/Norway), R. Mahdon (UKMO/UK), M. Martin (UKMO/UK), A. Ratsimandresy (DFO/Canada), and C. Regnier (Mercator Ocean/France)

An Operational System for Surveillance and Ecological Forecasting of West Nile Virus Outbreaks

NASA Astrophysics Data System (ADS)

Wimberly, M. C.; Davis, J. K.; Vincent, G.; Hess, A.; Hildreth, M. B.

2017-12-01

Mosquito-borne disease surveillance has traditionally focused on tracking human cases along with the abundance and infection status of mosquito vectors. For many of these diseases, vector and host population dynamics are also sensitive to climatic factors, including temperature fluctuations and the availability of surface water for mosquito breeding. Thus, there is a potential to strengthen surveillance and predict future outbreaks by monitoring environmental risk factors using broad-scale sensor networks that include earth-observing satellites. The South Dakota Mosquito Information System (SDMIS) project combines entomological surveillance with gridded meteorological data from NASA's North American Land Data Assimilation System (NLDAS) to generate weekly risk maps for West Nile virus (WNV) in the north-central United States. Critical components include a mosquito infection model that smooths the noisy infection rate and compensates for unbalanced sampling, and a human infection model that combines the entomological risk estimates with lagged effects of meteorological variables from the North American Land Data Assimilation System (NLDAS). Two types of forecasts are generated: long-term forecasts of statewide risk extending through the entire WNV season, and short-term forecasts of the geographic pattern of WNV risk in the upcoming week. Model forecasts are connected to public health actions through decision support matrices that link predicted risk levels to a set of phased responses. In 2016, the SDMIS successfully forecast an early start to the WNV season and a large outbreak of WNV cases following several years of low transmission. An evaluation of the 2017 forecasts will also be presented. Our experiences with the SDMIS highlight several important lessons that can inform future efforts at disease early warning. These include the value of integrating climatic models with recent observations of infection, the critical role of automated workflows to facilitate the timely integration of multiple data streams, the need for effective synthesis and visualization of forecasts, and the importance of linking forecasts to specific public health responses.

GOES-S Mission Science Briefing

NASA Image and Video Library

2018-02-27

In the Kennedy Space Center's Press Site auditorium, Dan Lindsey, GOES-R senior scientific advisor for NOAA, speaks to members of the media at a mission briefing on National Oceanic and Atmospheric Administration's, or NOAA's, Geostationary Operational Environmental Satellite, or GOES-S. The spacecraft is the second satellite in a series of next-generation NOAA weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting. GOES-S is slated to lift off at 5:02 p.m. EST on March 1, 2018 aboard a United Launch Alliance Atlas V rocket.

GOES-S NASA Social

NASA Image and Video Library

2018-02-28

Jason Townsend, NASA's social media manager, speaks to members of social media in the Kennedy Space Center’s Press Site auditorium. The briefing focused on the National Oceanic and Atmospheric Administration's, or NOAA's, Geostationary Operational Environmental Satellite, or GOES-S. The spacecraft is the second satellite in a series of next-generation NOAA weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting. GOES-S is slated to lift off at 5:02 p.m. EST on March 1, 2018 aboard a United Launch Alliance Atlas V rocket.

GOES-S Mission Science Briefing

NASA Image and Video Library

2018-02-27

In the Kennedy Space Center's Press Site auditorium, Steve Cole of NASA Communications speaks to members of the media at a mission briefing on National Oceanic and Atmospheric Administration's, or NOAA's, Geostationary Operational Environmental Satellite, or GOES-S. The spacecraft is the second satellite in a series of next-generation NOAA weather satellites. It will launch to a geostationary position over the U.S. to provide images of storms and help predict weather forecasts, severe weather outlooks, watches, warnings, lightning conditions and longer-term forecasting. GOES-S is slated to lift off at 5:02 p.m. EST on March 1, 2018 aboard a United Launch Alliance Atlas V rocket.

Challenges for operational forecasting and early warning of rainfall induced landslides

NASA Astrophysics Data System (ADS)

Guzzetti, Fausto

2017-04-01

In many areas of the world, landslides occur every year, claiming lives and producing severe economic and environmental damage. Many of the landslides with human or economic consequences are the result of intense or prolonged rainfall. For this reason, in many areas the timely forecast of rainfall-induced landslides is of both scientific interest and social relevance. In the recent years, there has been a mounting interest and an increasing demand for operational landslide forecasting, and for associated landslide early warning systems. Despite the relevance of the problem, and the increasing interest and demand, only a few systems have been designed, and are currently operated. Inspection of the - limited - literature on operational landslide forecasting, and on the associated early warning systems, reveals that common criteria and standards for the design, the implementation, the operation, and the evaluation of the performances of the systems, are lacking. This limits the possibility to compare and to evaluate the systems critically, to identify their inherent strengths and weaknesses, and to improve the performance of the systems. Lack of common criteria and of established standards can also limit the credibility of the systems, and consequently their usefulness and potential practical impact. Landslides are very diversified phenomena, and the information and the modelling tools used to attempt landslide forecasting vary largely, depending on the type and size of the landslides, the extent of the geographical area considered, the timeframe of the forecasts, and the scope of the predictions. Consequently, systems for landslide forecasting and early warning can be designed and implemented at several different geographical scales, from the local (site or slope specific) to the regional, or even national scale. The talk focuses on regional to national scale landslide forecasting systems, and specifically on operational systems based on empirical rainfall threshold models. Building on the experience gained in designing, implementing, and operating national and regional landslide forecasting systems in Italy, and on a preliminary review of the existing literature on regional landslide early warning systems, the talk discusses concepts, limitations and challenges inherent to the design of reliable forecasting and early warning systems for rainfall-triggered landslides, the evaluation of the performances of the systems, and on problems related to the use of the forecasts and the issuing of landslide warnings. Several of the typical elements of an operational landslide forecasting system are considered, including: (i) the rainfall and landslide information used to establish the threshold models, (ii) the methods and tools used to define the empirical rainfall thresholds, and their associated uncertainty, (iii) the quality (e.g., the temporal and spatial resolution) of the rainfall information used for operational forecasting, including rain gauge and radar measurements, satellite estimates, and quantitative weather forecasts, (iv) the ancillary information used to prepare the forecasts, including e.g., the terrain subdivisions and the landslide susceptibility zonations, (v) the criteria used to transform the forecasts into landslide warnings and the methods used to communicate the warnings, and (vi) the criteria and strategies adopted to evaluate the performances of the systems, and to define minimum or optimal performance levels.

