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.

Improving Quantitative Precipitation Estimation via Data Fusion of High-Resolution Ground-based Radar Network and CMORPH Satellite-based Product

NASA Astrophysics Data System (ADS)

Cifelli, R.; Chen, H.; Chandrasekar, V.; Xie, P.

2015-12-01

A large number of precipitation products at multi-scales have been developed based upon satellite, radar, and/or rain gauge observations. However, how to produce optimal rainfall estimation for a given region is still challenging due to the spatial and temporal sampling difference of different sensors. In this study, we develop a data fusion mechanism to improve regional quantitative precipitation estimation (QPE) by utilizing satellite-based CMORPH product, ground radar measurements, as well as numerical model simulations. The CMORPH global precipitation product is essentially derived based on retrievals from passive microwave measurements and infrared observations onboard satellites (Joyce et al. 2004). The fine spatial-temporal resolution of 0.05o Lat/Lon and 30-min is appropriate for regional hydrologic and climate studies. However, it is inadequate for localized hydrometeorological applications such as urban flash flood forecasting. Via fusion of the Regional CMORPH product and local precipitation sensors, the high-resolution QPE performance can be improved. The area of interest is the Dallas-Fort Worth (DFW) Metroplex, which is the largest land-locked metropolitan area in the U.S. In addition to an NWS dual-polarization S-band WSR-88DP radar (i.e., KFWS radar), DFW hosts the high-resolution dual-polarization X-band radar network developed by the center for Collaborative Adaptive Sensing of the Atmosphere (CASA). This talk will present a general framework of precipitation data fusion based on satellite and ground observations. The detailed prototype architecture of using regional rainfall instruments to improve regional CMORPH precipitation product via multi-scale fusion techniques will also be discussed. Particularly, the temporal and spatial fusion algorithms developed for the DFW Metroplex will be described, which utilizes CMORPH product, S-band WSR-88DP, and X-band CASA radar measurements. In order to investigate the uncertainties associated with each

Revisiting borehole strain, typhoons, and slow earthquakes using quantitative estimates of precipitation-induced strain changes

NASA Astrophysics Data System (ADS)

Hsu, Ya-Ju; Chang, Yuan-Shu; Liu, Chi-Ching; Lee, Hsin-Ming; Linde, Alan T.; Sacks, Selwyn I.; Kitagawa, Genshio; Chen, Yue-Gau

2015-06-01

Taiwan experiences high deformation rates, particularly along its eastern margin where a shortening rate of about 30 mm/yr is experienced in the Longitudinal Valley and the Coastal Range. Four Sacks-Evertson borehole strainmeters have been installed in this area since 2003. Liu et al. (2009) proposed that a number of strain transient events, primarily coincident with low-barometric pressure during passages of typhoons, were due to deep-triggered slow slip. Here we extend that investigation with a quantitative analysis of the strain responses to precipitation as well as barometric pressure and the Earth tides in order to isolate tectonic source effects. Estimates of the strain responses to barometric pressure and groundwater level changes for the different stations vary over the ranges -1 to -3 nanostrain/millibar(hPa) and -0.3 to -1.0 nanostrain/hPa, respectively, consistent with theoretical values derived using Hooke's law. Liu et al. (2009) noted that during some typhoons, including at least one with very heavy rainfall, the observed strain changes were consistent with only barometric forcing. By considering a more extensive data set, we now find that the strain response to rainfall is about -5.1 nanostrain/hPa. A larger strain response to rainfall compared to that to air pressure and water level may be associated with an additional strain from fluid pressure changes that take place due to infiltration of precipitation. Using a state-space model, we remove the strain response to rainfall, in addition to those due to air pressure changes and the Earth tides, and investigate whether corrected strain changes are related to environmental disturbances or tectonic-original motions. The majority of strain changes attributed to slow earthquakes seem rather to be associated with environmental factors. However, some events show remaining strain changes after all corrections. These events include strain polarity changes during passages of typhoons (a characteristic that is

WPC Quantitative Precipitation Forecasts - Day 1

Science.gov Websites

to all federal, state, and local government web resources and services. Quantitative Precipitation Prediction Center 5830 University Research Court College Park, Maryland 20740 Weather Prediction Center Web

Observation-Corrected Precipitation Estimates in GEOS-5

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Liu, Qing

2014-01-01

Several GEOS-5 applications, including the GEOS-5 seasonal forecasting system and the MERRA-Land data product, rely on global precipitation data that have been corrected with satellite and or gauge-based precipitation observations. This document describes the methodology used to generate the corrected precipitation estimates and their use in GEOS-5 applications. The corrected precipitation estimates are derived by disaggregating publicly available, observationally based, global precipitation products from daily or pentad totals to hourly accumulations using background precipitation estimates from the GEOS-5 atmospheric data assimilation system. Depending on the specific combination of the observational precipitation product and the GEOS-5 background estimates, the observational product may also be downscaled in space. The resulting corrected precipitation data product is at the finer temporal and spatial resolution of the GEOS-5 background and matches the observed precipitation at the coarser scale of the observational product, separately for each day (or pentad) and each grid cell.

Estimation of continental precipitation recycling

NASA Technical Reports Server (NTRS)

Brubaker, Kaye L.; Entekhabi, Dara; Eagleson, P. S.

1993-01-01

The total amount of water that precipitates on large continental regions is supplied by two mechanisms: 1) advection from the surrounding areas external to the region and 2) evaporation and transpiration from the land surface within the region. The latter supply mechanism is tantamount to the recycling of precipitation over the continental area. The degree to which regional precipitation is supplied by recycled moisture is a potentially significant climate feedback mechanism and land surface-atmosphere interaction, which may contribute to the persistence and intensification of droughts. Gridded data on observed wind and humidity in the global atmosphere are used to determine the convergence of atmospheric water vapor over continental regions. A simplified model of the atmospheric moisture over continents and simultaneous estimates of regional precipitation are employed to estimate, for several large continental regions, the fraction of precipitation that is locally derived. The results indicate that the contribution of regional evaporation to regional precipitation varies substantially with location and season. For the regions studied, the ratio of locally contributed to total monthly precipitation generally lies between 0. 10 and 0.30 but is as high as 0.40 in several cases.

Quantitative precipitation estimation in complex orography using quasi-vertical profiles of dual polarization radar variables

NASA Astrophysics Data System (ADS)

Montopoli, Mario; Roberto, Nicoletta; Adirosi, Elisa; Gorgucci, Eugenio; Baldini, Luca

2017-04-01

Weather radars are nowadays a unique tool to estimate quantitatively the rain precipitation near the surface. This is an important task for a plenty of applications. For example, to feed hydrological models, mitigate the impact of severe storms at the ground using radar information in modern warning tools as well as aid the validation studies of satellite-based rain products. With respect to the latter application, several ground validation studies of the Global Precipitation Mission (GPM) products have recently highlighted the importance of accurate QPE from ground-based weather radars. To date, a plenty of works analyzed the performance of various QPE algorithms making use of actual and synthetic experiments, possibly trained by measurement of particle size distributions and electromagnetic models. Most of these studies support the use of dual polarization variables not only to ensure a good level of radar data quality but also as a direct input in the rain estimation equations. Among others, one of the most important limiting factors in radar QPE accuracy is the vertical variability of particle size distribution that affects at different levels, all the radar variables acquired as well as rain rates. This is particularly impactful in mountainous areas where the altitudes of the radar sampling is likely several hundred of meters above the surface. In this work, we analyze the impact of the vertical profile variations of rain precipitation on several dual polarization radar QPE algorithms when they are tested a in complex orography scenario. So far, in weather radar studies, more emphasis has been given to the extrapolation strategies that make use of the signature of the vertical profiles in terms of radar co-polar reflectivity. This may limit the use of the radar vertical profiles when dual polarization QPE algorithms are considered because in that case all the radar variables used in the rain estimation process should be consistently extrapolated at the surface

Connecting Satellite-Based Precipitation Estimates to Users

NASA Technical Reports Server (NTRS)

Huffman, George J.; Bolvin, David T.; Nelkin, Eric

2018-01-01

Beginning in 1997, the Merged Precipitation Group at NASA Goddard has distributed gridded global precipitation products built by combining satellite and surface gauge data. This started with the Global Precipitation Climatology Project (GPCP), then the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), and recently the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG). This 20+-year (and on-going) activity has yielded an important set of insights and lessons learned for making state-of-the-art precipitation data accessible to the diverse communities of users. Merged-data products critically depend on the input sensors and the retrieval algorithms providing accurate, reliable estimates, but it is also important to provide ancillary information that helps users determine suitability for their application. We typically provide fields of estimated random error, and recently reintroduced the quality index concept at user request. Also at user request we have added a (diagnostic) field of estimated precipitation phase. Over time, increasingly more ancillary fields have been introduced for intermediate products that give expert users insight into the detailed performance of the combination algorithm, such as individual merged microwave and microwave-calibrated infrared estimates, the contributing microwave sensor types, and the relative influence of the infrared estimate.

Precipitation Estimates for Hydroelectricity

NASA Technical Reports Server (NTRS)

Tapiador, Francisco J.; Hou, Arthur Y.; de Castro, Manuel; Checa, Ramiro; Cuartero, Fernando; Barros, Ana P.

2011-01-01

Hydroelectric plants require precise and timely estimates of rain, snow and other hydrometeors for operations. However, it is far from being a trivial task to measure and predict precipitation. This paper presents the linkages between precipitation science and hydroelectricity, and in doing so it provides insight into current research directions that are relevant for this renewable energy. Methods described include radars, disdrometers, satellites and numerical models. Two recent advances that have the potential of being highly beneficial for hydropower operations are featured: the Global Precipitation Measuring (GPM) mission, which represents an important leap forward in precipitation observations from space, and high performance computing (HPC) and grid technology, that allows building ensembles of numerical weather and climate models.

Precipitation Estimation from the ARM Distributed Radar Network During the MC3E Campaign

NASA Astrophysics Data System (ADS)

Theisen, A. K.; Giangrande, S. E.; Collis, S. M.

2012-12-01

The DOE - NASA Midlatitude Continental Convective Cloud Experiment (MC3E) was the first demonstration of the Atmospheric Radiation Measurement (ARM) Climate Research Facility scanning precipitation radar platforms. A goal for the MC3E field campaign over the Southern Great Plains (SGP) facility was to demonstrate the capabilities of ARM polarimetric radar systems for providing unique insights into deep convective storm evolution and microphysics. One practical application of interest for climate studies and the forcing of cloud resolving models is improved Quantitative Precipitation Estimates (QPE) from ARM radar systems positioned at SGP. This study presents the results of ARM radar-based precipitation estimates during the 2-month MC3E campaign. Emphasis is on the usefulness of polarimetric C-band radar observations (CSAPR) for rainfall estimation to distances within 100 km of the Oklahoma SGP facility. Collocated ground disdrometer resources, precipitation profiling radars and nearby surface Oklahoma Mesonet gauge records are consulted to evaluate potential ARM radar-based rainfall products and optimal methods. Rainfall products are also evaluated against the regional NEXRAD-standard observations.

Large-scale precipitation estimation using Kalpana-1 IR measurements and its validation using GPCP and GPCC data

NASA Astrophysics Data System (ADS)

Prakash, Satya; Mahesh, C.; Gairola, Rakesh M.

2011-12-01

Large-scale precipitation estimation is very important for climate science because precipitation is a major component of the earth's water and energy cycles. In the present study, the GOES precipitation index technique has been applied to the Kalpana-1 satellite infrared (IR) images of every three-hourly, i.e., of 0000, 0300, 0600,…., 2100 hours UTC, for rainfall estimation as a preparatory to the INSAT-3D. After the temperatures of all the pixels in a grid are known, they are distributed to generate a three-hourly 24-class histogram of brightness temperatures of IR (10.5-12.5 μm) images for a 1.0° × 1.0° latitude/longitude box. The daily, monthly, and seasonal rainfall have been estimated using these three-hourly rain estimates for the entire south-west monsoon period of 2009 in the present study. To investigate the potential of these rainfall estimates, the validation of monthly and seasonal rainfall estimates has been carried out using the Global Precipitation Climatology Project and Global Precipitation Climatology Centre data. The validation results show that the present technique works very well for the large-scale precipitation estimation qualitatively as well as quantitatively. The results also suggest that the simple IR-based estimation technique can be used to estimate rainfall for tropical areas at a larger temporal scale for climatological applications.

Recent Progress on the Second Generation CMORPH: LEO-IR Based Precipitation Estimates and Cloud Motion Vector

NASA Astrophysics Data System (ADS)

Xie, Pingping; Joyce, Robert; Wu, Shaorong

2015-04-01

As reported at the EGU General Assembly of 2014, a prototype system was developed for the second generation CMORPH to produce global analyses of 30-min precipitation on a 0.05olat/lon grid over the entire globe from pole to pole through integration of information from satellite observations as well as numerical model simulations. The second generation CMORPH is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) as well as LEO platforms, and precipitation simulations from numerical global models. Key to the success of the 2nd generation CMORPH, among a couple of other elements, are the development of a LEO-IR based precipitation estimation to fill in the polar gaps and objectively analyzed cloud motion vectors to capture the cloud movements of various spatial scales over the entire globe. In this presentation, we report our recent work on the refinement for these two important algorithm components. The prototype algorithm for the LEO IR precipitation estimation is refined to achieve improved quantitative accuracy and consistency with PMW retrievals. AVHRR IR TBB data from all LEO satellites are first remapped to a 0.05olat/lon grid over the entire globe and in a 30-min interval. Temporally and spatially co-located data pairs of the LEO TBB and inter-calibrated combined satellite PMW retrievals (MWCOMB) are then collected to construct tables. Precipitation at a grid box is derived from the TBB through matching the PDF tables for the TBB and the MWCOMB. This procedure is implemented for different season, latitude band and underlying surface types to account for the variations in the cloud - precipitation relationship. At the meantime, a sub-system is developed to construct analyzed fields of

Quantitative precipitation estimation for an X-band weather radar network

NASA Astrophysics Data System (ADS)

Chen, Haonan

Currently, the Next Generation (NEXRAD) radar network, a joint effort of the U.S. Department of Commerce (DOC), Defense (DOD), and Transportation (DOT), provides radar data with updates every five-six minutes across the United States. This network consists of about 160 S-band (2.7 to 3.0 GHz) radar sites. At the maximum NEXRAD range of 230 km, the 0.5 degree radar beam is about 5.4 km above ground level (AGL) because of the effect of earth curvature. Consequently, much of the lower atmosphere (1-3 km AGL) cannot be observed by the NEXRAD. To overcome the fundamental coverage limitations of today's weather surveillance radars, and improve the spatial and temporal resolution issues, the National Science Foundation Engineering Center (NSF-ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) was founded to revolutionize weather sensing in the lower atmosphere by deploying a dense network of shorter-range, low-power X-band dual-polarization radars. The distributed CASA radars are operating collaboratively to adapt the changing atmospheric conditions. Accomplishments and breakthroughs after five years operation have demonstrated the success of CASA program. Accurate radar quantitative precipitation estimation (QPE) has been pursued since the beginning of weather radar. For certain disaster prevention applications such as flash flood and landslide forecasting, the rain rate must however be measured at a high spatial and temporal resolution. To this end, high-resolution radar QPE is one of the major research activities conducted by the CASA community. A radar specific differential propagation phase (Kdp)-based QPE methodology has been developed in CASA. Unlike the rainfall estimation based on the power terms such as radar reflectivity (Z) and differential reflectivity (Zdr), Kdp-based QPE is less sensitive to the path attenuation, drop size distribution (DSD), and radar calibration errors. The CASA Kdp-based QPE system is also immune to the partial beam

Global precipitation estimates based on a technique for combining satellite-based estimates, rain gauge analysis, and NWP model precipitation information

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Rudolf, Bruno; Schneider, Udo; Keehn, Peter R.

1995-01-01

The 'satellite-gauge model' (SGM) technique is described for combining precipitation estimates from microwave satellite data, infrared satellite data, rain gauge analyses, and numerical weather prediction models into improved estimates of global precipitation. Throughout, monthly estimates on a 2.5 degrees x 2.5 degrees lat-long grid are employed. First, a multisatellite product is developed using a combination of low-orbit microwave and geosynchronous-orbit infrared data in the latitude range 40 degrees N - 40 degrees S (the adjusted geosynchronous precipitation index) and low-orbit microwave data alone at higher latitudes. Then the rain gauge analysis is brougth in, weighting each field by its inverse relative error variance to produce a nearly global, observationally based precipitation estimate. To produce a complete global estimate, the numerical model results are used to fill data voids in the combined satellite-gauge estimate. Our sequential approach to combining estimates allows a user to select the multisatellite estimate, the satellite-gauge estimate, or the full SGM estimate (observationally based estimates plus the model information). The primary limitation in the method is imperfections in the estimation of relative error for the individual fields. The SGM results for one year of data (July 1987 to June 1988) show important differences from the individual estimates, including model estimates as well as climatological estimates. In general, the SGM results are drier in the subtropics than the model and climatological results, reflecting the relatively dry microwave estimates that dominate the SGM in oceanic regions.