Improving Satellite Observation Utilization for Model Initialization with Machine Learning: An Introduction and Tackling the "Labeled Dataset" Challenge for Cyclones Around the World

NASA Astrophysics Data System (ADS)

Bonfanti, C. E.; Stewart, J.; Lee, Y. J.; Govett, M.; Trailovic, L.; Etherton, B.

2017-12-01

One of the National Oceanic and Atmospheric Administration (NOAA) goals is to provide timely and reliable weather forecasts to support important decisions when and where people need it for safety, emergencies, planning for day-to-day activities. Satellite data is essential for areas lacking in-situ observations for use as initial conditions in Numerical Weather Prediction (NWP) Models, such as spans of the ocean or remote areas of land. Currently only about 7% of total received satellite data is selected for use and from that, an even smaller percentage ever are assimilated into NWP models. With machine learning, the computational and time costs needed for satellite data selection can be greatly reduced. We study various machine learning approaches to process orders of magnitude more satellite data in significantly less time allowing for a greater quantity and more intelligent selection of data to be used for assimilation purposes. Given the future launches of satellites in the upcoming years, machine learning is capable of being applied for better selection of Regions of Interest (ROI) in the magnitudes more of satellite data that will be received. This paper discusses the background of machine learning methods as applied to weather forecasting and the challenges of creating a "labeled dataset" for training and testing purposes. In the training stage of supervised machine learning, labeled data are important to identify a ROI as either true or false so that the model knows what signatures in satellite data to identify. Authors have selected cyclones, including tropical cyclones and mid-latitude lows, as ROI for their machine learning purposes and created a labeled dataset of true or false for ROI from Global Forecast System (GFS) reanalysis data. A dataset like this does not yet exist and given the need for a high quantity of samples, is was decided this was best done with automation. This process was done by developing a program similar to the National Center for Environmental Prediction (NCEP) tropical cyclone tracker by Marchok that was used to identify cyclones based off its physical characteristics. We will discuss the methods and challenges to creating this dataset and the dataset's use for our current supervised machine learning model as well as use for future work on events such as convection initiation.

Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory

NASA Astrophysics Data System (ADS)

Dumitrache, Rodica Claudia; Iriza, Amalia; Maco, Bogdan Alexandru; Barbu, Cosmin Danut; Hirtl, Marcus; Mantovani, Simone; Nicola, Oana; Irimescu, Anisoara; Craciunescu, Vasile; Ristea, Alina; Diamandi, Andrei

2016-10-01

The numerical forecast of particulate matter concentrations in general, and PM10 in particular is a theme of high socio-economic relevance. The aim of this study was to investigate the impact of ground and satellite data assimilation of PM10 observations into the Weather Research and Forecasting model coupled with Chemistry (WRF-CHEM) numerical air quality model for Romanian territory. This is the first initiative of the kind for this domain of interest. Assimilation of satellite information - e.g. AOT's in air quality models is of interest due to the vast spatial coverage of the observations. Support Vector Regression (SVR) techniques are used to estimate the PM content from heterogeneous data sources, including EO products (Aerosol Optical Thickness), ground measurements and numerical model data (temperature, humidity, wind, etc.). In this study we describe the modeling framework employed and present the evaluation of the impact from the data assimilation of PM10 observations on the forecast of the WRF-CHEM model. Integrations of the WRF-CHEM model in data assimilation enabled/disabled configurations allowed the evaluation of satellite and ground data assimilation impact on the PM10 forecast performance for the Romanian territory. The model integration and evaluation were performed for two months, one in winter conditions (January 2013) and one in summer conditions (June 2013).

Solar radio proxies for improved satellite orbit prediction

NASA Astrophysics Data System (ADS)

Yaya, Philippe; Hecker, Louis; Dudok de Wit, Thierry; Fèvre, Clémence Le; Bruinsma, Sean

2017-12-01

Specification and forecasting of solar drivers to thermosphere density models is critical for satellite orbit prediction and debris avoidance. Satellite operators routinely forecast orbits up to 30 days into the future. This requires forecasts of the drivers to these orbit prediction models such as the solar Extreme-UV (EUV) flux and geomagnetic activity. Most density models use the 10.7 cm radio flux (F10.7 index) as a proxy for solar EUV. However, daily measurements at other centimetric wavelengths have also been performed by the Nobeyama Radio Observatory (Japan) since the 1950's, thereby offering prospects for improving orbit modeling. Here we present a pre-operational service at the Collecte Localisation Satellites company that collects these different observations in one single homogeneous dataset and provides a 30 days forecast on a daily basis. Interpolation and preprocessing algorithms were developed to fill in missing data and remove anomalous values. We compared various empirical time series prediction techniques and selected a multi-wavelength non-recursive analogue neural network. The prediction of the 30 cm flux, and to a lesser extent that of the 10.7 cm flux, performs better than NOAA's present prediction of the 10.7 cm flux, especially during periods of high solar activity. In addition, we find that the DTM-2013 density model (Drag Temperature Model) performs better with (past and predicted) values of the 30 cm radio flux than with the 10.7 flux.

AIRS Retrieval Validation During the EAQUATE

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Smith, William L.; Cuomo, Vincenzo; Taylor, Jonathan P.; Barnet, Christopher D.; DiGirolamo, Paolo; Pappalardo, Gelsomina; Larar, Allen M.; Liu, Xu; Newman, Stuart M.