Precipitation evidences on X-Band Synthetic Aperture Radar imagery: an approach for quantitative detection and estimation

NASA Astrophysics Data System (ADS)

Mori, Saverio; Marzano, Frank S.; Montopoli, Mario; Pulvirenti, Luca; Pierdicca, Nazzareno

2017-04-01

al. 2014 and Mori et al. 2012); ancillary data, such as local incident angle and land cover, are used. This stage is necessary to tune the precipitation map stage and to avoid severe misinterpretations on the precipitation map routines. The second stage consist of estimating the local cloud attenuation. Finally the precipitation map is estimated, using the the retrieval algorithm developed by Marzano et al. (2011), applied only to pixels where rain is known to be present. Within the FP7 project EartH2Observe we have applied this methodology to 14 study cases, acquired within TSX and CSK missions over Italy and United States. This choice allows analysing both hurricane-like intense events and continental mid-latitude precipitations, with the possibility to verify and validate the proposed methodology through the available weather radar networks. Moreover it allows in same extent analysing the contribution of orography and quality of ancillary data (i.e. landcover). In this work we will discuss the results obtained until now in terms of improved rain cell localization and precipitation quantification.

Quantitative analysis of Bordeaux red wine precipitates by solid-state NMR: Role of tartrates and polyphenols.

PubMed

Prakash, Shipra; Iturmendi, Nerea; Grelard, Axelle; Moine, Virginie; Dufourc, Erick

2016-05-15

Stability of wines is of great importance in oenology matters. Quantitative estimation of dark red precipitates formed in Merlot and Cabernet Sauvignon wine from Bordeaux region for vintages 2012 and 2013 was performed during the oak barrel ageing process. Precipitates were obtained by placing wine at -4°C or 4°C for 2-6 days and monitored by periodic sampling during a one-year period. Spectroscopic identification of the main families of components present in the precipitate powder was performed with (13)C solid-state CPMAS NMR and 1D and 2D solution NMR of partially water re-solubilized precipitates. The study revealed that the amount of precipitate obtained is dependent on vintage, temperature and grape variety. Major components identified include potassium bitartrate, polyphenols, polysaccharides, organic acids and free amino acids. No evidence was found for the presence of proteins. The influence of main compounds found in the precipitates is discussed in relation to wine stability. Copyright © 2016. Published by Elsevier Ltd.

Approaches and Data Quality for Global Precipitation Estimation

NASA Astrophysics Data System (ADS)

Huffman, G. J.; Bolvin, D. T.; Nelkin, E. J.

2015-12-01

The space and time scales on which precipitation varies are small compared to the satellite coverage that we have, so it is necessary to merge "all" of the available satellite estimates. Differing retrieval capabilities from the various satellites require inter-calibration for the satellite estimates, while "morphing", i.e., Lagrangian time interpolation, is used to lengthen the period over which time interpolation is valid. Additionally, estimates from geostationary-Earth-orbit infrared data are plentiful, but of sufficiently lower quality compared to low-Earth-orbit passive microwave estimates that they are only used when needed. Finally, monthly surface precipitation gauge data can be used to reduce bias and improve patterns of occurrence for monthly satellite data, and short-interval satellite estimates can be improved with a simple scaling such that they sum to the monthly satellite-gauge combination. The presentation will briefly consider some of the design decisions for practical computation of the Global Precipitation Measurement (GPM) mission product Integrated Multi-satellitE Retrievals for GPM (IMERG), then examine design choices that maximize value for end users. For example, data fields are provided in the output file that provide insight into the basis for the estimated precipitation, including error, sensor providing the estimate, precipitation phase (solid/liquid), and intermediate precipitation estimates. Another important initiative is successive computations for the same data date/time at longer latencies as additional data are received, which for IMERG is currently done at 6 hours, 16 hours, and 3 months after observation time. Importantly, users require long records for each latency, which runs counter to the data archiving practices at most archive sites. As well, the assignment of Digital Object Identifiers (DOI's) for near-real-time data sets (at 6 and 16 hours for IMERG) is not a settled issue.

Estimating rates of authigenic carbonate precipitation in modern marine sediments

NASA Astrophysics Data System (ADS)

Mitnick, E. H.; Lammers, L. N.; DePaolo, D. J.

2015-12-01

The formation of authigenic carbonate (AC) in marine sediments provides a plausible explanation for large, long-lasting marine δ13C excursions that does not require extreme swings in atmospheric O2 or CO2. AC precipitation during diagenesis is driven by alkalinity production during anaerobic organic matter oxidation and is coupled to sulfate reduction. To evaluate the extent to which this process contributes to global carbon cycling, we need to relate AC production to the geochemical and geomicrobiological processes and ocean chemical conditions that control it. We present a method to estimate modern rates of AC precipitation using an inversion approach based on the diffusion-advection-reaction equation and sediment pore fluid chemistry profiles as a function of depth. SEM images and semi-quantitative elemental map analyses provide further constraints. Our initial focus is on ODP sites 807 and 1082. We sum the diffusive, advective, and reactive terms that describe changes in pore fluid Ca and Mg concentrations due to precipitation of secondary carbonate. We calculate the advective and diffusive terms from the first and second derivatives of the Ca and Mg pore fluid concentrations using a spline fit to the data. Assuming steady-state behavior we derive net AC precipitation rates of up to 8 x 10-4 mmol m-2 y-1 for Site 807 and 0.6 mmol m-2 y-1 for Site 1082. Site 1082 sediments contain pyrite, which increases in amount down-section towards the estimated peak carbonate precipitation rate, consistent with sulfate-reduction-induced AC precipitation. However, the presence of gypsum and barite throughout the sediment column implies incomplete sulfate reduction and merits further investigation of the biogeochemical reactions controlling authigenesis. Further adjustments to our method could account for the small but non-negligible fraction of groundmass with a CaSO4 signature. Our estimates demonstrate that AC formation may represent a sizeable flux in the modern global

Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.

2014-10-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets (bias-adjusted TMPA 3B42, near-real time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (± 6%). However, differences at the RFC are more important in particular for near-real time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near real time counterpart 3B42RT. However, large biases remained for 3B42 over the Western US for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in day-1) over the Northwest. Furthermore, the conditional analysis and the contingency analysis conducted illustrated the challenge of retrieving extreme precipitation from remote sensing estimates.

Comparing NEXRAD Operational Precipitation Estimates and Raingage Observations of Intense Precipitation in the Missouri River Basin.

NASA Astrophysics Data System (ADS)

Young, C. B.

2002-05-01

Accurate observation of precipitation is critical to the study and modeling of land surface hydrologic processes. NEXRAD radar-based precipitation estimates are increasingly used in field experiments, hydrologic modeling, and water and energy budget studies due to their high spatial and temporal resolution, national coverage, and perceived accuracy. Extensive development and testing of NEXRAD precipitation algorithms have been carried out in the Southern Plains. Previous studies (Young et al. 2000, Young et al. 1999, Smith et al. 1996) indicate that NEXRAD operational products tend to underestimate precipitation at light rain rates. This study investigates the performance of NEXRAD precipitation estimates of high-intensity rainfall, focusing on flood-producing storms in the Missouri River Basin. NEXRAD estimates for these storms are compared with data from multiple raingage networks, including NWS recording and non-recording gages and ALERT raingage data for the Kansas City metropolitan area. Analyses include comparisons of gage and radar data at a wide range of temporal and spatial scales. Particular attention is paid to the October 4th, 1998, storm that produced severe flooding in Kansas City. NOTE: The phrase `NEXRAD operational products' in this abstract includes precipitation estimates generated using the Stage III and P1 algorithms. Both of these products estimate hourly accumulations on the (approximately) 4 km HRAP grid.

Skill Assessment of An Hybrid Technique To Estimate Quantitative Precipitation Forecast For Galicia (nw Spain)

NASA Astrophysics Data System (ADS)

Lage, A.; Taboada, J. J.

Precipitation is the most obvious of the weather elements in its effects on normal life. Numerical weather prediction (NWP) is generally used to produce quantitative precip- itation forecast (QPF) beyond the 1-3 h time frame. These models often fail to predict small-scale variations of rain because of spin-up problems and their coarse spatial and temporal resolution (Antolik, 2000). Moreover, there are some uncertainties about the behaviour of the NWP models in extreme situations (de Bruijn and Brandsma, 2000). Hybrid techniques, combining the benefits of NWP and statistical approaches in a flexible way, are very useful to achieve a good QPF. In this work, a new technique of QPF for Galicia (NW of Spain) is presented. This region has a percentage of rainy days per year greater than 50% with quantities that may cause floods, with human and economical damages. The technique is composed of a NWP model (ARPS) and a statistical downscaling process based on an automated classification scheme of at- mospheric circulation patterns for the Iberian Peninsula (J. Ribalaygua and R. Boren, 1995). Results show that QPF for Galicia is improved using this hybrid technique. [1] Antolik, M.S. 2000 "An Overview of the National Weather Service's centralized statistical quantitative precipitation forecasts". Journal of Hydrology, 239, pp:306- 337. [2] de Bruijn, E.I.F and T. Brandsma "Rainfall prediction for a flooding event in Ireland caused by the remnants of Hurricane Charley". Journal of Hydrology, 239, pp:148-161. [3] Ribalaygua, J. and Boren R. "Clasificación de patrones espaciales de precipitación diaria sobre la España Peninsular". Informes N 3 y 4 del Servicio de Análisis e Investigación del Clima. Instituto Nacional de Meteorología. Madrid. 53 pp.

Long-Term Quantitative Precipitation Estimates (QPE) at High Spatial and Temporal Resolution over CONUS: Bias-Adjustment of the Radar-Only National Mosaic and Multi-sensor QPE (NMQ/Q2) Precipitation Reanalysis (2001-2012)

NASA Astrophysics Data System (ADS)

Prat, Olivier; Nelson, Brian; Stevens, Scott; Seo, Dong-Jun; Kim, Beomgeun

2015-04-01

The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor Quantitative (NMQ/Q2) based on the WSR-88D Next-generation Radar (NEXRAD) network over Continental United States (CONUS) is completed for the period covering from 2001 to 2012. This important milestone constitutes a unique opportunity to study precipitation processes at a 1-km spatial resolution for a 5-min temporal resolution. However, in order to be suitable for hydrological, meteorological and climatological applications, the radar-only product needs to be bias-adjusted and merged with in-situ rain gauge information. Several in-situ datasets are available to assess the biases of the radar-only product and to adjust for those biases to provide a multi-sensor QPE. The rain gauge networks that are used such as the Global Historical Climatology Network-Daily (GHCN-D), the Hydrometeorological Automated Data System (HADS), the Automated Surface Observing Systems (ASOS), and the Climate Reference Network (CRN), have different spatial density and temporal resolution. The challenges related to incorporating non-homogeneous networks over a vast area and for a long-term record are enormous. Among the challenges we are facing are the difficulties incorporating differing resolution and quality surface measurements to adjust gridded estimates of precipitation. Another challenge is the type of adjustment technique. The objective of this work is threefold. First, we investigate how the different in-situ networks can impact the precipitation estimates as a function of the spatial density, sensor type, and temporal resolution. Second, we assess conditional and un-conditional biases of the radar-only QPE for various time scales (daily, hourly, 5-min) using in-situ precipitation observations. Finally, after assessing the bias and applying reduction or elimination techniques, we are using a unique in-situ dataset merging the different RG networks (CRN, ASOS, HADS, GHCN-D) to

Precipitation Estimation Using L-Band and C-Band Soil Moisture Retrievals

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Brocca, Luca; Crow, Wade T.; Burgin, Mariko S.; De Lannoy, Gabrielle J. M.

2016-01-01

An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterometer (ASCAT) mission. The precipitation estimates so obtained are evaluated against in situ (gauge-based) precipitation observations from across the globe. The precipitation estimation skill achieved using the L-band SMAP and SMOS data sets is higher than that obtained with the C-band product, as might be expected given that L-band is sensitive to a thicker layer of soil and thereby provides more information on the response of soil moisture to precipitation. The square of the correlation coefficient between the SMAP-based precipitation estimates and the observations (for aggregations to approximately100 km and 5 days) is on average about 0.6 in areas of high rain gauge density. Satellite missions specifically designed to monitor soil moisture thus do provide significant information on precipitation variability, information that could contribute to efforts in global precipitation estimation.

Validation of Ground-based Optical Estimates of Auroral Electron Precipitation Energy Deposition

NASA Astrophysics Data System (ADS)

Hampton, D. L.; Grubbs, G. A., II; Conde, M.; Lynch, K. A.; Michell, R.; Zettergren, M. D.; Samara, M.; Ahrns, M. J.

2017-12-01

One of the major energy inputs into the high latitude ionosphere and mesosphere is auroral electron precipitation. Not only does the kinetic energy get deposited, the ensuing ionization in the E and F-region ionosphere modulates parallel and horizontal currents that can dissipate in the form of Joule heating. Global models to simulate these interactions typically use electron precipitation models that produce a poor representation of the spatial and temporal complexity of auroral activity as observed from the ground. This is largely due to these precipitation models being based on averages of multiple satellite overpasses separated by periods much longer than typical auroral feature durations. With the development of regional and continental observing networks (e.g. THEMIS ASI), the possibility of ground-based optical observations producing quantitative estimates of energy deposition with temporal and spatial scales comparable to those known to be exhibited in auroral activity become a real possibility. Like empirical precipitation models based on satellite overpasses such optics-based estimates are subject to assumptions and uncertainties, and therefore require validation. Three recent sounding rocket missions offer such an opportunity. The MICA (2012), GREECE (2014) and Isinglass (2017) missions involved detailed ground based observations of auroral arcs simultaneously with extensive on-board instrumentation. These have afforded an opportunity to examine the results of three optical methods of determining auroral electron energy flux, namely 1) ratio of auroral emissions, 2) green line temperature vs. emission altitude, and 3) parametric estimates using white-light images. We present comparisons from all three methods for all three missions and summarize the temporal and spatial scales and coverage over which each is valid.

Quantitative Precipitation Nowcasting: A Lagrangian Pixel-Based Approach

DTIC Science & Technology

2012-01-01

Sorooshian, T. Bellerby, and G. Huffman, 2010: REFAME: Rain Estimation Using Forward-Adjusted Advection of Microwave Estimates. J. of Hydromet ., 11...precipitation forecasting using information from radar and Numerical Weather Prediction models. J. of Hydromet ., 4(6):1168-1180. Germann, U., and I

Satellite precipitation estimation over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Porcu, F.; Gjoka, U.

2012-04-01

Precipitation characteristics over the Tibetan Plateau are very little known, given the scarcity of reliable and widely distributed ground observation, thus the satellite approach is a valuable choice for large scale precipitation analysis and hydrological cycle studies. However,the satellite perspective undergoes various shortcomings at the different wavelengths used in atmospheric remote sensing. In the microwave spectrum often the high soil emissivity masks or hides the atmospheric signal upwelling from light-moderate precipitation layers, while low and relatively thin precipitating clouds are not well detected in the visible-infrared, because of their low contrast with cold and bright (if snow covered) background. In this work an IR-based, statistical rainfall estimation technique is trained and applied over the Tibetan Plateau hydrological basin to retrive precipitation intensity at different spatial and temporal scales. The technique is based on a simple artificial neural network scheme trained with two supervised training sets assembled for monsoon season and for the rest of the year. For the monsoon season (estimated from June to September), the ground radar precipitation data for few case studies are used to build the training set: four days in summer 2009 are considered. For the rest of the year, CloudSat-CPR derived snowfall rate has been used as reference precipitation data, following the Kulie and Bennartz (2009) algorithm. METEOSAT-7 infrared channels radiance (at 6.7 and 11 micometers) and derived local variability features (such as local standard deviation and local average) are used as input and the actual rainrate is obtained as output for each satellite slot, every 30 minutes on the satellite grid. The satellite rainrate maps for three years (2008-2010) are computed and compared with available global precipitation products (such as C-MORPH and TMPA products) and with other techniques applied to the Plateau area: similarities and differences are

An improved procedure for the validation of satellite-based precipitation estimates

NASA Astrophysics Data System (ADS)

Tang, Ling; Tian, Yudong; Yan, Fang; Habib, Emad

2015-09-01

The objective of this study is to propose and test a new procedure to improve the validation of remote-sensing, high-resolution precipitation estimates. Our recent studies show that many conventional validation measures do not accurately capture the unique error characteristics in precipitation estimates to better inform both data producers and users. The proposed new validation procedure has two steps: 1) an error decomposition approach to separate the total retrieval error into three independent components: hit error, false precipitation and missed precipitation; and 2) the hit error is further analyzed based on a multiplicative error model. In the multiplicative error model, the error features are captured by three model parameters. In this way, the multiplicative error model separates systematic and random errors, leading to more accurate quantification of the uncertainties. The proposed procedure is used to quantitatively evaluate the recent two versions (Version 6 and 7) of TRMM's Multi-sensor Precipitation Analysis (TMPA) real-time and research product suite (3B42 and 3B42RT) for seven years (2005-2011) over the continental United States (CONUS). The gauge-based National Centers for Environmental Prediction (NCEP) Climate Prediction Center (CPC) near-real-time daily precipitation analysis is used as the reference. In addition, the radar-based NCEP Stage IV precipitation data are also model-fitted to verify the effectiveness of the multiplicative error model. The results show that winter total bias is dominated by the missed precipitation over the west coastal areas and the Rocky Mountains, and the false precipitation over large areas in Midwest. The summer total bias is largely coming from the hit bias in Central US. Meanwhile, the new version (V7) tends to produce more rainfall in the higher rain rates, which moderates the significant underestimation exhibited in the previous V6 products. Moreover, the error analysis from the multiplicative error model

Estimating Global Precipitation for Science and Application

NASA Technical Reports Server (NTRS)

Huffman, George J.

2013-01-01

Over the past two decades there has been vigorous development in the satellite assets and the algorithms necessary to estimate precipitation around the globe. In particular the highly successful joint NASAJAXA Tropical Rainfall Measuring Mission (TRMM) and the upcoming Global Precipitation Measurement (GPM) mission, also joint between NASA and JAXA, have driven these issues. At the same time, the long-running Global Precipitation Climatology Project (GPCP) continues to extend a stable, climate-oriented view of global precipitation. This talk will provide an overview of these projects and the wider international community of precipitation datasets, sketch plans for next-generation products, and provide some examples of the best use for the different products. One key lesson learned is that different data sets are needed to address the variety of issues that need precipitation data, including detailed 3-D views of hurricanes, flash flood forecasting, drought analysis, and global change.

The TRMM Multi-satellite Precipitation Analysis (TMPA): Quasi-Global Precipitation Estimates at Fine Scales

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.; Gu, Guojun; Nelkin, Eric J.; Bowman, Kenneth P.; Stocker, Erich; Wolff, David B.

2006-01-01

The TRMM Multi-satellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining multiple precipitation estimates from satellites, as well as gauge analyses where feasible, at fine scales (0.25 degrees x 0.25 degrees and 3-hourly). It is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The data set covers the latitude band 50 degrees N-S for the period 1998 to the delayed present. Early validation results are as follows: The TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate dependent low bias due to lack of sensitivity to low precipitation rates in one of the input products (based on AMSU-B). At finer scales the TMPA is successful at approximately reproducing the surface-observation-based histogram of precipitation, as well as reasonably detecting large daily events. The TMPA, however, has lower skill in correctly specifying moderate and light event amounts on short time intervals, in common with other fine-scale estimators. Examples are provided of a flood event and diurnal cycle determination.

Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP)

NASA Technical Reports Server (NTRS)

Adler, Robert; Gu, Guojun; Huffman, George

2012-01-01

A procedure is described to estimate bias errors for mean precipitation by using multiple estimates from different algorithms, satellite sources, and merged products. The Global Precipitation Climatology Project (GPCP) monthly product is used as a base precipitation estimate, with other input products included when they are within +/- 50% of the GPCP estimates on a zonal-mean basis (ocean and land separately). The standard deviation s of the included products is then taken to be the estimated systematic, or bias, error. The results allow one to examine monthly climatologies and the annual climatology, producing maps of estimated bias errors, zonal-mean errors, and estimated errors over large areas such as ocean and land for both the tropics and the globe. For ocean areas, where there is the largest question as to absolute magnitude of precipitation, the analysis shows spatial variations in the estimated bias errors, indicating areas where one should have more or less confidence in the mean precipitation estimates. In the tropics, relative bias error estimates (s/m, where m is the mean precipitation) over the eastern Pacific Ocean are as large as 20%, as compared with 10%-15% in the western Pacific part of the ITCZ. An examination of latitudinal differences over ocean clearly shows an increase in estimated bias error at higher latitudes, reaching up to 50%. Over land, the error estimates also locate regions of potential problems in the tropics and larger cold-season errors at high latitudes that are due to snow. An empirical technique to area average the gridded errors (s) is described that allows one to make error estimates for arbitrary areas and for the tropics and the globe (land and ocean separately, and combined). Over the tropics this calculation leads to a relative error estimate for tropical land and ocean combined of 7%, which is considered to be an upper bound because of the lack of sign-of-the-error canceling when integrating over different areas with a

A New Method for Near Real Time Precipitation Estimates Using a Derived Statistical Relationship between Precipitable Water Vapor and Precipitation

NASA Astrophysics Data System (ADS)

Roman, J.

2015-12-01

The IPCC 5th Assessment found that the predicted warming of 1oC would increase the risk of extreme events such as heat waves, droughts, and floods. Weather extremes, like floods, have shown the vulnerability and susceptibility society has to these extreme weather events, through impacts such as disruption of food production, water supply, health, and damage of infrastructure. This paper examines a new way of near-real time forecasting of precipitation. A 10-year statistical climatological relationship was derived between precipitable water vapor (PWV) and precipitation by using the NASA Atmospheric Infrared Sounder daily gridded PWV product and the NASA Tropical Rainfall Measuring Mission daily gridded precipitation total. Forecasting precipitation estimates in real time is dire for flood monitoring and disaster management. Near real time PWV observations from AIRS on Aqua are available through the Goddard Earth Sciences Data and Information Service Center. In addition, PWV observations are available through direct broadcast from the NASA Suomi-NPP ATMS/CrIS instrument, the operational follow on to AIRS. The derived climatological relationship can be applied to create precipitation estimates in near real time by utilizing the direct broadcasting capabilities currently available in the CONUS region. The application of this relationship will be characterized through case-studies by using near real-time NASA AIRS Science Team v6 PWV products and ground-based SuomiNet GPS to estimate the current precipitation potential; the max amount of precipitation that can occur based on the moisture availability. Furthermore, the potential contribution of using the direct broadcasting of the NUCAPS ATMS/CrIS PWV products will be demonstrated. The analysis will highlight the advantages of applying this relationship in near-real time for flash flood monitoring and risk management. Relevance to the NWS River Forecast Centers will be discussed.

NOAA Atlas 14: Updated Precipitation Frequency Estimates for the United States

NASA Astrophysics Data System (ADS)

Pavlovic, S.; Perica, S.; Martin, D.; Roy, I.; StLaurent, M.; Trypaluk, C.; Unruh, D.; Yekta, M.; Bonnin, G. M.

2013-12-01

NOAA Atlas 14 precipitation frequency estimates, developed by the National Weather Service's Hydrometeorological Design Studies Center, serve as the de-facto standards for a wide variety of design and planning activities under federal, state, and local regulations. Precipitation frequency estimates are used in the design of drainage for highways, culverts, bridges, parking lots, as well as in sizing sewer and stormwater infrastructure. Water resources engineers use them to estimate the amount of runoff, to estimate the volume of detention basins and size detention-basin outlet structures, and to estimate the volume of sediment or the amount of erosion. They are also used by floodplain managers to delineate floodplains and regulate the development in floodplains, which is crucial for all communities in the National Flood Insurance Program. Hydrometeorological Design Studies Center now provides more than 35,000 downloads per month to its Precipitation Frequency Data Server. Precipitation frequency estimates are often used in engineering design without any understanding how these estimates have been developed or without any understanding of the uncertainties associated with these estimates. This presentation will describe novel tools and techniques that have being developed in the last years to determine precipitation frequency estimates in NOAA Atlas 14. Particular attention will be given to the regional frequency analysis approach based on L-moment statistics calculated from annual maximum series, selected statistics obtained in determining and parameterizing the probability distribution functions, and the potential implication for engineering design of recently published estimates.

NOAA Atlas 14: Updated Precipitation Frequency Estimates for the United States

NASA Astrophysics Data System (ADS)

Pavlovic, S.; Perica, S.; Martin, D.; Roy, I.; StLaurent, M.; Trypaluk, C.; Unruh, D.; Yekta, M.; Bonnin, G. M.

2011-12-01

NOAA Atlas 14 precipitation frequency estimates, developed by the National Weather Service's Hydrometeorological Design Studies Center, serve as the de-facto standards for a wide variety of design and planning activities under federal, state, and local regulations. Precipitation frequency estimates are used in the design of drainage for highways, culverts, bridges, parking lots, as well as in sizing sewer and stormwater infrastructure. Water resources engineers use them to estimate the amount of runoff, to estimate the volume of detention basins and size detention-basin outlet structures, and to estimate the volume of sediment or the amount of erosion. They are also used by floodplain managers to delineate floodplains and regulate the development in floodplains, which is crucial for all communities in the National Flood Insurance Program. Hydrometeorological Design Studies Center now provides more than 35,000 downloads per month to its Precipitation Frequency Data Server. Precipitation frequency estimates are often used in engineering design without any understanding how these estimates have been developed or without any understanding of the uncertainties associated with these estimates. This presentation will describe novel tools and techniques that have being developed in the last years to determine precipitation frequency estimates in NOAA Atlas 14. Particular attention will be given to the regional frequency analysis approach based on L-moment statistics calculated from annual maximum series, selected statistics obtained in determining and parameterizing the probability distribution functions, and the potential implication for engineering design of recently published estimates.

Status of High Latitude Precipitation Estimates from Observations and Reanalyses

NASA Technical Reports Server (NTRS)

Behrangi, Ali; Christensen, Matthew; Richardson, Mark; Lebsock, Matthew; Stephens, Graeme; Huffman, George J.; Bolvin, David T.; Adler, Robert F.; Gardner, Alex; Lambrigtsen, Bjorn H.;

Estimating Precipitation Input to a Watershed by Combining Gauge and Radar Derived Observations

NASA Astrophysics Data System (ADS)

Ercan, M. B.; Goodall, J. L.

2011-12-01

One challenge in creating an accurate watershed model is obtaining estimates of precipitation intensity over the watershed area. While precipitation measurements are generally available from gauging stations and radar instruments, both of these approaches for measuring precipitation have strengths and weakness. A typical way of addressing this challenge is to use gauged precipitation estimates to calibrate radar based estimates, however this study proposes a slightly different approach in which the optimal daily precipitation value is selected from either the gauged or the radar estimates based on the observed streamflow for that day. Our proposed approach is perhaps most relevant for cases of modeling watersheds that do not have a nearby precipitation gauge, or for regions that experience convective storms that are often highly spatially variable. Using the Eno River watershed located in Orange County, NC, three different precipitation datasets were created to predict streamflow at the watershed outlet for the time period 2005-2010 using the Soil and Water Assessment Tool (SWAT): (1) estimates based on only precipitation gauging stations, (2) estimates based only on gauged-corrected radar observations, and (3) the combination of precipitation estimates from the gauge and radar data determined using our proposed approach. The results show that the combined precipitation approach significantly improves streamflow predictions (Nash-Sutcliffe Coefficient, E = 0.66) when compared to the gauged estimates alone (E = 0.47) and the radar based estimates alone (E = 0.45). Our study was limited to one watershed, therefore additional studies are needed to control for factors such as climate, ecology, and hydrogeology that will likely influence the results of the analysis.

A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons

NASA Astrophysics Data System (ADS)

Sun, Qiaohong; Miao, Chiyuan; Duan, Qingyun; Ashouri, Hamed; Sorooshian, Soroosh; Hsu, Kuo-Lin

2018-03-01

In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge-based, satellite-related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high-latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.

Enhancement of regional wet deposition estimates based on modeled precipitation inputs

Treesearch

James A. Lynch; Jeffery W. Grimm; Edward S. Corbett

1996-01-01

Application of a variety of two-dimensional interpolation algorithms to precipitation chemistry data gathered at scattered monitoring sites for the purpose of estimating precipitation- born ionic inputs for specific points or regions have failed to produce accurate estimates. The accuracy of these estimates is particularly poor in areas of high topographic relief....

Assessment of satellite-based precipitation estimates over Paraguay

NASA Astrophysics Data System (ADS)

Oreggioni Weiberlen, Fiorella; Báez Benítez, Julián

2018-04-01

Satellite-based precipitation estimates represent a potential alternative source of input data in a plethora of meteorological and hydrological applications, especially in regions characterized by a low density of rain gauge stations. Paraguay provides a good example of a case where the use of satellite-based precipitation could be advantageous. This study aims to evaluate the version 7 of the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TMPA V7; 3B42 V7) and the version 1.0 of the purely satellite-based product of the Climate Prediction Center Morphing Technique (CMORPH RAW) through their comparison with daily in situ precipitation measurements from 1998 to 2012 over Paraguay. The statistical assessment is conducted with several commonly used indexes. Specifically, to evaluate the accuracy of daily precipitation amounts, mean error (ME), root mean square error (RMSE), BIAS, and coefficient of determination (R 2) are used, and to analyze the capability to correctly detect different precipitation intensities, false alarm ratio (FAR), frequency bias index (FBI), and probability of detection (POD) are applied to various rainfall rates (0, 0.1, 0.5, 1, 2, 5, 10, 20, 40, 60, and 80 mm/day). Results indicate that TMPA V7 has a better performance than CMORPH RAW over Paraguay. TMPA V7 has higher accuracy in the estimation of daily rainfall volumes and greater precision in the detection of wet days (> 0 mm/day). However, both satellite products show a lower ability to appropriately detect high intensity precipitation events.

Augmenting Satellite Precipitation Estimation with Lightning Information

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

Mahrooghy, Majid; Anantharaj, Valentine G; Younan, Nicolas H.

2013-01-01

We have used lightning information to augment the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network - Cloud Classification System (PERSIANN-CCS). Co-located lightning data are used to segregate cloud patches, segmented from GOES-12 infrared data, into either electrified (EL) or non-electrified (NEL) patches. A set of features is extracted separately for the EL and NEL cloud patches. The features for the EL cloud patches include new features based on the lightning information. The cloud patches are classified and clustered using self-organizing maps (SOM). Then brightness temperature and rain rate (T-R) relationships are derived for the different clusters.more » Rain rates are estimated for the cloud patches based on their representative T-R relationship. The Equitable Threat Score (ETS) for daily precipitation estimates is improved by almost 12% for the winter season. In the summer, no significant improvements in ETS are noted.« less

Systematic evaluation of NASA precipitation radar estimates using NOAA/NSSL National Mosaic QPE products

NASA Astrophysics Data System (ADS)

Kirstetter, P.; Hong, Y.; Gourley, J. J.; Chen, S.; Flamig, Z.; Zhang, J.; Howard, K.; Petersen, W. A.

2011-12-01

Proper characterization of the error structure of TRMM Precipitation Radar (PR) quantitative precipitation estimation (QPE) is needed for their use in TRMM combined products, water budget studies and hydrological modeling applications. Due to the variety of sources of error in spaceborne radar QPE (attenuation of the radar signal, influence of land surface, impact of off-nadir viewing angle, etc.) and the impact of correction algorithms, the problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements (GV) using NOAA/NSSL's National Mosaic QPE (NMQ) system. An investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) on the basis of a 3-month-long data sample. A significant effort has been carried out to derive a bias-corrected, robust reference rainfall source from NMQ. The GV processing details will be presented along with preliminary results of PR's error characteristics using contingency table statistics, probability distribution comparisons, scatter plots, semi-variograms, and systematic biases and random errors.

Quantitative estimation of Tropical Rainfall Mapping Mission precipitation radar signals from ground-based polarimetric radar observations

NASA Astrophysics Data System (ADS)

Bolen, Steven M.; Chandrasekar, V.