2006-01-01

Atmospheric and surface thermodynamic parameters retrieved with advanced hyperspectral remote sensors of Earth observing satellites are critical for weather prediction and scientific research. The retrieval algorithms and retrieved parameters from satellite sounders must be validated to demonstrate the capability and accuracy of both observation and data processing systems. The European AQUA Thermodynamic Experiment (EAQUATE) was conducted mainly for validation of the Atmospheric InfraRed Sounder (AIRS) on the AQUA satellite, but also for assessment of validation systems of both ground-based and aircraft-based instruments which will be used for other satellite systems such as the Infrared Atmospheric Sounding Interferometer (IASI) on the European MetOp satellite, the Cross-track Infrared Sounder (CrIS) from the NPOESS Preparatory Project and the following NPOESS series of satellites. Detailed inter-comparisons were conducted and presented using different retrieval methodologies: measurements from airborne ultraspectral Fourier transform spectrometers, aircraft in-situ instruments, dedicated dropsondes and radiosondes, and ground based Raman Lidar, as well as from the European Center for Medium range Weather Forecasting (ECMWF) modeled thermal structures. The results of this study not only illustrate the quality of the measurements and retrieval products but also demonstrate the capability of these validation systems which are put in place to validate current and future hyperspectral sounding instruments and their scientific products.

An Improved Ocean Observing System for Coastal Louisiana: WAVCIS (WAVE-CURRENT-SURGE Information System )

NASA Astrophysics Data System (ADS)

Zhang, X.; Stone, G. W.; Gibson, W. J.; Braud, D.

2005-05-01

WAVCIS is a regional ocean observing and forecasting system. It was designed to measure, process, forecast, and distribute oceanographic and meteorological information. WAVCIS was developed and is maintained by the Coastal Studies Institute at Louisiana State University. The in-situ observing stations are distributed along the central Louisiana and Mississippi coast. The forecast region covers the entire Gulf of Mexico with emphasis on offshore Louisiana. By using state-of-the-art instrumentation, WAVCIS measures directional waves, currents, temperature, water level, conductivity, turbidity, salinity, dissolved oxygen, chlorophyll, Meteorological parameters include wind speed and direction, air pressure and temperature visibility and humidity. Through satellite communication links, the measured data are transmitted to the WAVCIS laboratory. After processing, they are available to the public via the internet on a near real-time basis. WAVCIS also includes a forecasting capability. Waves, tides, currents, and winds are forecast daily for up to 80 hours in advance. There are a number of numerical wave and surge models that can be used for forecasts. WAM and SWAN are used for operational purposes to forecast sea state. Tides at each station are predicted based on the harmonic constants calculated from past in-situ observations at respective sites. Interpolated winds from the ETA model are used as input forcing for waves. Both in-situ and forecast information are available online to the users through WWW. Interactive GIS web mapping is implemented on the WAVCIS webpage to visualize the model output and in-situ observational data. WAVCIS data can be queried, retrieved, downloaded, and analyzed through the web page. Near real-time numerical model skill assessment can also be performed by using the data from in-situ observing stations.

The GOES-R/JPSS Approach for Identifying Hazardous Low Clouds: Overview and Operational Impacts

NASA Astrophysics Data System (ADS)

Calvert, Corey; Pavolonis, Michael; Lindstrom, Scott; Gravelle, Chad; Terborg, Amanda

2017-04-01

Low ceiling and visibility is a weather hazard that nearly every forecaster, in nearly every National Weather Service (NWS) Weather Forecast Office (WFO), must regularly address. In addition, national forecast centers such as the Aviation Weather Center (AWC), Alaska Aviation Weather Unit (AAWU) and the Ocean Prediction Center (OPC) are responsible for issuing low ceiling and visibility related products. As such, reliable methods for detecting and characterizing hazardous low clouds are needed. Traditionally, hazardous areas of Fog/Low Stratus (FLS) are identified using a simple stand-alone satellite product that is constructed by subtracting the 3.9 and 11 μm brightness temperatures. However, the 3.9-11 μm brightness temperature difference (BTD) has several major limitations. In an effort to address the limitations of the BTD product, the GOES-R Algorithm Working Group (AWG) developed an approach that fuses satellite, Numerical Weather Prediction (NWP) model, Sea Surface Temperature (SST) analyses, and other data sets (e.g. digital surface elevation maps, surface emissivity maps, and surface type maps) to determine the probability that hazardous low clouds are present using a naïve Bayesian classifier. In addition, recent research has focused on blending geostationary (e.g. GOES-R) and low earth orbit (e.g. JPSS) satellite data to further improve the products. The FLS algorithm has adopted an enterprise approach in that it can utilize satellite data from a variety of current and future operational sensors and NWP data from a variety of models. The FLS products are available in AWIPS/N-AWIPS/AWIPS-II and have been evaluated within NWS operations over the last four years as part of the Satellite Proving Ground. Forecaster feedback has been predominantly positive and references to these products within Area Forecast Discussions (AFD's) indicate that the products are influencing operational forecasts. At the request of the NWS, the FLS products are currently being transitioned to NOAA/NESDIS operations, which will ensure that users have long-term access to these products. This paper will provide an overview of the FLS products and illustrate how they are being used to improve transportation safety and efficiency.

Applications of Experimental Suomi-NPP VIIRS Flood Inundation Maps in Operational Flood Forecasting

NASA Astrophysics Data System (ADS)

Deweese, M. M.

2017-12-01

Flooding is the most costly natural disaster across the globe. In 2016 flooding caused more fatalities than any other natural disaster in the United States. The U.S. National Weather Service (NWS) is mandated to forecast rivers for the protection of life and property and the enhancement of the national economy. Since 2014, the NWS North Central River Forecast Center has utilized experimental near real time flood mapping products from the JPSS Suomi-NPP VIIRS satellite. These products have been demonstrated to provide reliable and high value information for forecasters in ice jam and snowmelt flooding in data sparse regions of the northern plains. In addition, they have proved valuable in rainfall induced flooding within the upper Mississippi River basin. Aerial photography and ground observations have validated the accuracy of the products. Examples are provided from numerous flooding events to demonstrate the operational application of this satellite derived information as a remotely sensed observational data source and it's utility in real time flood forecasting.