2003-06-01

The Tropical Rainfall Mapping Mission (TRMM) is the first mission dedicated to measuring rainfall from space using radar. The precipitation radar (PR) is one of several instruments aboard the TRMM satellite that is operating in a nearly circular orbit with nominal altitude of 350 km, inclination of 35°, and period of 91.5 min. The PR is a single-frequency Ku-band instrument that is designed to yield information about the vertical storm structure so as to gain insight into the intensity and distribution of rainfall. Attenuation effects on PR measurements, however, can be significant and as high as 10-15 dB. This can seriously impair the accuracy of rain rate retrieval algorithms derived from PR signal returns. Quantitative estimation of PR attenuation is made along the PR beam via ground-based polarimetric observations to validate attenuation correction procedures used by the PR. The reflectivity (Zh) at horizontal polarization and specific differential phase (Kdp) are found along the beam from S-band ground radar measurements, and theoretical modeling is used to determine the expected specific attenuation (k) along the space-Earth path at Ku-band frequency from these measurements. A theoretical k-Kdp relationship is determined for rain when Kdp ≥ 0.5°/km, and a power law relationship, k = a Zhb, is determined for light rain and other types of hydrometers encountered along the path. After alignment and resolution volume matching is made between ground and PR measurements, the two-way path-integrated attenuation (PIA) is calculated along the PR propagation path by integrating the specific attenuation along the path. The PR reflectivity derived after removing the PIA is also compared against ground radar observations.

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.

Estimating mountain basin-mean precipitation from streamflow using Bayesian inference

NASA Astrophysics Data System (ADS)

Henn, Brian; Clark, Martyn P.; Kavetski, Dmitri; Lundquist, Jessica D.

2015-10-01

Estimating basin-mean precipitation in complex terrain is difficult due to uncertainty in the topographical representativeness of precipitation gauges relative to the basin. To address this issue, we use Bayesian methodology coupled with a multimodel framework to infer basin-mean precipitation from streamflow observations, and we apply this approach to snow-dominated basins in the Sierra Nevada of California. Using streamflow observations, forcing data from lower-elevation stations, the Bayesian Total Error Analysis (BATEA) methodology and the Framework for Understanding Structural Errors (FUSE), we infer basin-mean precipitation, and compare it to basin-mean precipitation estimated using topographically informed interpolation from gauges (PRISM, the Parameter-elevation Regression on Independent Slopes Model). The BATEA-inferred spatial patterns of precipitation show agreement with PRISM in terms of the rank of basins from wet to dry but differ in absolute values. In some of the basins, these differences may reflect biases in PRISM, because some implied PRISM runoff ratios may be inconsistent with the regional climate. We also infer annual time series of basin precipitation using a two-step calibration approach. Assessment of the precision and robustness of the BATEA approach suggests that uncertainty in the BATEA-inferred precipitation is primarily related to uncertainties in hydrologic model structure. Despite these limitations, time series of inferred annual precipitation under different model and parameter assumptions are strongly correlated with one another, suggesting that this approach is capable of resolving year-to-year variability in basin-mean precipitation.

Comparison of GPCP Monthly and Daily Precipitation Estimates with High-Latitude Gauge Observations

NASA Technical Reports Server (NTRS)

Bolvin, David T.; Adler, Robert G.; Nelkin, Eric J.; Poutiainen, Jani

2008-01-01

It is very important to know how much rain and snow falls around the world for uses that range from crop forecasting to disaster response, drought monitoring to flood forecasting, and weather analysis to climate research. Precipitation is usually measured with rain gauges, but rain gauges don t exist in areas that are sparsely populated, which tends to be a good portion of the globe. To overcome this, meteorologists use satellite data to estimate global precipitation. However, it is difficult to estimate rain and especially snow in cold climates using most current satellites. The satellite sensors are often "confused" by a snowy or frozen surface and therefore cannot distinguish precipitation. One commonly used satellite-based precipitation data set, the Global Precipitation Climatology Project (GPCP) data, overcomes this frozen-surface problem through the innovative use of two sources of satellite data, the Television Infrared Observation Satellite Operational Vertical Sounder (TOVS) and the Atmospheric Infrared Sounder (AIRS). Though the GPCP estimates are generally considered a very reliable source of precipitation, it has been difficult to assess the quality of these estimates in cold climates due to the lack of gauges. Recently, the Finnish Meteorological Institute (FMI) has provided a 12-year span of high-quality daily rain gauge observations, covering all of Finland, that can be used to compare with the GPCP data to determine how well the satellites estimate cold-climate precipitation. Comparison of the monthly GPCP satellite-based estimates and the FMI gauge observations shows remarkably good agreement, with the GPCP estimates being 6% lower in the amount of precipitation than the FMI observations. Furthermore, the month-to-month correlation between the GPCP and FMI is very high at 0.95 (1.0 is perfect). The daily GPCP estimates replicate the FMI daily occurrences of precipitation with a correlation of 0.55 in the summer and 0.45 in the winter. The winter

Bayesian Processor of Output for Probabilistic Quantitative Precipitation Forecasting

NASA Astrophysics Data System (ADS)

Krzysztofowicz, R.; Maranzano, C. J.

2006-05-01

The Bayesian Processor of Output (BPO) is a new, theoretically-based technique for probabilistic forecasting of weather variates. It processes output from a numerical weather prediction (NWP) model and optimally fuses it with climatic data in order to quantify uncertainty about a predictand. The BPO is being tested by producing Probabilistic Quantitative Precipitation Forecasts (PQPFs) for a set of climatically diverse stations in the contiguous U.S. For each station, the PQPFs are produced for the same 6-h, 12-h, and 24-h periods up to 84- h ahead for which operational forecasts are produced by the AVN-MOS (Model Output Statistics technique applied to output fields from the Global Spectral Model run under the code name AVN). The inputs into the BPO are estimated as follows. The prior distribution is estimated from a (relatively long) climatic sample of the predictand; this sample is retrieved from the archives of the National Climatic Data Center. The family of the likelihood functions is estimated from a (relatively short) joint sample of the predictor vector and the predictand; this sample is retrieved from the same archive that the Meteorological Development Laboratory of the National Weather Service utilized to develop the AVN-MOS system. This talk gives a tutorial introduction to the principles and procedures behind the BPO, and highlights some results from the testing: a numerical example of the estimation of the BPO, and a comparative verification of the BPO forecasts and the MOS forecasts. It concludes with a list of demonstrated attributes of the BPO (vis- à-vis the MOS): more parsimonious definitions of predictors, more efficient extraction of predictive information, better representation of the distribution function of predictand, and equal or better performance (in terms of calibration and informativeness).

Extreme precipitation depths for Texas, excluding the Trans-Pecos region

USGS Publications Warehouse

Lanning-Rush, Jennifer; Asquith, William H.; Slade, Raymond M.

1998-01-01

Storm durations of 1, 2, 3, 4, 5, and 6 days were investigated for this report. The extreme precipitation depth for a particular area is estimated from an “extreme precipitation curve” (an upper limit or envelope curve developed from graphs of extreme precipitation depths for each climatic region). The extreme precipitation curves were determined using precipitation depth-duration information from a subset (24 “extreme” storms) of 213 “notable” storms documented throughout Texas. The extreme precipitation curves can be used to estimate extreme precipitation depth for a particular area. The extreme precipitation depth represents a limiting depth, which can provide useful comparative information for more quantitative analyses.

3800 Years of Quantitative Precipitation Reconstruction from the Northwest Yucatan Peninsula

PubMed Central

Carrillo-Bastos, Alicia; Islebe, Gerald A.; Torrescano-Valle, Nuria

2013-01-01

Precipitation over the last 3800 years has been reconstructed using modern pollen calibration and precipitation data. A transfer function was then performed via the linear method of partial least squares. By calculating precipitation anomalies, it is estimated that precipitation deficits were greater than surpluses, reaching 21% and <9%, respectively. The period from 50 BC to 800 AD was the driest of the record. The drought related to the abandonment of the Maya Preclassic period featured a 21% reduction in precipitation, while the drought of the Maya collapse (800 to 860 AD) featured a reduction of 18%. The Medieval Climatic Anomaly was a period of positive phases (3.8–7.6%). The Little Ice Age was a period of climatic variability, with reductions in precipitation but without deficits. PMID:24391940

Downsizing a long-term precipitation network: Using a quantitative approach to inform difficult decisions.

PubMed

Green, Mark B; Campbell, John L; Yanai, Ruth D; Bailey, Scott W; Bailey, Amey S; Grant, Nicholas; Halm, Ian; Kelsey, Eric P; Rustad, Lindsey E

2018-01-01

The design of a precipitation monitoring network must balance the demand for accurate estimates with the resources needed to build and maintain the network. If there are changes in the objectives of the monitoring or the availability of resources, network designs should be adjusted. At the Hubbard Brook Experimental Forest in New Hampshire, USA, precipitation has been monitored with a network established in 1955 that has grown to 23 gauges distributed across nine small catchments. This high sampling intensity allowed us to simulate reduced sampling schemes and thereby evaluate the effect of decommissioning gauges on the quality of precipitation estimates. We considered all possible scenarios of sampling intensity for the catchments on the south-facing slope (2047 combinations) and the north-facing slope (4095 combinations), from the current scenario with 11 or 12 gauges to only 1 gauge remaining. Gauge scenarios differed by as much as 6.0% from the best estimate (based on all the gauges), depending on the catchment, but 95% of the scenarios gave estimates within 2% of the long-term average annual precipitation. The insensitivity of precipitation estimates and the catchment fluxes that depend on them under many reduced monitoring scenarios allowed us to base our reduction decision on other factors such as technician safety, the time required for monitoring, and co-location with other hydrometeorological measurements (snow, air temperature). At Hubbard Brook, precipitation gauges could be reduced from 23 to 10 with a change of <2% in the long-term precipitation estimates. The decision-making approach illustrated in this case study is applicable to the redesign of monitoring networks when reduction of effort seems warranted.

Sensitivity of quantitative groundwater recharge estimates to volumetric and distribution uncertainty in rainfall forcing products

NASA Astrophysics Data System (ADS)

Werner, Micha; Westerhoff, Rogier; Moore, Catherine

2017-04-01

Quantitative estimates of recharge due to precipitation excess are an important input to determining sustainable abstraction of groundwater resources, as well providing one of the boundary conditions required for numerical groundwater modelling. Simple water balance models are widely applied for calculating recharge. In these models, precipitation is partitioned between different processes and stores; including surface runoff and infiltration, storage in the unsaturated zone, evaporation, capillary processes, and recharge to groundwater. Clearly the estimation of recharge amounts will depend on the estimation of precipitation volumes, which may vary, depending on the source of precipitation data used. However, the partitioning between the different processes is in many cases governed by (variable) intensity thresholds. This means that the estimates of recharge will not only be sensitive to input parameters such as soil type, texture, land use, potential evaporation; but mainly to the precipitation volume and intensity distribution. In this paper we explore the sensitivity of recharge estimates due to difference in precipitation volumes and intensity distribution in the rainfall forcing over the Canterbury region in New Zealand. We compare recharge rates and volumes using a simple water balance model that is forced using rainfall and evaporation data from; the NIWA Virtual Climate Station Network (VCSN) data (which is considered as the reference dataset); the ERA-Interim/WATCH dataset at 0.25 degrees and 0.5 degrees resolution; the TRMM-3B42 dataset; the CHIRPS dataset; and the recently releases MSWEP dataset. Recharge rates are calculated at a daily time step over the 14 year period from the 2000 to 2013 for the full Canterbury region, as well as at eight selected points distributed over the region. Lysimeter data with observed estimates of recharge are available at four of these points, as well as recharge estimates from the NGRM model, an independent model

Performance of near real-time Global Satellite Mapping of Precipitation estimates during heavy precipitation events over northern China

NASA Astrophysics Data System (ADS)

Chen, Sheng; Hu, Junjun; Zhang, Asi; Min, Chao; Huang, Chaoying; Liang, Zhenqing

2018-02-01

This study assesses the performance of near real-time Global Satellite Mapping of Precipitation (GSMaP_NRT) estimates over northern China, including Beijing and its adjacent regions, during three heavy precipitation events from 21 July 2012 to 2 August 2012. Two additional near real-time satellite-based products, the Climate Prediction Center morphing method (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), were used for parallel comparison with GSMaP_NRT. Gridded gauge observations were used as reference for a performance evaluation with respect to spatiotemporal variability, probability distribution of precipitation rate and volume, and contingency scores. Overall, GSMaP_NRT generally captures the spatiotemporal variability of precipitation and shows promising potential in near real-time mapping applications. GSMaP_NRT misplaced storm centers in all three storms. GSMaP_NRT demonstrated higher skill scores in the first high-impact storm event on 21 July 2015. GSMaP_NRT passive microwave only precipitation can generally capture the pattern of heavy precipitation distributions over flat areas but failed to capture the intensive rain belt over complicated mountainous terrain. The results of this study can be useful to both algorithm developers and the scientific end users, providing a better understanding of strengths and weaknesses to hydrologists using satellite precipitation products.

Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002-2012)

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.

2015-04-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over the contiguous United States (CONUS) for the period 2002-2012. This comparison effort includes satellite multi-sensor data sets (bias-adjusted TMPA 3B42, near-real-time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation data sets are compared with surface observations from the Global Historical Climatology Network-Daily (GHCN-D) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (±6%). However, differences at the RFC are more important in particular for near-real-time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near-real-time counterpart 3B42RT. However, large biases remained for 3B42 over the western USA for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in. day-1) over the Pacific Northwest. Furthermore, the conditional analysis and a contingency analysis conducted illustrated the challenge in retrieving extreme precipitation from remote sensing estimates.

An Automated Technique for Estimating Daily Precipitation over the State of Virginia

NASA Technical Reports Server (NTRS)

Follansbee, W. A.; Chamberlain, L. W., III

1981-01-01

Digital IR and visible imagery obtained from a geostationary satellite located over the equator at 75 deg west latitude were provided by NASA and used to obtain a linear relationship between cloud top temperature and hourly precipitation. Two computer programs written in FORTRAN were used. The first program computes the satellite estimate field from the hourly digital IR imagery. The second program computes the final estimate for the entire state area by comparing five preliminary estimates of 24 hour precipitation with control raingage readings and determining which of the five methods gives the best estimate for the day. The final estimate is then produced by incorporating control gage readings into the winning method. In presenting reliable precipitation estimates for every cell in Virginia in near real time on a daily on going basis, the techniques require on the order of 125 to 150 daily gage readings by dependable, highly motivated observers distributed as uniformly as feasible across the state.

Pareto-Optimal Estimates of California Precipitation Change

NASA Astrophysics Data System (ADS)

Langenbrunner, Baird; Neelin, J. David

2017-12-01

In seeking constraints on global climate model projections under global warming, one commonly finds that different subsets of models perform well under different objective functions, and these trade-offs are difficult to weigh. Here a multiobjective approach is applied to a large set of subensembles generated from the Climate Model Intercomparison Project phase 5 ensemble. We use observations and reanalyses to constrain tropical Pacific sea surface temperatures, upper level zonal winds in the midlatitude Pacific, and California precipitation. An evolutionary algorithm identifies the set of Pareto-optimal subensembles across these three measures, and these subensembles are used to constrain end-of-century California wet season precipitation change. This methodology narrows the range of projections throughout California, increasing confidence in estimates of positive mean precipitation change. Finally, we show how this technique complements and generalizes emergent constraint approaches for restricting uncertainty in end-of-century projections within multimodel ensembles using multiple criteria for observational constraints.

Uncertainty Estimation using Bootstrapped Kriging Predictions for Precipitation Isoscapes

NASA Astrophysics Data System (ADS)

Ma, C.; Bowen, G. J.; Vander Zanden, H.; Wunder, M.

2017-12-01

Isoscapes are spatial models representing the distribution of stable isotope values across landscapes. Isoscapes of hydrogen and oxygen in precipitation are now widely used in a diversity of fields, including geology, biology, hydrology, and atmospheric science. To generate isoscapes, geostatistical methods are typically applied to extend predictions from limited data measurements. Kriging is a popular method in isoscape modeling, but quantifying the uncertainty associated with the resulting isoscapes is challenging. Applications that use precipitation isoscapes to determine sample origin require estimation of uncertainty. Here we present a simple bootstrap method (SBM) to estimate the mean and uncertainty of the krigged isoscape and compare these results with a generalized bootstrap method (GBM) applied in previous studies. We used hydrogen isotopic data from IsoMAP to explore these two approaches for estimating uncertainty. We conducted 10 simulations for each bootstrap method and found that SBM results in more kriging predictions (9/10) compared to GBM (4/10). Prediction from SBM was closer to the original prediction generated without bootstrapping and had less variance than GBM. SBM was tested on different datasets from IsoMAP with different numbers of observation sites. We determined that predictions from the datasets with fewer than 40 observation sites using SBM were more variable than the original prediction. The approaches we used for estimating uncertainty will be compiled in an R package that is under development. We expect that these robust estimates of precipitation isoscape uncertainty can be applied in diagnosing the origin of samples ranging from various type of waters to migratory animals, food products, and humans.