Nowcasting of deep convective clouds and heavy precipitation: Comparison study between NWP model simulation and extrapolation

NASA Astrophysics Data System (ADS)

Bližňák, Vojtěch; Sokol, Zbyněk; Zacharov, Petr

2017-02-01

An evaluation of convective cloud forecasts performed with the numerical weather prediction (NWP) model COSMO and extrapolation of cloud fields is presented using observed data derived from the geostationary satellite Meteosat Second Generation (MSG). The present study focuses on the nowcasting range (1-5 h) for five severe convective storms in their developing stage that occurred during the warm season in the years 2012-2013. Radar reflectivity and extrapolated radar reflectivity data were assimilated for at least 6 h depending on the time of occurrence of convection. Synthetic satellite imageries were calculated using radiative transfer model RTTOV v10.2, which was implemented into the COSMO model. NWP model simulations of IR10.8 μm and WV06.2 μm brightness temperatures (BTs) with a horizontal resolution of 2.8 km were interpolated into the satellite projection and objectively verified against observations using Root Mean Square Error (RMSE), correlation coefficient (CORR) and Fractions Skill Score (FSS) values. Naturally, the extrapolation of cloud fields yielded an approximately 25% lower RMSE, 20% higher CORR and 15% higher FSS at the beginning of the second forecasted hour compared to the NWP model forecasts. On the other hand, comparable scores were observed for the third hour, whereas the NWP forecasts outperformed the extrapolation by 10% for RMSE, 15% for CORR and up to 15% for FSS during the fourth forecasted hour and 15% for RMSE, 27% for CORR and up to 15% for FSS during the fifth forecasted hour. The analysis was completed by a verification of the precipitation forecasts yielding approximately 8% higher RMSE, 15% higher CORR and up to 45% higher FSS when the NWP model simulation is used compared to the extrapolation for the first hour. Both the methods yielded unsatisfactory level of precipitation forecast accuracy from the fourth forecasted hour onward.

Mapping and Visualization of The Deepwater Horizon Oil Spill Using Satellite Imagery

NASA Astrophysics Data System (ADS)

Ferreira Pichardo, E.

2017-12-01

Satellites are man-made objects hovering around the Earth's orbit and are essential for Earth observation, i.e. the monitoring and gathering of data about the Earth's vital systems. Environmental Satellites are used for atmospheric research, weather forecasting, and warning as well as monitoring extreme weather events. These satellites are categorized into Geosynchronous and Low Earth (Polar) orbiting satellites. Visualizing satellite data is critical to understand the Earth's systems and changes to our environment. The objective of this research is to examine satellite-based remotely sensed data that needs to be processed and rendered in the form of maps or other forms of visualization to understand and interpret the satellites' observations to monitor the status, changes and evolution of the mega-disaster Deepwater Horizon Spill that occurred on April 20, 2010 in the Gulf of Mexico. In this project, we will use an array of tools and programs such as Python, CSPP and Linux. Also, we will use data from the National Oceanic and Atmospheric Administration (NOAA): Polar-Orbiting Satellites Terra Earth Observing System AM-1 (EOS AM-1), and Aqua EOS PM-1 to investigate the mega-disaster. Each of these satellites carry a variety of instruments, and we will use the data obtained from the remote sensor Moderate-Resolution Imaging Spectroradiometer (MODIS). Ultimately, this study shows the importance of mapping and visualizing data such as satellite data (MODIS) to understand the extents of environmental impacts disasters such as the Deepwater Horizon Oil spill.

Variational Assimilation of Global Microwave Rainfall Retrievals: Physical and Dynamical Impact on GEOS Analyses and Forecasts

NASA Technical Reports Server (NTRS)

Lin, Xin; Zhang, Sara Q.; Hou, Arthur Y.

2006-01-01

Global microwave rainfall retrievals from a 5-satellite constellation, including TMI from TRMM, SSWI from DMSP F13, F14 and F15, and AMSR-E from EOS-AQUA, are assimilated into the NASA Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) using a 1-D variational continuous assimilation (VCA) algorithm. The physical and dynamical impact of rainfall assimilation on GEOS analyses and forecasts is examined at various temporal and spatial scales. This study demonstrates that the 1-D VCA algorithm, which was originally developed and evaluated for rainfall assimilations over tropical oceans, can effectively assimilate satellite microwave rainfall retrievals and improve GEOS analyses over both the Tropics and the extratropics where the atmospheric processes are dominated by different large-scale dynamics and moist physics, and also over the land, where rainfall estimates from passive microwave radiometers are believed to be less accurate. Results show that rainfall assimilation renders the GEOS analysis physically and dynamically more consistent with the observed precipitation at the monthly-mean and 6-hour time scales. Over regions where the model precipitation tends to misbehave in distinctly different rainy regimes, the 1-D VCA algorithm, by compensating for errors in the model s moist time-tendency in a 6-h analysis window, is able to bring the rainfall analysis closer to the observed. The radiation and cloud fields also tend to be in better agreement with independent satellite observations in the rainfall-assimilation m especially over regions where rainfall analyses indicate large improvements. Assimilation experiments with and without rainfall data for a midlatitude frontal system clearly indicates that the GEOS analysis is improved through changes in the thermodynamic and dynamic fields that respond to the rainfall assimilation. The synoptic structures of temperature, moisture, winds, divergence, and vertical motion, as well as vorticity are more realistically captured across the front. Short-term forecasts using initial conditions assimilated with rainfall data also show slight improvements. 1

DAPAGLOCO - A global daily precipitation dataset from satellite and rain-gauge measurements

NASA Astrophysics Data System (ADS)

Spangehl, T.; Danielczok, A.; Dietzsch, F.; Andersson, A.; Schroeder, M.; Fennig, K.; Ziese, M.; Becker, A.

2017-12-01

The BMBF funded project framework MiKlip(Mittelfristige Klimaprognosen) develops a global climate forecast system on decadal time scales for operational applications. Herein, the DAPAGLOCO project (Daily Precipitation Analysis for the validation of Global medium-range Climate predictions Operationalized) provides a global precipitation dataset as a combination of microwave-based satellite measurements over ocean and rain gauge measurements over land on daily scale. The DAPAGLOCO dataset is created for the evaluation of the MiKlip forecast system in the first place. The HOAPS dataset (Hamburg Ocean Atmosphere Parameter and Fluxes from Satellite data) is used for the derivation of precipitation rates over ocean and is extended by the use of measurements from TMI, GMI, and AMSR-E, in addition to measurements from SSM/I and SSMIS. A 1D-Var retrieval scheme is developed to retrieve rain rates from microwave imager data, which also allows for the determination of uncertainty estimates. Over land, the GPCC (Global Precipitation Climatology Center) Full Data Daily product is used. It consists of rain gauge measurements that are interpolated on a regular grid by ordinary Kriging. The currently available dataset is based on a neuronal network approach, consists of 21 years of data from 1988 to 2008 and is currently extended until 2015 using the 1D-Var scheme and with improved sampling. Three different spatial resolved dataset versions are available with 1° and 2.5° global, and 0.5° for Europe. The evaluation of the MiKlip forecast system by DAPAGLOCO is based on ETCCDI (Expert Team on Climate Change and Detection Indices). Hindcasts are used for the index-based comparison between model and observations. These indices allow for the evaluation of precipitation extremes, their spatial and temporal distribution as well as for the duration of dry and wet spells, average precipitation amounts and percentiles on global scale. Besides, an ETCCDI-based climatology of the DAPAGLOCO precipitation dataset has been derived.

Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago.

PubMed

Caldwell, Jamie M; Heron, Scott F; Eakin, C Mark; Donahue, Megan J

2016-02-01

Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as coral reefs in the Hawaiian archipelago. Warming ocean temperature has increased coral disease outbreaks contributing to declines in coral cover worldwide. In this study we investigated seasonal effects of thermal stress on the prevalence of the three most widespread coral diseases in Hawai'i: Montipora white syndrome, Porites growth anomalies and Porites tissue loss syndrome. To predict outbreak likelihood we compared disease prevalence from surveys conducted between 2004 and 2015 from 18 Hawaiian Islands and atolls with biotic (e.g., coral density) and abiotic (satellite-derived sea surface temperature metrics) variables using boosted regression trees. To date, the only coral disease forecast models available were developed for Acropora white syndrome on the Great Barrier Reef (GBR). Given the complexities of disease etiology, differences in host demography and environmental conditions across reef regions, it is important to refine and adapt such models for different diseases and geographic regions of interest. Similar to the Acropora white syndrome models, anomalously warm conditions were important for predicting Montipora white syndrome, possibly due to a relationship between thermal stress and a compromised host immune system. However, coral density and winter conditions were the most important predictors of all three coral diseases in this study, enabling development of a forecasting system that can predict regions of elevated disease risk up to six months before an expected outbreak. Our research indicates satellite-derived systems for forecasting disease outbreaks can be appropriately adapted from the GBR tools and applied for a variety of diseases in a new region. These models can be used to enhance management capacity to prepare for and respond to emerging coral diseases throughout Hawai'i and can be modified for other diseases and regions around the world.

Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago

PubMed Central

Caldwell, Jamie M.; Heron, Scott F.; Eakin, C. Mark; Donahue, Megan J.

2017-01-01

Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as coral reefs in the Hawaiian archipelago. Warming ocean temperature has increased coral disease outbreaks contributing to declines in coral cover worldwide. In this study we investigated seasonal effects of thermal stress on the prevalence of the three most widespread coral diseases in Hawai’i: Montipora white syndrome, Porites growth anomalies and Porites tissue loss syndrome. To predict outbreak likelihood we compared disease prevalence from surveys conducted between 2004 and 2015 from 18 Hawaiian Islands and atolls with biotic (e.g., coral density) and abiotic (satellite-derived sea surface temperature metrics) variables using boosted regression trees. To date, the only coral disease forecast models available were developed for Acropora white syndrome on the Great Barrier Reef (GBR). Given the complexities of disease etiology, differences in host demography and environmental conditions across reef regions, it is important to refine and adapt such models for different diseases and geographic regions of interest. Similar to the Acropora white syndrome models, anomalously warm conditions were important for predicting Montipora white syndrome, possibly due to a relationship between thermal stress and a compromised host immune system. However, coral density and winter conditions were the most important predictors of all three coral diseases in this study, enabling development of a forecasting system that can predict regions of elevated disease risk up to six months before an expected outbreak. Our research indicates satellite-derived systems for forecasting disease outbreaks can be appropriately adapted from the GBR tools and applied for a variety of diseases in a new region. These models can be used to enhance management capacity to prepare for and respond to emerging coral diseases throughout Hawai’i and can be modified for other diseases and regions around the world. PMID:29071133

SEASAT economic assessment. Volume 2: The SEASAT system description and performance. [performance prediction and systems analysis for seasat satellites

NASA Technical Reports Server (NTRS)

1975-01-01

Results are presented of preliminary trade-off studies of operational SEASAT systems. The trade-off studies were used as the basis for the estimation of costs and net benefits of the operational SEASAT system. Also presented are the preliminary results of simulation studies that were designed to lead to a measure of the impact of SEASAT data through the use of numerical weather forecast models.

The sixth conference on satellite meteorology and oceanography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hauth, F.F.; Purdom, J.F.W.

The Sixth Conference on Satellite Meteorology and Oceanography was held in conjunction with the AMS Annual Meeting in Atlanta, Georgia, the week of 6 January 1992. Over 150 scientific papers were presented orally or in poster sessions. Joint sessions were held with the Symposium on Weather Forecasting and the Eighth International Conference on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology. The quality of the papers in the preprint volume, as well as in the presentations at both oral and poster sessions, reflects the robustness of national and international operational and research interests in satellite meteorology and oceanography.more » A preprint volume for this conference is available through the AMS.« less

Evaluating NMME Seasonal Forecast Skill for use in NASA SERVIR Hub Regions

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Roberts, Franklin R.