Bayesian quantitative precipitation forecasts in terms of quantiles

NASA Astrophysics Data System (ADS)

Bentzien, Sabrina; Friederichs, Petra

2014-05-01

Ensemble prediction systems (EPS) for numerical weather predictions on the mesoscale are particularly developed to obtain probabilistic guidance for high impact weather. An EPS not only issues a deterministic future state of the atmosphere but a sample of possible future states. Ensemble postprocessing then translates such a sample of forecasts into probabilistic measures. This study focus on probabilistic quantitative precipitation forecasts in terms of quantiles. Quantiles are particular suitable to describe precipitation at various locations, since no assumption is required on the distribution of precipitation. The focus is on the prediction during high-impact events and related to the Volkswagen Stiftung funded project WEX-MOP (Mesoscale Weather Extremes - Theory, Spatial Modeling and Prediction). Quantile forecasts are derived from the raw ensemble and via quantile regression. Neighborhood method and time-lagging are effective tools to inexpensively increase the ensemble spread, which results in more reliable forecasts especially for extreme precipitation events. Since an EPS provides a large amount of potentially informative predictors, a variable selection is required in order to obtain a stable statistical model. A Bayesian formulation of quantile regression allows for inference about the selection of predictive covariates by the use of appropriate prior distributions. Moreover, the implementation of an additional process layer for the regression parameters accounts for spatial variations of the parameters. Bayesian quantile regression and its spatially adaptive extension is illustrated for the German-focused mesoscale weather prediction ensemble COSMO-DE-EPS, which runs (pre)operationally since December 2010 at the German Meteorological Service (DWD). Objective out-of-sample verification uses the quantile score (QS), a weighted absolute error between quantile forecasts and observations. The QS is a proper scoring function and can be decomposed into

Comparison Of Quantitative Precipitation Estimates Derived From Rain Gauge And Radar Derived Algorithms For Operational Flash Flood Support.

NASA Astrophysics Data System (ADS)

Streubel, D. P.; Kodama, K.

2014-12-01

To provide continuous flash flood situational awareness and to better differentiate severity of ongoing individual precipitation events, the National Weather Service Research Distributed Hydrologic Model (RDHM) is being implemented over Hawaii and Alaska. In the implementation process of RDHM, three gridded precipitation analyses are used as forcing. The first analysis is a radar only precipitation estimate derived from WSR-88D digital hybrid reflectivity, a Z-R relationship and aggregated into an hourly ¼ HRAP grid. The second analysis is derived from a rain gauge network and interpolated into an hourly ¼ HRAP grid using PRISM climatology. The third analysis is derived from a rain gauge network where rain gauges are assigned static pre-determined weights to derive a uniform mean areal precipitation that is applied over a catchment on a ¼ HRAP grid. To assess the effect of different QPE analyses on the accuracy of RDHM simulations and to potentially identify a preferred analysis for operational use, each QPE was used to force RDHM to simulate stream flow for 20 USGS peak flow events. An evaluation of the RDHM simulations was focused on peak flow magnitude, peak flow timing, and event volume accuracy to be most relevant for operational use. Results showed RDHM simulations based on the observed rain gauge amounts were more accurate in simulating peak flow magnitude and event volume relative to the radar derived analysis. However this result was not consistent for all 20 events nor was it consistent for a few of the rainfall events where an annual peak flow was recorded at more than one USGS gage. Implications of this indicate that a more robust QPE forcing with the inclusion of uncertainty derived from the three analyses may provide a better input for simulating extreme peak flow events.

Surface Runoff Estimation Using SMOS Observations, Rain-gauge Measurements and Satellite Precipitation Estimations. Comparison with Model Predictions

NASA Astrophysics Data System (ADS)

Garcia Leal, Julio A.; Lopez-Baeza, Ernesto; Khodayar, Samiro; Estrela, Teodoro; Fidalgo, Arancha; Gabaldo, Onofre; Kuligowski, Robert; Herrera, Eddy

Surface runoff is defined as the amount of water that originates from precipitation, does not infiltrates due to soil saturation and therefore circulates over the surface. A good estimation of runoff is useful for the design of draining systems, structures for flood control and soil utilisation. For runoff estimation there exist different methods such as (i) rational method, (ii) isochrone method, (iii) triangular hydrograph, (iv) non-dimensional SCS hydrograph, (v) Temez hydrograph, (vi) kinematic wave model, represented by the dynamics and kinematics equations for a uniforme precipitation regime, and (vii) SCS-CN (Soil Conservation Service Curve Number) model. This work presents a way of estimating precipitation runoff through the SCS-CN model, using SMOS (Soil Moisture and Ocean Salinity) mission soil moisture observations and rain-gauge measurements, as well as satellite precipitation estimations. The area of application is the Jucar River Basin Authority area where one of the objectives is to develop the SCS-CN model in a spatial way. The results were compared to simulations performed with the 7-km COSMO-CLM (COnsortium for Small-scale MOdelling, COSMO model in CLimate Mode) model. The use of SMOS soil moisture as input to the COSMO-CLM model will certainly improve model simulations.

Precipitation Estimation Using Combined Radar/Radiometer Measurements Within the GPM Framework

NASA Technical Reports Server (NTRS)

Hou, Arthur

2012-01-01

The Global Precipitation Measurement (GPM) Mission is an international satellite mission specifically designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. The GPM mission centers upon the deployment of a Core Observatory in a 65o non-Sun-synchronous orbit to serve as a physics observatory and a transfer standard for intersatellite calibration of constellation radiometers. The GPM Core Observatory will carry a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a conical-scanning multi-channel (10-183 GHz) GPM Microwave Radiometer (GMI). The DPR will be the first dual-frequency radar in space to provide not only measurements of 3-D precipitation structures but also quantitative information on microphysical properties of precipitating particles needed for improving precipitation retrievals from microwave sensors. The DPR and GMI measurements will together provide a database that relates vertical hydrometeor profiles to multi-frequency microwave radiances over a variety of environmental conditions across the globe. This combined database will be used as a common transfer standard for improving the accuracy and consistency of precipitation retrievals from all constellation radiometers. For global coverage, GPM relies on existing satellite programs and new mission opportunities from a consortium of partners through bilateral agreements with either NASA or JAXA. Each constellation member may have its unique scientific or operational objectives but contributes microwave observations to GPM for the generation and dissemination of unified global precipitation data products. In addition to the DPR and GMI on the Core Observatory, the baseline GPM constellation consists of the following sensors: (1) Special Sensor Microwave Imager/Sounder (SSMIS) instruments on the U.S. Defense Meteorological Satellite Program (DMSP) satellites, (2) the Advanced Microwave Scanning Radiometer-2 (AMSR-2) on the GCOM-W1

A Comparison of Multisensor Precipitation Estimation Methods in Complex Terrain for Flash Flood Warning and Mitigation

NASA Astrophysics Data System (ADS)

Cifelli, R.; Chen, H.; Chandrasekar, C. V.; Willie, D.; Reynolds, D.; Campbell, C.; Zhang, Y.; Sukovich, E.

2012-12-01

Investigating the uncertainties and improving the accuracy of quantitative precipitation estimation (QPE) is a critical mission of the National Oceanic and Atmospheric Administration (NOAA). QPE is extremely challenging in regions of complex terrain like the western U.S. because of the sparse coverage of ground-based radar, complex orographic precipitation processes, and the effects of beam blockages (e.g., Westrick et al. 1999). In addition, the rain gauge density in complex terrain is often inadequate to capture spatial variability in the precipitation patterns. The NOAA Hydrometeorology Testbed (HMT) conducts research on precipitation and weather conditions that can lead to flooding, and fosters transition of scientific advances and new tools into forecasting operations (see hmt.noaa.gov). The HMT program consists of a series of demonstration projects in different geographical regions to enhance understanding of region specific processes related to precipitation, including QPE. There are a number of QPE systems that are widely used across NOAA for precipitation estimation (e.g., Cifelli et al. 2011; Chandrasekar et al. 2012). Two of these systems have been installed at the NOAA Earth System Research Laboratory: Multisensor Precipitation Estimator (MPE) and National Mosaic and Multi-sensor QPE (NMQ) developed by NWS and NSSL, respectively. Both provide gridded QPE products that include radar-only, gauge-only and gauge-radar-merged, etc; however, these systems often provide large differences in QPE (in terms of amounts and spatial patterns) due to differences in Z-R selection, vertical profile of reflectivity correction, and gauge interpolation procedures. Determining the appropriate QPE product and quantification of QPE uncertainty is critical for operational applications, including water management decisions and flood warnings. For example, hourly QPE is used to correct radar based rain rates used by the Flash Flood Monitoring and Prediction (FFMP) package in

Synchronous precipitation reduction in the American Tropics associated with Heinrich 2.

PubMed

Medina-Elizalde, Martín; Burns, Stephen J; Polanco-Martinez, Josué; Lases-Hernández, Fernanda; Bradley, Raymond; Wang, Hao-Cheng; Shen, Chuan-Chou

2017-09-11

During the last ice age temperature in the North Atlantic oscillated in cycles known as Dansgaard-Oeschger (D-O) events. The magnitude of Caribbean hydroclimate change associated with D-O variability and particularly with stadial intervals, remains poorly constrained by paleoclimate records. We present a 3.3 thousand-year long stalagmite δ 18 O record from the Yucatan Peninsula (YP) that spans the interval between 26.5 and 23.2 thousand years before present. We estimate quantitative precipitation variability and the high resolution and dating accuracy of this record allow us to investigate how rainfall in the region responds to D-O events. Quantitative precipitation estimates are based on observed regional amount effect variability, last glacial paleotemperature records, and estimates of the last glacial oxygen isotopic composition of precipitation based on global circulation models (GCMs). The new precipitation record suggests significant low latitude hydrological responses to internal modes of climate variability and supports a role of Caribbean hydroclimate in helping Atlantic Meridional Overturning Circulation recovery during D-O events. Significant in-phase precipitation reduction across the equator in the tropical Americas associated with Heinrich event 2 is suggested by available speleothem oxygen isotope records.

Confidence estimation for quantitative photoacoustic imaging

NASA Astrophysics Data System (ADS)

Gröhl, Janek; Kirchner, Thomas; Maier-Hein, Lena

2018-02-01

Quantification of photoacoustic (PA) images is one of the major challenges currently being addressed in PA research. Tissue properties can be quantified by correcting the recorded PA signal with an estimation of the corresponding fluence. Fluence estimation itself, however, is an ill-posed inverse problem which usually needs simplifying assumptions to be solved with state-of-the-art methods. These simplifications, as well as noise and artifacts in PA images reduce the accuracy of quantitative PA imaging (PAI). This reduction in accuracy is often localized to image regions where the assumptions do not hold true. This impedes the reconstruction of functional parameters when averaging over entire regions of interest (ROI). Averaging over a subset of voxels with a high accuracy would lead to an improved estimation of such parameters. To achieve this, we propose a novel approach to the local estimation of confidence in quantitative reconstructions of PA images. It makes use of conditional probability densities to estimate confidence intervals alongside the actual quantification. It encapsulates an estimation of the errors introduced by fluence estimation as well as signal noise. We validate the approach using Monte Carlo generated data in combination with a recently introduced machine learning-based approach to quantitative PAI. Our experiments show at least a two-fold improvement in quantification accuracy when evaluating on voxels with high confidence instead of thresholding signal intensity.

A TRMM-Based System for Real-Time Quasi-Global Merged Precipitation Estimates

NASA Technical Reports Server (NTRS)

Starr, David OC. (Technical Monitor); Huffman, G. J.; Adler, R. F.; Stocker, E. F.; Bolvin, D. T.; Nelkin, E. J.

2002-01-01

A new processing system has been developed to combine IR and microwave data into 0.25 degree x 0.25 degree gridded precipitation estimates in near-real time over the latitude band plus or minus 50 degrees. Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) precipitation estimates are used to calibrate Special Sensor Microwave/Imager (SSM/I) estimates, and Advanced Microwave Sounding Unit (AMSU) and Advanced Microwave Scanning Radiometer (AMSR) estimates, when available. The merged microwave estimates are then used to create a calibrated IR estimate in a Probability-Matched-Threshold approach for each individual hour. The microwave and IR estimates are combined for each 3-hour interval. Early results will be shown, including typical tropical and extratropical storm evolution and examples of the diurnal cycle. Major issues will be discussed, including the choice of IR algorithm, the approach for merging the IR and microwave estimates, extension to higher latitudes, retrospective processing back to 1999, and extension to the GPCP One-Degree Daily product (for which the authors are responsible). The work described here provides one approach to using data from the future NASA Global Precipitation Measurement program, which is designed to provide Jill global coverage by low-orbit passive microwave satellites every three hours beginning around 2008.

Using GRACE to constrain precipitation amount over cold mountainous basins

NASA Astrophysics Data System (ADS)

Behrangi, Ali; Gardner, Alex S.; Reager, John T.; Fisher, Joshua B.

2017-01-01

Despite the importance for hydrology and climate-change studies, current quantitative knowledge on the amount and distribution of precipitation in mountainous and high-elevation regions is limited due to instrumental and retrieval shortcomings. Here by focusing on two large endorheic basins in High Mountain Asia, we show that satellite gravimetry (Gravity Recovery and Climate Experiment (GRACE)) can be used to provide an independent estimate of monthly accumulated precipitation using mass balance equation. Results showed that the GRACE-based precipitation estimate has the highest agreement with most of the commonly used precipitation products in summer, but it deviates from them in cold months, when the other products are expected to have larger errors. It was found that most of the products capture about or less than 50% of the total precipitation estimated using GRACE in winter. Overall, Global Precipitation Climatology Project (GPCP) showed better agreement with GRACE estimate than other products. Yet on average GRACE showed 30% more annual precipitation than GPCP in the study basins. In basins of appropriate size with an absence of dense ground measurements, as is a typical case in cold mountainous regions, we find GRACE can be a viable alternative to constrain monthly and seasonal precipitation estimates from other remotely sensed precipitation products that show large bias.

A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region

NASA Astrophysics Data System (ADS)

Prakash, Satya; Mitra, Ashis K.; AghaKouchak, Amir; Liu, Zhong; Norouzi, Hamidreza; Pai, D. S.

2018-01-01

Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high resolution multi-satellite precipitation products namely, Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) version 6 are released. A critical evaluation of these newly released precipitation data sets is very important for both the end users and data developers. This study provides a comprehensive assessment of IMERG research product and GSMaP estimates over India at a daily scale for the southwest monsoon season (June to September 2014). The GPM-based precipitation products are inter-compared with widely used TRMM Multi-satellite Precipitation Analysis (TMPA), and gauge-based observations over India. Results show that the IMERG estimates represent the mean monsoon rainfall and its variability more realistically than the gauge-adjusted TMPA and GSMaP data. However, GSMaP has relatively smaller root-mean-square error than IMERG and TMPA, especially over the low mean rainfall regimes and along the west coast of India. An entropy-based approach is employed to evaluate the distributions of the selected precipitation products. The results indicate that the distribution of precipitation in IMERG and GSMaP has been improved markedly, especially for low precipitation rates. IMERG shows a clear improvement in missed and false precipitation bias over India. However, all the three satellite-based rainfall estimates show exceptionally smaller correlation coefficient, larger RMSE, larger negative total bias and hit bias over the northeast India where precipitation is dominated by orographic effects. Similarly, the three satellite-based estimates show larger false precipitation over the southeast peninsular India which is a rain-shadow region. The categorical verification confirms that these satellite-based rainfall estimates have difficulties in detection of rain over the southeast peninsula and northeast India. These

Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically-Based Retrieval Scheme

NASA Technical Reports Server (NTRS)

Kidd, Chris; Matsui, Toshi; Chern, Jiundar; Mohr, Karen; Kummerow, Christian; Randel, Dave

2015-01-01

The estimation of precipitation across the globe from satellite sensors provides a key resource in the observation and understanding of our climate system. Estimates from all pertinent satellite observations are critical in providing the necessary temporal sampling. However, consistency in these estimates from instruments with different frequencies and resolutions is critical. This paper details the physically based retrieval scheme to estimate precipitation from cross-track (XT) passive microwave (PM) sensors on board the constellation satellites of the Global Precipitation Measurement (GPM) mission. Here the Goddard profiling algorithm (GPROF), a physically based Bayesian scheme developed for conically scanning (CS) sensors, is adapted for use with XT PM sensors. The present XT GPROF scheme utilizes a model-generated database to overcome issues encountered with an observational database as used by the CS scheme. The model database ensures greater consistency across meteorological regimes and surface types by providing a more comprehensive set of precipitation profiles. The database is corrected for bias against the CS database to ensure consistency in the final product. Statistical comparisons over western Europe and the United States show that the XT GPROF estimates are comparable with those from the CS scheme. Indeed, the XT estimates have higher correlations against surface radar data, while maintaining similar root-mean-square errors. Latitudinal profiles of precipitation show the XT estimates are generally comparable with the CS estimates, although in the southern midlatitudes the peak precipitation is shifted equatorward while over the Arctic large differences are seen between the XT and the CS retrievals.