2013-01-01

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The coupled forecasts have numerous potential applications, both national and international in scope. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in driving applications models in hub regions including East Africa, the Hindu Kush- Himalayan (HKH) region and Mesoamerica. A prerequisite for seasonal forecast use in application modeling (e.g. hydrology, agriculture) is bias correction and skill assessment. Efforts to address systematic biases and multi-model combination in support of NASA SERVIR impact modeling requirements will be highlighted. Specifically, quantilequantile mapping for bias correction has been implemented for all archived NMME hindcasts. Both deterministic and probabilistic skill estimates for raw, bias-corrected, and multi-model ensemble forecasts as a function of forecast lead will be presented for temperature and precipitation. Complementing this statistical assessment will be case studies of significant events, for example, the ability of the NMME forecasts suite to anticipate the 2010/2011 drought in the Horn of Africa and its relationship to evolving SST patterns.

Applications of TRMM-based Multi-Satellite Precipitation Estimation for Global Runoff Simulation: Prototyping a Global Flood Monitoring System

NASA Technical Reports Server (NTRS)

Hong, Yang; Adler, Robert F.; Huffman, George J.; Pierce, Harold

2008-01-01

Advances in flood monitoring/forecasting have been constrained by the difficulty in estimating rainfall continuously over space (catchment-, national-, continental-, or even global-scale areas) and flood-relevant time scale. With the recent availability of satellite rainfall estimates at fine time and space resolution, this paper describes a prototype research framework for global flood monitoring by combining real-time satellite observations with a database of global terrestrial characteristics through a hydrologically relevant modeling scheme. Four major components included in the framework are (1) real-time precipitation input from NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA); (2) a central geospatial database to preprocess the land surface characteristics: water divides, slopes, soils, land use, flow directions, flow accumulation, drainage network etc.; (3) a modified distributed hydrological model to convert rainfall to runoff and route the flow through the stream network in order to predict the timing and severity of the flood wave, and (4) an open-access web interface to quickly disseminate flood alerts for potential decision-making. Retrospective simulations for 1998-2006 demonstrate that the Global Flood Monitor (GFM) system performs consistently at both station and catchment levels. The GFM website (experimental version) has been running at near real-time in an effort to offer a cost-effective solution to the ultimate challenge of building natural disaster early warning systems for the data-sparse regions of the world. The interactive GFM website shows close-up maps of the flood risks overlaid on topography/population or integrated with the Google-Earth visualization tool. One additional capability, which extends forecast lead-time by assimilating QPF into the GFM, also will be implemented in the future.

Transitioning the Rice Realtime Forecast Models to DSCOVR

NASA Astrophysics Data System (ADS)

Bala, R.; Reiff, P. H.

2016-12-01

The Rice realtime forecast models of global magnetospheric indices Kp, Dst and AE have been actively running at mms.rice.edu/realtime/forecast.html for nearly a decade now. These neural network models were trained using the ACE archival solar wind data while the near-realtime forecasts are provided using instantaneous upwind solar wind data stream measured at the L1 point through ACE. Additionally, the webpage also provide status of the current space weather condition as an additional resource, updating every ten minutes. Furthermore, the subscribers of our space weather alert system, called `spacalrt', have been receiving email notices based on predefined thresholds. One of the gaps that is currently seen in the Rice neural network models lies in the density dependent models using variants of the solar wind pressure. The anomalous behavior in reporting densities in ACE has been a common issue for some time now. Often such behavior is observed when the solar energetic particle that are associated with solar flares or CMEs are Earth directed. Therefore, it is understood that the subsequent measures of the density reported by ACE will be either very low or, at a minimum, contaminated. Under these circumstances, the density-based Rice models typically underpredict. However, the newly launched DSCOVR satellite will help enhance our prediction models with high-quality data; it has real time space weather data available through the NOAA's Space Weather Prediction Center as of July, 2016. We are in the process of transitioning our forecast operations to include data from DSCOVR while running the original ACE data stream in parallel until it lasts. This paper will compare and contrast the forecasted values from the two satellites. Finally, we will discuss our efforts in providing the forecast products for the Rice space weather website that will be a part of the book on "Machine Learning Techniques for Space Weather" to be published by Elsiever.

MetEd Resources for Embracing Advances with S-NPP and JPSS

NASA Astrophysics Data System (ADS)

Abshire, W. E.; Dills, P. N.; Weingroff, M.

2014-12-01

The COMET® Program (www.comet.ucar.edu), a part of the UCAR Community Programs (UCP) at UCAR, receives funding from NOAA NESDIS as well as EUMETSAT and the Meteorological Service of Canada to support education and training in satellite meteorology. For many years COMET's satellite education programs have focused on developing self-paced online educational materials that highlight the capabilities and applications of current and next-generation operational geostationary and polar-orbiting satellites and their relevance to operational forecasters and other user communities. By partnering with experts from the Naval Research Laboratory, NOAA-NESDIS and its Cooperative Institutes, Meteorological Service of Canada, EUMETSAT, and other user communities, COMET stimulates greater use of current and future satellite observations and products. This presentation provides a tour of COMET's satellite training and education offerings that are directly applicable to data and products from the S-NPP and JPSS satellite series. A recommended set of lessons for users who wish to learn more will be highlighted, including excerpts from the newest materials on the Suomi NPP VIIRS imager and its applications, as well as advances in nighttime visible observation with the VIIRS Day-Night Band. We'll show how the lessons introduce users to the advances these systems bring to forecasting, numerical weather prediction, and environmental monitoring. Over 90 satellite-focused, self-paced, online materials are freely available on the of the MetEd Web site (http://www.meted.ucar.edu) via the "Education & Training", "Satellite" topic area. Quite a few polar-orbiting-related lessons are available in both English, Spanish, and French. Additionally, S-NPP and JPSS relevant information can also be found on the the Environmental Satellite Resource Center (ESRC) Web site (www.meted.ucar.edu/esrc) that is maintained by COMET. The ESRC is a searchable, database-driven Web site that provides access to nearly 600 education, training, and informational resources on Earth-observing satellites.