Improving Frozen Precipitation Density Estimation in Land Surface Modeling

NASA Astrophysics Data System (ADS)

Sparrow, K.; Fall, G. M.

2017-12-01

The Office of Water Prediction (OWP) produces high-value water supply and flood risk planning information through the use of operational land surface modeling. Improvements in diagnosing frozen precipitation density will benefit the NWS's meteorological and hydrological services by refining estimates of a significant and vital input into land surface models. A current common practice for handling the density of snow accumulation in a land surface model is to use a standard 10:1 snow-to-liquid-equivalent ratio (SLR). Our research findings suggest the possibility of a more skillful approach for assessing the spatial variability of precipitation density. We developed a 30-year SLR climatology for the coterminous US from version 3.22 of the Daily Global Historical Climatology Network - Daily (GHCN-D) dataset. Our methods followed the approach described by Baxter (2005) to estimate mean climatological SLR values at GHCN-D sites in the US, Canada, and Mexico for the years 1986-2015. In addition to the Baxter criteria, the following refinements were made: tests were performed to eliminate SLR outliers and frequent reports of SLR = 10, a linear SLR vs. elevation trend was fitted to station SLR mean values to remove the elevation trend from the data, and detrended SLR residuals were interpolated using ordinary kriging with a spherical semivariogram model. The elevation values of each station were based on the GMTED 2010 digital elevation model and the elevation trend in the data was established via linear least squares approximation. The ordinary kriging procedure was used to interpolate the data into gridded climatological SLR estimates for each calendar month at a 0.125 degree resolution. To assess the skill of this climatology, we compared estimates from our SLR climatology with observations from the GHCN-D dataset to consider the potential use of this climatology as a first guess of frozen precipitation density in an operational land surface model. The difference in

Constraining precipitation amount and distribution over cold regions using GRACE

NASA Astrophysics Data System (ADS)

Behrangi, A.; Reager, J. T., II; Gardner, A. S.; Fisher, J.

2017-12-01

Current quantitative knowledge on the amount and distribution of precipitation in high-elevation and high latitude regions is limited due to instrumental and retrieval shortcomings. Here we demonstrate how that satellite gravimetry (Gravity Recovery and Climate Experiment, GRACE) can be used to provide an independent estimate of monthly accumulated precipitation using mass balance. Results showed that the GRACE-based precipitation estimate has the highest agreement with most of the commonly used precipitation products in summer, but it deviates from them in cold months, when the other products are expected to have larger error. We also observed that as near surface temperature decreases products tend to underestimate accumulated precipitation retrieved from GRACE. The analysis performed using various products such as GPCP, GPCC, TRMM, and gridded station data over vast regions in high latitudes and two large endorheic basins in High Mountain Asia. Based on the analysis over High Mountain Asia it was found that most of the products capture about or less than 50% of the total precipitation estimated using GRACE in winter. Overall, GPCP showed better agreement with GRACE estimate than other products. Yet on average GRACE showed 30% more annual precipitation than GPCP in the study basin.

Multi-scale Quantitative Precipitation Forecasting Using ...

EPA Pesticide Factsheets

Global sea surface temperature (SST) anomalies can affect terrestrial precipitation via ocean-atmosphere interaction known as climate teleconnection. Non-stationary and non-linear characteristics of the ocean-atmosphere system make the identification of the teleconnection signals difficult to be detected at a local scale as it could cause large uncertainties when using linear correlation analysis only. This paper explores the relationship between global SST and terrestrial precipitation with respect to long-term non-stationary teleconnection signals during 1981-2010 over three regions in North America and one in Central America. Empirical mode decomposition as well as wavelet analysis is utilized to extract the intrinsic trend and the dominant oscillation of the SST and precipitation time series in sequence. After finding possible associations between the dominant oscillation of seasonal precipitation and global SST through lagged correlation analysis, the statistically significant SST regions are extracted based on the correlation coefficient. With these characterized associations, individual contribution of these SST forcing regions linked to the related precipitation responses are further quantified through nonlinear modeling with the aid of extreme learning machine. Results indicate that the non-leading SST regions also contribute a salient portion to the terrestrial precipitation variability compared to some known leading SST regions. In some cases, these

Analyzing Spatial and Temporal Variation in Precipitation Estimates in a Coupled Model

NASA Astrophysics Data System (ADS)

Tomkins, C. D.; Springer, E. P.; Costigan, K. R.

2001-12-01

Integrated modeling efforts at the Los Alamos National Laboratory aim to simulate the hydrologic cycle and study the impacts of climate variability and land use changes on water resources and ecosystem function at the regional scale. The integrated model couples three existing models independently responsible for addressing the atmospheric, land surface, and ground water components: the Regional Atmospheric Model System (RAMS), the Los Alamos Distributed Hydrologic System (LADHS), and the Finite Element and Heat Mass (FEHM). The upper Rio Grande Basin, extending 92,000 km2 over northern New Mexico and southern Colorado, serves as the test site for this model. RAMS uses nested grids to simulate meteorological variables, with the smallest grid over the Rio Grande having 5-km horizontal grid spacing. As LADHS grid spacing is 100 m, a downscaling approach is needed to estimate meteorological variables from the 5km RAMS grid for input into LADHS. This study presents daily and cumulative precipitation predictions, in the month of October for water year 1993, and an approach to compare LADHS downscaled precipitation to RAMS-simulated precipitation. The downscaling algorithm is based on kriging, using topography as a covariate to distribute the precipitation and thereby incorporating the topographical resolution achieved at the 100m-grid resolution in LADHS. The results of the downscaling are analyzed in terms of the level of variance introduced into the model, mean simulated precipitation, and the correlation between the LADHS and RAMS estimates. Previous work presented a comparison of RAMS-simulated and observed precipitation recorded at COOP and SNOTEL sites. The effects of downscaling the RAMS precipitation were evaluated using Spearman and linear correlations and by examining the variance of both populations. The study focuses on determining how the downscaling changes the distribution of precipitation compared to the RAMS estimates. Spearman correlations computed for

Analysis of long term trends of precipitation estimates acquired using radar network in Turkey

NASA Astrophysics Data System (ADS)

Tugrul Yilmaz, M.; Yucel, Ismail; Kamil Yilmaz, Koray

2016-04-01

Precipitation estimates, a vital input in many hydrological and agricultural studies, can be obtained using many different platforms (ground station-, radar-, model-, satellite-based). Satellite- and model-based estimates are spatially continuous datasets, however they lack the high resolution information many applications often require. Station-based values are actual precipitation observations, however they suffer from their nature that they are point data. These datasets may be interpolated however such end-products may have large errors over remote locations with different climate/topography/etc than the areas stations are installed. Radars have the particular advantage of having high spatial resolution information over land even though accuracy of radar-based precipitation estimates depends on the Z-R relationship, mountain blockage, target distance from the radar, spurious echoes resulting from anomalous propagation of the radar beam, bright band contamination and ground clutter. A viable method to obtain spatially and temporally high resolution consistent precipitation information is merging radar and station data to take advantage of each retrieval platform. An optimally merged product is particularly important in Turkey where complex topography exerts strong controls on the precipitation regime and in turn hampers observation efforts. There are currently 10 (additional 7 are planned) weather radars over Turkey obtaining precipitation information since 2007. This study aims to optimally merge radar precipitation data with station based observations to introduce a station-radar blended precipitation product. This study was supported by TUBITAK fund # 114Y676.

Evaluation of spatial and spatiotemporal estimation methods in simulation of precipitation variability patterns

NASA Astrophysics Data System (ADS)

Bayat, Bardia; Zahraie, Banafsheh; Taghavi, Farahnaz; Nasseri, Mohsen

2013-08-01

Identification of spatial and spatiotemporal precipitation variations plays an important role in different hydrological applications such as missing data estimation. In this paper, the results of Bayesian maximum entropy (BME) and ordinary kriging (OK) are compared for modeling spatial and spatiotemporal variations of annual precipitation with and without incorporating elevation variations. The study area of this research is Namak Lake watershed located in the central part of Iran with an area of approximately 90,000 km2. The BME and OK methods have been used to model the spatial and spatiotemporal variations of precipitation in this watershed, and their performances have been evaluated using cross-validation statistics. The results of the case study have shown the superiority of BME over OK in both spatial and spatiotemporal modes. The results have shown that BME estimates are less biased and more accurate than OK. The improvements in the BME estimates are mostly related to incorporating hard and soft data in the estimation process, which resulted in more detailed and reliable results. Estimation error variance for BME results is less than OK estimations in the study area in both spatial and spatiotemporal modes.

Satellite estimates of precipitation susceptibility in low-level marine stratiform clouds

DOE PAGES

Terai, C. R.; Wood, R.; Kubar, T. L.

2015-09-05

Quantifying the sensitivity of warm rain to aerosols is important for constraining climate model estimates of aerosol indirect effects. In this study, the precipitation sensitivity to cloud droplet number concentration (N d) in satellite retrievals is quantified by applying the precipitation susceptibility metric to a combined CloudSat/Moderate Resolution Imaging Spectroradiometer data set of stratus and stratocumulus clouds that cover the tropical and subtropical Pacific Ocean and Gulf of Mexico. We note that consistent with previous observational studies of marine stratocumulus, precipitation susceptibility decreases with increasing liquid water path (LWP), and the susceptibility of the mean precipitation rate R is nearlymore » equal to the sum of the susceptibilities of precipitation intensity and of probability of precipitation. Consistent with previous modeling studies, the satellite retrievals reveal that precipitation susceptibility varies not only with LWP but also with N d. Puzzlingly, negative values of precipitation susceptibility are found at low LWP and high N d. There is marked regional variation in precipitation susceptibility values that cannot simply be explained by regional variations in LWP and N d. This suggests other controls on precipitation apart from LWP and N d and that precipitation susceptibility will need to be quantified and understood at the regional scale when relating to its role in controlling possible aerosol-induced cloud lifetime effects.« less

Operational Estimation of Accumulated Precipitation using Satellite Observation, by Eumetsat Satellite Application facility in Support to Hydrology (H-SAF Consortium).

NASA Astrophysics Data System (ADS)

di Diodato, A.; de Leonibus, L.; Zauli, F.; Biron, D.; Melfi, D.

2009-04-01

compared by climatic thresholds got, basically, by the project "Climate Atlas of Europe" led by Meteo France inside the project ECSN (European Climate Support Network) of EUMETNET. To reduce the bias errors introduced by satellite estimates the rain gauge data are used to make an intercalibration with the satellite estimates, using information achieved by GTS network. Precipitation increments are estimated at each observation location from the observation and the interpolated background field. A field of the increments is carried out by standard Kriging method. The final precipitation analysis is achieved by the sum of the increments and the precipitation estimation at each grid points. It is also considered that major error sources in retrieval 15 minutes instantaneous precipitation from cloud top temperature comes from high (cold) non precipitating clouds and the use of same regression coefficients both for warm clouds (stratus) and cold clouds (convective). As that error is intrinsic in the blending technique applied, we are going to improve performances making use of cloud type specified retrievals. To apply such scheme on the products, we apply a discrimination from convective and stratified clouds, then we retrieve precipitation in parallel for the two clouds classes; the two outputs are merged again into one products, solving the double retrieval pixels keeping the convection retrieval. Basic tools for that is the computation of two different lookup tables to associate precipitation at a brightness temperature for the two kinds of cloudiness. The clouds discrimination will be done by the NWC-SAF product named "cloud type" for the stratified clouds and with an application, running operationally at Italian Met Service, named NEFODINA for automatic detection of convective phenomena. Results of studies to improve the accumulated precipitation as well are presented. The studies exploit the potential to use other source of information like quantitative precipitation

Estimating Reservoir Inflow Using RADAR Forecasted Precipitation and Adaptive Neuro Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Yi, J.; Choi, C.

2014-12-01

Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.

Quantitative reconstruction of summer precipitation using a mid-Holocene δ13C common millet record from Guanzhong Basin, northern China

NASA Astrophysics Data System (ADS)

Yang, Qing; Li, Xiaoqiang; Zhou, Xinying; Zhao, Keliang; Sun, Nan

2016-12-01

To quantitatively reconstruct Holocene precipitation for particular geographical areas, suitable proxies and faithful dating controls are required. The fossilized seeds of common millet (Panicum miliaceum) are found throughout the sedimentary strata of northern China and are suited to the production of quantitative Holocene precipitation reconstructions: their isotopic carbon composition (δ13C) gives a measure of the precipitation required during the growing season of summer (here the interval from mid-June to September) and allows these seeds to be dated. We therefore used a regression function, as part of a systematic study of the δ13C of common millet, to produce a quantitative reconstruction of mid-Holocene summer precipitation in the Guanzhong Basin (107°40'-107°49' E, 33°39'-34°45' N). Our results showed that mean summer precipitation at 7.7-3.4 ka BP was 353 mm, ˜ 50 mm or 17 % higher than present levels, and the variability increased, especially after 5.2 ka BP. Maximum mean summer precipitation peaked at 414 mm during the period 6.1-5.5 ka BP, ˜ 109 mm (or 36 %) higher than today, indicating that the East Asian summer monsoon (EASM) peaked at this time. This work can provide a new proxy for further research into continuous paleoprecipitation sequences and the variability of summer precipitation, which will promote the further research into the relation between early human activity and environmental change.

Radar-based quantitative precipitation estimation for the identification of debris flow occurrence over earthquake-affected regions in Sichuan, China

NASA Astrophysics Data System (ADS)

Shi, Zhao; Wei, Fangqiang; Chandrasekar, Venkatachalam

2018-03-01

Both Ms 8.0 Wenchuan earthquake on 12 May 2008 and Ms 7.0 Lushan earthquake on 20 April 2013 occurred in the province of Sichuan, China. In the earthquake-affected mountainous area, a large amount of loose material caused a high occurrence of debris flow during the rainy season. In order to evaluate the rainfall intensity-duration (I-D) threshold of the debris flow in the earthquake-affected area, and to fill up the observational gaps caused by the relatively scarce and low-altitude deployment of rain gauges in this area, raw data from two S-band China New Generation Doppler Weather Radar (CINRAD) were captured for six rainfall events that triggered 519 debris flows between 2012 and 2014. Due to the challenges of radar quantitative precipitation estimation (QPE) over mountainous areas, a series of improvement measures are considered: a hybrid scan mode, a vertical reflectivity profile (VPR) correction, a mosaic of reflectivity, a merged rainfall-reflectivity (R - Z) relationship for convective and stratiform rainfall, and rainfall bias adjustment with Kalman filter (KF). For validating rainfall accumulation over complex terrains, the study areas are divided into two kinds of regions by the height threshold of 1.5 km from the ground. Three kinds of radar rainfall estimates are compared with rain gauge measurements. It is observed that the normalized mean bias (NMB) is decreased by 39 % and the fitted linear ratio between radar and rain gauge observation reaches at 0.98. Furthermore, the radar-based I-D threshold derived by the frequentist method is I = 10.1D-0.52 and is underestimated by uncorrected raw radar data. In order to verify the impacts on observations due to spatial variation, I-D thresholds are identified from the nearest rain gauge observations and radar observations at the rain gauge locations. It is found that both kinds of observations have similar I-D thresholds and likewise underestimate I-D thresholds due to undershooting at the core of convective

Quantitative analysis of precipitation over Fukushima to understand the wet deposition process in March 2011

NASA Astrophysics Data System (ADS)

Yatagai, A.; Onda, Y.; Watanabe, A.