In Brief: Congressman asks about ocean winds satellite replacement

NASA Astrophysics Data System (ADS)

Zielinski, Sarah

2007-05-01

NASA's QuikSCAT satellite, which launched in 1999, was intended to measure ocean winds over a two-year period. Now in its fifth year, the satellite has proven its worth; NOAA, for example, uses QuikSCAT data in its hurricane forecasts. However, there are no plans to replace the satellite or its capabilities. Rep. Nick Lampson (D-Tex.), chair of the U.S. House of Representatives Science and Technology Subcommittee on Energy and Environment, sent letters on 8 May to the NASA Director and the NOAA Administrator asking about their plans for replacing QuikSCAT data in the event of the satellite's failure, particularly during the Atlantic hurricane season. He noted that in a recent media report, the director of NOAA's Hurricane Center said that without QuikSCAT data, the accuracy of the center's two- and three-day forecasts would decrease by 10% and 16%, respectively.

Development of GNSS PWV information management system for very short-term weather forecast in the Korean Peninsula

NASA Astrophysics Data System (ADS)

Park, Han-Earl; Yoon, Ha Su; Yoo, Sung-Moon; Cho, Jungho

2017-04-01

Over the past decade, Global Navigation Satellite System (GNSS) was in the spotlight as a meteorological research tool. The Korea Astronomy and Space Science Institute (KASI) developed a GNSS precipitable water vapor (PWV) information management system to apply PWV to practical applications, such as very short-term weather forecast. The system consists of a DPR, DRS, and TEV, which are divided functionally. The DPR processes GNSS data using the Bernese GNSS software and then retrieves PWV from zenith total delay (ZTD) with the optimized mean temperature equation for the Korean Peninsula. The DRS collects data from eighty permanent GNSS stations in the southern part of the Korean Peninsula and provides the PWV retrieved from GNSS data to a user. The TEV is in charge of redundancy of the DPR. The whole process is performed in near real-time where the delay is ten minutes. The validity of the GNSS PWV was proved by means of a comparison with radiosonde data. In the experiment of numerical weather prediction model, the GNSS PWV was utilized as the initial value of the Weather Research & Forecasting (WRF) model for heavy rainfall event. As a result, we found that the forecasting capability of the WRF is improved by data assimilation of GNSS PWV.

Evaluation of the MACC operational forecast system - potential and challenges of global near-real-time modelling with respect to reactive gases in the troposphere

NASA Astrophysics Data System (ADS)

Wagner, A.; Blechschmidt, A.-M.; Bouarar, I.; Brunke, E.-G.; Clerbaux, C.; Cupeiro, M.; Cristofanelli, P.; Eskes, H.; Flemming, J.; Flentje, H.; George, M.; Gilge, S.; Hilboll, A.; Inness, A.; Kapsomenakis, J.; Richter, A.; Ries, L.; Spangl, W.; Stein, O.; Weller, R.; Zerefos, C.

2015-03-01

Monitoring Atmospheric Composition and Climate (MACC/MACCII) currently represents the European Union's Copernicus Atmosphere Monitoring Service (CAMS) (http://www.copernicus.eu), which will become fully operational in the course of 2015. The global near-real-time MACC model production run for aerosol and reactive gases provides daily analyses and 5 day forecasts of atmospheric composition fields. It is the only assimilation system world-wide that is operational to produce global analyses and forecasts of reactive gases and aerosol fields. We have investigated the ability of the MACC analysis system to simulate tropospheric concentrations of reactive gases (CO, O3, and NO2) covering the period between 2009 and 2012. A validation was performed based on CO and O3 surface observations from the Global Atmosphere Watch (GAW) network, O3 surface observations from the European Monitoring and Evaluation Programme (EMEP) and furthermore, NO2 tropospheric columns derived from the satellite sensors SCIAMACHY and GOME-2, and CO total columns derived from the satellite sensor MOPITT. The MACC system proved capable of reproducing reactive gas concentrations in consistent quality, however, with a seasonally dependent bias compared to surface and satellite observations: for northern hemispheric surface O3 mixing ratios, positive biases appear during the warm seasons and negative biases during the cold parts of the years, with monthly Modified Normalised Mean Biases (MNMBs) ranging between -30 and 30% at the surface. Model biases are likely to result from difficulties in the simulation of vertical mixing at night and deficiencies in the model's dry deposition parameterization. Observed tropospheric columns of NO2 and CO could be reproduced correctly during the warm seasons, but are mostly underestimated by the model during the cold seasons, when anthropogenic emissions are at a highest, especially over the US, Europe and Asia. Monthly MNMBs of the satellite data evaluation range between -110 and 40% for NO2 and at most -20% for CO, over the investigated regions. The underestimation is likely to result from a combination of errors concerning the dry deposition parameterization and certain limitations in the current emission inventories, together with an insufficiently established seasonality in the emissions.

Application of a medium-range global hydrologic probabilistic forecast scheme to the Ohio River Basin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Voisin, Nathalie; Pappenberger, Florian; Lettenmaier, D. P.