2012-04-01

The Great East Japan Earthquake caused a severe accident at the Fukushima-Daiichi nuclear power plant (NPP), leading to the emission of large amounts of radioactive pollutants into the environment. The transport and diffusion of these radioactive pollutants in the atmosphere caused a disaster for residents in and around Fukushima. Studies have sought to understand the transport, diffusion, and deposition process, and to understand the movement of radioactive pollutants through the soil, vegetation, rivers, and groundwater. However, a detailed simulation and understanding of the distribution of radioactive compounds depend on a simulation of precipitation and on the information on the timing of the emission of these radioactive pollutants from the NPP. Past nuclear expansion studies have demonstrated the importance of wet deposition in distributing pollutants. Hence, this study examined the quantitative precipitation pattern in March 2011 using rain-gauge observations and X-band radar data from Fukushima University. We used the AMeDAS rain-gauge network data of 1) the Japan Meteorological Agency (1273 stations in Japan) and 2) the Water Information System (47 stations in Fukushima prefecture) and 3) the rain-gauge data of the Environmental Information Network of NTT Docomo (30 stations in Fukushima) to construct 0.05-degree mesh data using the same method used to create the APHRODITE daily grid precipitation data (Yatagai et al., 2009). Since some AMeDAS data for the coastal region were lost due to the earthquake, the complementary network of 2) and 3) yielded better precipitation estimates. The data clarified that snowfall was observed on the night of Mar 15 into the morning of Mar 16 throughout Fukushima prefecture. This had an important effect on the radioactive contamination pattern in Fukushima prefecture. The precipitation pattern itself does not show one-on-one correspondence with the contamination pattern. While the pollutants transported northeast of the

Combining Radar and Daily Precipitation Data to Estimate Meaningful Sub-daily Precipitation Extremes

NASA Astrophysics Data System (ADS)

Pegram, G. G. S.; Bardossy, A.

2016-12-01

Short duration extreme rainfalls are important for design. The purpose of this presentation is not to improve the day by day estimation of precipitation, but to obtain reasonable statistics for the subdaily extremes at gauge locations. We are interested specifically in daily and sub-daily extreme values of precipitation at gauge locations. We do not employ the common procedure of using time series of control station to determine the missing data values in a target. We are interested in individual rare events, not sequences. The idea is to use radar to disaggregate daily totals to sub-daily amounts. In South Arica, an S-band radar operated relatively continuously at Bethlehem from 1998 to 2003, whose scan at 1.5 km above ground [CAPPI] overlapped a dense (10 km spacing) set of 45 pluviometers recording in the same 6-year period. Using this valuable set of data, we are only interested in rare extremes, therefore small to medium values of rainfall depth were neglected, leaving 12 days of ranked daily maxima in each set per year, whose sum typically comprised about 50% of each annual rainfall total. The method presented here uses radar for disaggregating daily gauge totals in subdaily intervals down to 15 minutes in order to extract the maxima of sub-hourly through to daily rainfall at each of 37 selected radar pixels [1 km square in plan] which contained one of the 45 pluviometers not masked out by the radar foot-print. The pluviometer data were aggregated to daily totals, to act as if they were daily read gauges; their only other task was to help in the cross-validation exercise. The extrema were obtained as quantiles by ordering the 12 daily maxima of each interval per year. The unusual and novel goal was not to obtain the reproduction of the precipitation matching in space and time, but to obtain frequency distributions of the gauge and radar extremes, by matching their ranks, which we found to be stable and meaningful in cross-validation tests. We provide and

Merging Satellite Precipitation Products for Improved Streamflow Simulations

NASA Astrophysics Data System (ADS)

Maggioni, V.; Massari, C.; Barbetta, S.; Camici, S.; Brocca, L.

2017-12-01

Accurate quantitative precipitation estimation is of great importance for water resources management, agricultural planning and forecasting and monitoring of natural hazards such as flash floods and landslides. In situ observations are limited around the Earth, especially in remote areas (e.g., complex terrain, dense vegetation), but currently available satellite precipitation products are able to provide global precipitation estimates with an accuracy that depends upon many factors (e.g., type of storms, temporal sampling, season, etc.). The recent SM2RAIN approach proposes to estimate rainfall by using satellite soil moisture observations. As opposed to traditional satellite precipitation methods, which sense cloud properties to retrieve instantaneous estimates, this new bottom-up approach makes use of two consecutive soil moisture measurements for obtaining an estimate of the fallen precipitation within the interval between two satellite overpasses. As a result, the nature of the measurement is different and complementary to the one of classical precipitation products and could provide a different valid perspective to substitute or improve current rainfall estimates. Therefore, we propose to merge SM2RAIN and the widely used TMPA 3B42RT product across Italy for a 6-year period (2010-2015) at daily/0.25deg temporal/spatial scale. Two conceptually different merging techniques are compared to each other and evaluated in terms of different statistical metrics, including hit bias, threat score, false alarm rates, and missed rainfall volumes. The first is based on the maximization of the temporal correlation with a reference dataset, while the second is based on a Bayesian approach, which provides a probabilistic satellite precipitation estimate derived from the joint probability distribution of observations and satellite estimates. The merged precipitation products show a better performance with respect to the parental satellite-based products in terms of categorical

An "Ensemble Approach" to Modernizing Extreme Precipitation Estimation for Dam Safety Decision-Making

NASA Astrophysics Data System (ADS)

Cifelli, R.; Mahoney, K. M.; Webb, R. S.; McCormick, B.

2017-12-01

To ensure structural and operational safety of dams and other water management infrastructure, water resources managers and engineers require information about the potential for heavy precipitation. The methods and data used to estimate extreme rainfall amounts for managing risk are based on 40-year-old science and in need of improvement. The need to evaluate new approaches based on the best science available has led the states of Colorado and New Mexico to engage a body of scientists and engineers in an innovative "ensemble approach" to updating extreme precipitation estimates. NOAA is at the forefront of one of three technical approaches that make up the "ensemble study"; the three approaches are conducted concurrently and in collaboration with each other. One approach is the conventional deterministic, "storm-based" method, another is a risk-based regional precipitation frequency estimation tool, and the third is an experimental approach utilizing NOAA's state-of-the-art High Resolution Rapid Refresh (HRRR) physically-based dynamical weather prediction model. The goal of the overall project is to use the individual strengths of these different methods to define an updated and broadly acceptable state of the practice for evaluation and design of dam spillways. This talk will highlight the NOAA research and NOAA's role in the overarching goal to better understand and characterizing extreme precipitation estimation uncertainty. The research led by NOAA explores a novel high-resolution dataset and post-processing techniques using a super-ensemble of hourly forecasts from the HRRR model. We also investigate how this rich dataset may be combined with statistical methods to optimally cast the data in probabilistic frameworks. NOAA expertise in the physical processes that drive extreme precipitation is also employed to develop careful testing and improved understanding of the limitations of older estimation methods and assumptions. The process of decision making in the

Combination of radar and daily precipitation data to estimate meaningful sub-daily point precipitation extremes

NASA Astrophysics Data System (ADS)

Bárdossy, András; Pegram, Geoffrey

2017-01-01

The use of radar measurements for the space time estimation of precipitation has for many decades been a central topic in hydro-meteorology. In this paper we are interested specifically in daily and sub-daily extreme values of precipitation at gauged or ungauged locations which are important for design. The purpose of the paper is to develop a methodology to combine daily precipitation observations and radar measurements to estimate sub-daily extremes at point locations. Radar data corrected using precipitation-reflectivity relationships lead to biased estimations of extremes. Different possibilities of correcting systematic errors using the daily observations are investigated. Observed gauged daily amounts are interpolated to unsampled points and subsequently disaggregated using the sub-daily values obtained by the radar. Different corrections based on the spatial variability and the subdaily entropy of scaled rainfall distributions are used to provide unbiased corrections of short duration extremes. Additionally a statistical procedure not based on a matching day by day correction is tested. In this last procedure as we are only interested in rare extremes, low to medium values of rainfall depth were neglected leaving a small number of L days of ranked daily maxima in each set per year, whose sum typically comprises about 50% of each annual rainfall total. The sum of these L day maxima is first iterpolated using a Kriging procedure. Subsequently this sum is disaggregated to daily values using a nearest neighbour procedure. The daily sums are then disaggregated by using the relative values of the biggest L radar based days. Of course, the timings of radar and gauge maxima can be different, so the method presented here uses radar for disaggregating daily gauge totals down to 15 min intervals in order to extract the maxima of sub-hourly through to daily rainfall. The methodologies were tested in South Africa, where an S-band radar operated relatively continuously at

Bias-correction of PERSIANN-CDR Extreme Precipitation Estimates Over the United States

NASA Astrophysics Data System (ADS)

Faridzad, M.; Yang, T.; Hsu, K. L.; Sorooshian, S.

2017-12-01

Ground-based precipitation measurements can be sparse or even nonexistent over remote regions which make it difficult for extreme event analysis. PERSIANN-CDR (CDR), with 30+ years of daily rainfall information, provides an opportunity to study precipitation for regions where ground measurements are limited. In this study, the use of CDR annual extreme precipitation for frequency analysis of extreme events over limited/ungauged basins is explored. The adjustment of CDR is implemented in two steps: (1) Calculated CDR bias correction factor at limited gauge locations based on the linear regression analysis of gauge and CDR annual maxima precipitation; and (2) Extend the bias correction factor to the locations where gauges are not available. The correction factors are estimated at gauge sites over various catchments, elevation zones, and climate regions and the results were generalized to ungauged sites based on regional and climatic similarity. Case studies were conducted on 20 basins with diverse climate and altitudes in the Eastern and Western US. Cross-validation reveals that the bias correction factors estimated on limited calibration data can be extended to regions with similar characteristics. The adjusted CDR estimates also outperform gauge interpolation on validation sites consistently. It is suggested that the CDR with bias adjustment has a potential for study frequency analysis of extreme events, especially for regions with limited gauge observations.

Downsizing a long-term precipitation network: Using a quantitative approach to inform difficult decisions

Treesearch

Mark B. Green; John L. Campbell; Ruth D. Yanai; Scott W. Bailey; Amey S. Bailey; Nicholas Grant; Ian Halm; Eric P. Kelsey; Lindsey E. Rustad

2018-01-01

The design of a precipitation monitoring network must balance the demand for accurate estimates with the resources needed to build and maintain the network. If there are changes in the objectives of the monitoring or the availability of resources, network designs should be adjusted. At the Hubbard Brook Experimental Forest in New Hampshire, USA, precipitation has been...

Estimates of inorganic nitrogen wet deposition from precipitation for the conterminous United States, 1955-84

USGS Publications Warehouse

Gronberg, Jo Ann M.; Ludtke, Amy S.; Knifong, Donna L.

2014-01-01

The U.S. Geological Survey’s National Water-Quality Assessment program requires nutrient input information for analysis of national and regional assessment of water quality. Historical data are needed to lengthen the data record for assessment of trends in water quality. This report provides estimates of inorganic nitrogen deposition from precipitation for the conterminous United States for 1955–56, 1961–65, and 1981–84. The estimates were derived from ammonium, nitrate, and inorganic nitrogen concentrations in atmospheric wet deposition and precipitation-depth data. This report documents the sources of these data and the methods that were used to estimate the inorganic nitrogen deposition. Tabular datasets, including the analytical results, precipitation depth, and calculated site-specific precipitation-weighted concentrations, and raster datasets of nitrogen from wet deposition are provided as appendixes in this report.

Kriging and local polynomial methods for blending satellite-derived and gauge precipitation estimates to support hydrologic early warning systems

USGS Publications Warehouse

Verdin, Andrew; Funk, Christopher C.; Rajagopalan, Balaji; Kleiber, William

2016-01-01

Robust estimates of precipitation in space and time are important for efficient natural resource management and for mitigating natural hazards. This is particularly true in regions with developing infrastructure and regions that are frequently exposed to extreme events. Gauge observations of rainfall are sparse but capture the precipitation process with high fidelity. Due to its high resolution and complete spatial coverage, satellite-derived rainfall data are an attractive alternative in data-sparse regions and are often used to support hydrometeorological early warning systems. Satellite-derived precipitation data, however, tend to underrepresent extreme precipitation events. Thus, it is often desirable to blend spatially extensive satellite-derived rainfall estimates with high-fidelity rain gauge observations to obtain more accurate precipitation estimates. In this research, we use two different methods, namely, ordinary kriging and κ-nearest neighbor local polynomials, to blend rain gauge observations with the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates in data-sparse Central America and Colombia. The utility of these methods in producing blended precipitation estimates at pentadal (five-day) and monthly time scales is demonstrated. We find that these blending methods significantly improve the satellite-derived estimates and are competitive in their ability to capture extreme precipitation.

GPM Precipitation Estimates over the Walnut Gulch Experimental Watershed/LTAR site in Southeastern Arizona

NASA Astrophysics Data System (ADS)

Goodrich, D. C.; Tan, J.; Petersen, W. A.; Unkrich, C. C.; Demaria, E. M.; Hazenberg, P.; Lakshmi, V.

2017-12-01

Precipitation profiles from the GPM Core Observatory Dual-frequency Precipitation Radar (DPR) form part of the a priori database used in GPM Goddard Profiling (GPROF) algorithm passive microwave radiometer retrievals of rainfall. The GPROF retrievals are in turn used as high quality precipitation estimates in gridded products such as IMERG. Due to the variability in and high surface emissivity of land surfaces, GPROF performs precipitation retrievals as a function of surface classes. As such, different surface types may possess different error characteristics, especially over arid regions where high quality ground measurements are often lacking. Importantly, the emissive properties of land also result in GPROF rainfall estimates being driven primarily by the higher frequency radiometer channels (e.g., > 89 GHz) where precipitation signals are most sensitive to coupling between the ice-phase and rainfall production. In this study, we evaluate the rainfall estimates from the Ku channel of the DPR as well as GPROF estimates from various passive microwave sensors. Our evaluation is conducted at the level of individual satellite pixels (5 to 15 km in diameter), against a dense network of weighing rain gauges (90 in 150 km2) in the USDA-ARS Walnut Gulch Experimental Watershed and Long-Term Agroecosystem Research (LTAR) site in southeastern Arizona. The multiple gauges in each satellite pixel and precise accumulation about the overpass time allow a spatially and temporally representative comparison between the satellite estimates and ground reference. Over Walnut Gulch, both the Ku and GPROF estimates are challenged to delineate between rain and no-rain. Probabilities of detection are relatively high, but false alarm ratios are also high. The rain intensities possess a negative bias across nearly all sensors. It is likely that storm types, arid conditions and the highly variable precipitation regime present a challenge to both rainfall retrieval algorithms. An array of

Precipitation Estimate Using NEXRAD Ground-Based Radar Images: Validation, Calibration and Spatial Analysis

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

Zhang, Xuesong

2012-12-17

Precipitation is an important input variable for hydrologic and ecological modeling and analysis. Next Generation Radar (NEXRAD) can provide precipitation products that cover most of the continental United States with a high resolution display of approximately 4 × 4 km2. Two major issues concerning the applications of NEXRAD data are (1) lack of a NEXRAD geo-processing and geo-referencing program and (2) bias correction of NEXRAD estimates. In this chapter, a geographic information system (GIS) based software that can automatically support processing of NEXRAD data for hydrologic and ecological models is presented. Some geostatistical approaches to calibrating NEXRAD data using rainmore » gauge data are introduced, and two case studies on evaluating accuracy of NEXRAD Multisensor Precipitation Estimator (MPE) and calibrating MPE with rain-gauge data are presented. The first case study examines the performance of MPE in mountainous region versus south plains and cold season versus warm season, as well as the effect of sub-grid variability and temporal scale on NEXRAD performance. From the results of the first case study, performance of MPE was found to be influenced by complex terrain, frozen precipitation, sub-grid variability, and temporal scale. Overall, the assessment of MPE indicates the importance of removing bias of the MPE precipitation product before its application, especially in the complex mountainous region. The second case study examines the performance of three MPE calibration methods using rain gauge observations in the Little River Experimental Watershed in Georgia. The comparison results show that no one method can perform better than the others in terms of all evaluation coefficients and for all time steps. For practical estimation of precipitation distribution, implementation of multiple methods to predict spatial precipitation is suggested.« less

GPS Estimates of Integrated Precipitable Water Aid Weather Forecasters

NASA Technical Reports Server (NTRS)

Moore, Angelyn W.; Gutman, Seth I.; Holub, Kirk; Bock, Yehuda; Danielson, David; Laber, Jayme; Small, Ivory

2013-01-01

Global Positioning System (GPS) meteorology provides enhanced density, low-latency (30-min resolution), integrated precipitable water (IPW) estimates to NOAA NWS (National Oceanic and Atmospheric Adminis tration Nat ional Weather Service) Weather Forecast Offices (WFOs) to provide improved model and satellite data verification capability and more accurate forecasts of extreme weather such as flooding. An early activity of this project was to increase the number of stations contributing to the NOAA Earth System Research Laboratory (ESRL) GPS meteorology observing network in Southern California by about 27 stations. Following this, the Los Angeles/Oxnard and San Diego WFOs began using the enhanced GPS-based IPW measurements provided by ESRL in the 2012 and 2013 monsoon seasons. Forecasters found GPS IPW to be an effective tool in evaluating model performance, and in monitoring monsoon development between weather model runs for improved flood forecasting. GPS stations are multi-purpose, and routine processing for position solutions also yields estimates of tropospheric zenith delays, which can be converted into mm-accuracy PWV (precipitable water vapor) using in situ pressure and temperature measurements, the basis for GPS meteorology. NOAA ESRL has implemented this concept with a nationwide distribution of more than 300 "GPSMet" stations providing IPW estimates at sub-hourly resolution currently used in operational weather models in the U.S.