2011-08-15

A 10-day globally applicable flood prediction scheme was evaluated using the Ohio River basin as a test site for the period 2003-2007. The Variable Infiltration Capacity (VIC) hydrology model was initialized with the European Centre for Medium Range Weather Forecasts (ECMWF) analysis temperatures and wind, and Tropical Rainfall Monitoring Mission Multi Satellite Precipitation Analysis (TMPA) precipitation up to the day of forecast. In forecast mode, the VIC model was then forced with a calibrated and statistically downscaled ECMWF ensemble prediction system (EPS) 10-day ensemble forecast. A parallel set up was used where ECMWF EPS forecasts were interpolated to the spatialmore » scale of the hydrology model. Each set of forecasts was extended by 5 days using monthly mean climatological variables and zero precipitation in order to account for the effect of initial conditions. The 15-day spatially distributed ensemble runoff forecasts were then routed to four locations in the basin, each with different drainage areas. Surrogates for observed daily runoff and flow were provided by the reference run, specifically VIC simulation forced with ECMWF analysis fields and TMPA precipitation fields. The flood prediction scheme using the calibrated and downscaled ECMWF EPS forecasts was shown to be more accurate and reliable than interpolated forecasts for both daily distributed runoff forecasts and daily flow forecasts. Initial and antecedent conditions dominated the flow forecasts for lead times shorter than the time of concentration depending on the flow forecast amounts and the drainage area sizes. The flood prediction scheme had useful skill for the 10 following days at all sites.« less

An Automated System to Quantify Convectively induced Aircraft encounters with Turbulence over Europe and North Atlantic

NASA Astrophysics Data System (ADS)

Meneguz, Elena; Turp, Debi; Wells, Helen

2015-04-01

It is well known that encounters with moderate or severe turbulence can lead to passenger injuries and incur high costs for airlines from compensation and litigation. As one of two World Area Forecast Centres (WAFCs), the Met Office has responsibility for forecasting en-route weather hazards worldwide for aviation above a height of 10,000 ft. Observations from commercial aircraft provide a basis for gaining a better understanding of turbulence and for improving turbulence forecasts through verification. However there is currently a lack of information regarding the possible cause of the observed turbulence, or whether the turbulence occurred within cloud. Such information would be invaluable for the development of forecasting techniques for particular types of turbulence and for forecast verification. Of all the possible sources of turbulence, convective activity is believed to be a major cause of turbulence. Its relative importance over the Europe and North Atlantic area has not been yet quantified in a systematic way: in this study, a new approach is developed to automate identification of turbulent encounters in the proximity of convective clouds. Observations of convection are provided from two independent sources: a surface based lightning network and satellite imagery. Lightning observations are taken from the Met Office Arrival Time Detections network (ATDnet). ATDnet has been designed to identify cloud-to-ground flashes over Europe but also detects (a smaller fraction of) strikes over the North Atlantic. Meteosat Second Generation (MSG) satellite products are used to identify convective clouds by applying a brightness temperature filtering technique. The morphological features of cold cloud tops are also investigated. The system is run for all in situ turbulence reports received from airlines for a total of 12 months during summer 2013 and 2014 for the domain of interest. Results of this preliminary short term climatological study show significant intra-seasonal variability and an average of 15% of all aircraft encounters with turbulence are found in the proximity of convective clouds.

NOAA's weather forecasts go hyper-local with next-generation weather

Science.gov Websites

model NOAA HOME WEATHER OCEANS FISHERIES CHARTING SATELLITES CLIMATE RESEARCH COASTS CAREERS with next-generation weather model New model will help forecasters predict a storm's path, timing and intensity better than ever September 30, 2014 This is a comparison of two weather forecast models looking

Status of Air Quality in Central California and Needs for Further Study

NASA Astrophysics Data System (ADS)

Tanrikulu, S.; Beaver, S.; Soong, S.; Tran, C.; Jia, Y.; Matsuoka, J.; McNider, R. T.; Biazar, A. P.; Palazoglu, A.; Lee, P.; Wang, J.; Kang, D.; Aneja, V. P.

2012-12-01

Ozone and PM2.5 levels frequently exceed NAAQS in central California (CC). Additional emission reductions are needed to attain and maintain the standards there. Agencies are developing cost-effective emission control strategies along with complementary incentive programs to reduce emissions when exceedances are forecasted. These approaches require accurate modeling and forecasting capabilities. A variety of models have been rigorously applied (MM5, WRF, CMAQ, CAMx) over CC. Despite the vast amount of land-based measurements from special field programs and significant effort, models have historically exhibited marginal performance. Satellite data may improve model performance by: establishing IC/BC over outlying areas of the modeling domain having unknown conditions; enabling FDDA over the Pacific Ocean to characterize important marine inflows and pollutant outflows; and filling in the gaps of the land-based monitoring network. BAAQMD, in collaboration with the NASA AQAST, plans to conduct four studies that include satellite-based data in CC air quality analysis and modeling: The first project enhances and refines weather patterns, especially aloft, impacting summer ozone formation. Surface analyses were unable to characterize the strong attenuating effect of the complex terrain to steer marine winds impinging on the continent. The dense summer clouds and fog over the Pacific Ocean form spatial patterns that can be related to the downstream air flows through polluted areas. The goal of this project is to explore, characterize, and quantify these relationships using cloud cover data. Specifically, cloud agreement statistics will be developed using satellite data and model clouds. Model skin temperature predictions will be compared to both MODIS and GOES skin temperatures. The second project evaluates and improves the initial and simulated fields of meteorological models that provide inputs to air quality models. The study will attempt to determine whether a cloud dynamical adjustment developed by UAHuntsville can improve model performance for maritime stratus and whether a moisture adjustment scheme in the Pleim-Xiu boundary layer scheme can use satellite data in place of coarse surface air temperature measurements. The goal is to improve meteorological model performance that leads to improved air quality model performance. The third project evaluates and improves forecasting skills of the National Air Quality Forecasting Model in CC by using land-based routine measurements as well as satellite data. Local forecasts are mostly based on surface meteorological and air quality measurements and weather charts provided by NWS. The goal is to improve the average accuracy in forecasting exceedances, which is around 60%. The fourth project uses satellite data for monitoring trends in fine particulate matter (PM2.5) in the San Francisco Bay Area. It evaluates the effectiveness of a rule adopted in 2008 that restricts household wood burning on days forecasted to have high PM2.5 levels. The goal is to complement current analyses based on surface data covering the largest sub-regions and population centers. The overall goal is to use satellite data to overcome limitations of land-based measurements. The outcomes will be further conceptual understanding of pollutant formation, improved regulatory model performance, and better optimized forecasting programs.