Precipitation areal-reduction factor estimation using an annual-maxima centered approach

USGS Publications Warehouse

Asquith, W.H.; Famiglietti, J.S.

2000-01-01

The adjustment of precipitation depth of a point storm to an effective (mean) depth over a watershed is important for characterizing rainfall-runoff relations and for cost-effective designs of hydraulic structures when design storms are considered. A design storm is the precipitation point depth having a specified duration and frequency (recurrence interval). Effective depths are often computed by multiplying point depths by areal-reduction factors (ARF). ARF range from 0 to 1, vary according to storm characteristics, such as recurrence interval; and are a function of watershed characteristics, such as watershed size, shape, and geographic location. This paper presents a new approach for estimating ARF and includes applications for the 1-day design storm in Austin, Dallas, and Houston, Texas. The approach, termed 'annual-maxima centered,' specifically considers the distribution of concurrent precipitation surrounding an annual-precipitation maxima, which is a feature not seen in other approaches. The approach does not require the prior spatial averaging of precipitation, explicit determination of spatial correlation coefficients, nor explicit definition of a representative area of a particular storm in the analysis. The annual-maxima centered approach was designed to exploit the wide availability of dense precipitation gauge data in many regions of the world. The approach produces ARF that decrease more rapidly than those from TP-29. Furthermore, the ARF from the approach decay rapidly with increasing recurrence interval of the annual-precipitation maxima. (C) 2000 Elsevier Science B.V.The adjustment of precipitation depth of a point storm to an effective (mean) depth over a watershed is important for characterizing rainfall-runoff relations and for cost-effective designs of hydraulic structures when design storms are considered. A design storm is the precipitation point depth having a specified duration and frequency (recurrence interval). Effective depths are

The estimation of probable maximum precipitation: the case of Catalonia.

PubMed

Casas, M Carmen; Rodríguez, Raül; Nieto, Raquel; Redaño, Angel

2008-12-01

A brief overview of the different techniques used to estimate the probable maximum precipitation (PMP) is presented. As a particular case, the 1-day PMP over Catalonia has been calculated and mapped with a high spatial resolution. For this purpose, the annual maximum daily rainfall series from 145 pluviometric stations of the Instituto Nacional de Meteorología (Spanish Weather Service) in Catalonia have been analyzed. In order to obtain values of PMP, an enveloping frequency factor curve based on the actual rainfall data of stations in the region has been developed. This enveloping curve has been used to estimate 1-day PMP values of all the 145 stations. Applying the Cressman method, the spatial analysis of these values has been achieved. Monthly precipitation climatological data, obtained from the application of Geographic Information Systems techniques, have been used as the initial field for the analysis. The 1-day PMP at 1 km(2) spatial resolution over Catalonia has been objectively determined, varying from 200 to 550 mm. Structures with wavelength longer than approximately 35 km can be identified and, despite their general concordance, the obtained 1-day PMP spatial distribution shows remarkable differences compared to the annual mean precipitation arrangement over Catalonia.

The assessment of Global Precipitation Measurement estimates over the Indian subcontinent

NASA Astrophysics Data System (ADS)

Murali Krishna, U. V.; Das, Subrata Kumar; Deshpande, Sachin M.; Doiphode, S. L.; Pandithurai, G.

2017-08-01

Accurate and real-time precipitation estimation is a challenging task for current and future spaceborne measurements, which is essential to understand the global hydrological cycle. Recently, the Global Precipitation Measurement (GPM) satellites were launched as a next-generation rainfall mission for observing the global precipitation characteristics. The purpose of the GPM is to enhance the spatiotemporal resolution of global precipitation. The main objective of the present study is to assess the rainfall products from the GPM, especially the Integrated Multi-satellitE Retrievals for the GPM (IMERG) data by comparing with the ground-based observations. The multitemporal scale evaluations of rainfall involving subdaily, diurnal, monthly, and seasonal scales were performed over the Indian subcontinent. The comparison shows that the IMERG performed better than the Tropical Rainfall Measuring Mission (TRMM)-3B42, although both rainfall products underestimated the observed rainfall compared to the ground-based measurements. The analyses also reveal that the TRMM-3B42 and IMERG data sets are able to represent the large-scale monsoon rainfall spatial features but are having region-specific biases. The IMERG shows significant improvement in low rainfall estimates compared to the TRMM-3B42 for selected regions. In the spatial distribution, the IMERG shows higher rain rates compared to the TRMM-3B42, due to its enhanced spatial and temporal resolutions. Apart from this, the characteristics of raindrop size distribution (DSD) obtained from the GPM mission dual-frequency precipitation radar is assessed over the complex mountain terrain site in the Western Ghats, India, using the DSD measured by a Joss-Waldvogel disdrometer.

Validation of TRMM precipitation radar monthly rainfall estimates over Brazil

NASA Astrophysics Data System (ADS)

Franchito, Sergio H.; Rao, V. Brahmananda; Vasques, Ana C.; Santo, Clovis M. E.; Conforte, Jorge C.

2009-01-01

In an attempt to validate the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) over Brazil, TRMM PR estimates are compared with rain gauge station data from Agência Nacional de Energia Elétrica (ANEEL). The analysis is conducted on a seasonal basis and considers five geographic regions with different precipitation regimes. The results showed that TRMM PR seasonal rainfall is well correlated with ANEEL rainfall (correlation coefficients are significant at the 99% confidence level) over most of Brazil. The random and systematic errors of TRMM PR are sensitive to seasonal and regional differences. During December to February and March to May, TRMM PR rainfall is reliable over Brazil. In June to August (September to November) TRMM PR estimates are only reliable in the Amazonian and southern (Amazonian and southeastern) regions. In the other regions the relative RMS errors are larger than 50%, indicating that the random errors are high.

Pareto-optimal estimates that constrain mean California precipitation change

NASA Astrophysics Data System (ADS)

Langenbrunner, B.; Neelin, J. D.

2017-12-01

Global climate model (GCM) projections of greenhouse gas-induced precipitation change can exhibit notable uncertainty at the regional scale, particularly in regions where the mean change is small compared to internal variability. This is especially true for California, which is located in a transition zone between robust precipitation increases to the north and decreases to the south, and where GCMs from the Climate Model Intercomparison Project phase 5 (CMIP5) archive show no consensus on mean change (in either magnitude or sign) across the central and southern parts of the state. With the goal of constraining this uncertainty, we apply a multiobjective approach to a large set of subensembles (subsets of models from the full CMIP5 ensemble). These constraints are based on subensemble performance in three fields important to California precipitation: tropical Pacific sea surface temperatures, upper-level zonal winds in the midlatitude Pacific, and precipitation over the state. An evolutionary algorithm is used to sort through and identify the set of Pareto-optimal subensembles across these three measures in the historical climatology, and we use this information to constrain end-of-century California wet season precipitation change. This technique narrows the range of projections throughout the state and increases confidence in estimates of positive mean change. Furthermore, these methods complement and generalize emergent constraint approaches that aim to restrict uncertainty in end-of-century projections, and they have applications to even broader aspects of uncertainty quantification, including parameter sensitivity and model calibration.

Combination of radar and daily precipitation data to estimate meaningful sub-daily point precipitation extremes

NASA Astrophysics Data System (ADS)

Pegram, Geoff; Bardossy, Andras; Sinclair, Scott

2017-04-01

The use of radar measurements for the space time estimation of precipitation has for many decades been a central topic in hydro-meteorology. In this presentation we are interested specifically in daily and sub-daily extreme values of precipitation at gauged or ungauged locations which are important for design. The purpose of the presentation is to develop a methodology to combine daily precipitation observations and radar measurements to estimate sub-daily extremes at point locations. Radar data corrected using precipitation-reflectivity relationships lead to biased estimations of extremes. Different possibilities of correcting systematic errors using the daily observations are investigated. Observed gauged daily amounts are interpolated to un-sampled points and subsequently disaggregated using the sub-daily values obtained by the radar. Different corrections based on the spatial variability and the sub-daily entropy of scaled rainfall distributions are used to provide unbiased corrections of short duration extremes. In addition, a statistical procedure not based on a matching day by day correction is tested. In this last procedure, as we are only interested in rare extremes, low to medium values of rainfall depth were neglected leaving 12 days of ranked daily maxima in each set per year, whose sum typically comprises about 50% of each annual rainfall total. The sum of these 12 day maxima is first interpolated using a Kriging procedure. Subsequently this sum is disaggregated to daily values using a nearest neighbour procedure. The daily sums are then disaggregated by using the relative values of the biggest 12 radar based days in each year. Of course, the timings of radar and gauge maxima can be different, so the new method presented here uses radar for disaggregating daily gauge totals down to 15 min intervals in order to extract the maxima of sub-hourly through to daily rainfall. The methodologies were tested in South Africa, where an S-band radar operated

Quantitative precipitation forecasts in the Alps - an assessment from the Forecast Demonstration Project MAP D-PHASE

NASA Astrophysics Data System (ADS)

Ament, F.; Weusthoff, T.; Arpagaus, M.; Rotach, M.

2009-04-01

The main aim of the WWRP Forecast Demonstration Project MAP D-PHASE is to demonstrate the performance of today's models to forecast heavy precipitation and flood events in the Alpine region. Therefore an end-to-end, real-time forecasting system was installed and operated during the D PHASE Operations Period from June to November 2007. Part of this system are 30 numerical weather prediction models (deterministic as well as ensemble systems) operated by weather services and research institutes, which issue alerts if predicted precipitation accumulations exceed critical thresholds. Additionally to the real-time alerts, all relevant model fields of these simulations are stored in a central data archive. This comprehensive data set allows a detailed assessment of today's quantitative precipitation forecast (QPF) performance in the Alpine region. We will present results of QPF verifications against Swiss radar and rain gauge data both from a qualitative point of view, in terms of alerts, as well as from a quantitative perspective, in terms of precipitation rate. Various influencing factors like lead time, accumulation time, selection of warning thresholds, or bias corrections will be discussed. Additional to traditional verifications of area average precipitation amounts, the performance of the models to predict the correct precipitation statistics without requiring a point-to-point match will be described by using modern Fuzzy verification techniques. Both analyses reveal significant advantages of deep convection resolving models compared to coarser models with parameterized convection. An intercomparison of the model forecasts themselves reveals a remarkably high variability between different models, and makes it worthwhile to evaluate the potential of a multi-model ensemble. Various multi-model ensemble strategies will be tested by combining D-PHASE models to virtual ensemble systems.

Systematical estimation of GPM-based global satellite mapping of precipitation products over China

NASA Astrophysics Data System (ADS)

Zhao, Haigen; Yang, Bogang; Yang, Shengtian; Huang, Yingchun; Dong, Guotao; Bai, Juan; Wang, Zhiwei

2018-03-01

As the Global Precipitation Measurement (GPM) Core Observatory satellite continues its mission, new version 6 products for Global Satellite Mapping of Precipitation (GSMaP) have been released. However, few studies have systematically evaluated the GSMaP products over mainland China. This study quantitatively evaluated three GPM-based GSMaP version 6 precipitation products for China and eight subregions referring to the Chinese daily Precipitation Analysis Product (CPAP). The GSMaP products included near-real-time (GSMaP_NRT), microwave-infrared reanalyzed (GSMaP_MVK), and gauge-adjusted (GSMaP_Gau) data. Additionally, the gauge-adjusted Integrated Multi-Satellite Retrievals for Global Precipitation Measurement Mission (IMERG_Gau) was also assessed and compared with GSMaP_Gau. The analyses of the selected daily products were carried out at spatiotemporal resolutions of 1/4° for the period of March 2014 to December 2015 in consideration of the resolution of CPAP and the consistency of the coverage periods of the satellite products. The results indicated that GSMaP_MVK and GSMaP_NRT performed comparably and underdetected light rainfall events (< 5 mm/day) in the northwest and northeast of China. All the statistical metrics of GSMaP_MVK were slightly improved compared with GSMaP_NRT in spring, autumn, and winter, whereas GSMaP_NRT demonstrated superior Pearson linear correlation coefficient (CC), fractional standard error (FSE), and root-mean-square error (RMSE) metrics during the summer. Compared with GSMaP_NRT and GSMaP_MVK, GSMaP_Gau possessed significantly improved metrics over mainland China and the eight subregions and performed better in terms of CC, RMSE, and FSE but underestimated precipitation to a greater degree than IMERG_Gau. As a quantitative assessment of the GPM-era GSMaP products, these validation results will supply helpful references for both end users and algorithm developers. However, the study findings need to be confirmed over a longer future

Sampling problems: The small scale structure of precipitation

NASA Technical Reports Server (NTRS)

Crane, R. K.

1981-01-01

The quantitative measurement of precipitation characteristics for any area on the surface of the Earth is not an easy task. Precipitation is rather variable in both space and time, and the distribution of surface rainfall data given location typically is substantially skewed. There are a number of precipitation process at work in the atmosphere, and few of them are well understood. The formal theory on sampling and estimating precipitation appears considerably deficient. Little systematic attention is given to nonsampling errors that always arise in utilizing any measurement system. Although the precipitation measurement problem is an old one, it continues to be one that is in need of systematic and careful attention. A brief history of the presently competing measurement technologies should aid us in understanding the problem inherent in this measurement task.

Antecedent precipitation index determined from CST estimates of rainfall

NASA Technical Reports Server (NTRS)

Martin, David W.

1992-01-01

This paper deals with an experimental calculation of a satellite-based antecedent precipitation index (API). The index is also derived from daily rain images produced from infrared images using an improved version of GSFC's Convective/Stratiform Technique (CST). API is a measure of soil moisture, and is based on the notion that the amount of moisture in the soil at a given time is related to precipitation at earlier times. Four different CST programs as well as the Geostationary Operational Enviroment Satellite (GOES) Precipitation Index developed by Arkin in 1979 are compared to experimental results, for the Mississippi Valley during the month of July. Rain images are shown for the best CST code and the ARK program. Comparisons are made as to the accuracy and detail of the results for the two codes. This project demonstrates the feasibility of running the CST on a synoptic scale. The Mississippi Valley case is well suited for testing the feasibility of monitoring soil moisture by means of CST. Preliminary comparisons of CST and ARK indicate significant differences in estimates of rain amount and distribution.

Estimating Precipitation Susceptibility in Warm Marine Clouds Using Multi-sensor Aerosol and Cloud Products from A-Train Satellites

NASA Astrophysics Data System (ADS)

Bai, H.; Gong, C.; Wang, M.; Zhang, Z.

2017-12-01

Precipitation susceptibility to aerosol perturbation plays a key role in understanding aerosol-cloud interactions and constraining aerosol indirect effects. However, large discrepancies exist in the previous satellite estimates of precipitation susceptibility. In this paper, multi-sensor aerosol and cloud products, including those from CALIPSO, CloudSat, MODIS, and AMSR-E from June 2006 to April 2011 are analyzed to estimate precipitation susceptibility (including precipitation frequency susceptibility SPOP, precipitation intensity susceptibility SI, and precipitation rate susceptibility SR) in warm marine clouds. Our results show that SPOP demonstrates relatively robust features throughout independent LWP products and diverse rain products. In contrast, the behaviors of SI are more subject to LWP or rain products. Our results further show that SPOP strongly depends on atmospherics stability, with larger value under more stable environment. Precipitation susceptibility calculated with respect to cloud droplet number concentration (CDNC) is generally much larger than that estimated with respect to aerosol index (AI), which results from the weak dependency of CDNC on AI.

Regional and seasonal estimates of fractional storm coverage based on station precipitation observations

NASA Technical Reports Server (NTRS)

Gong, Gavin; Entekhabi, Dara; Salvucci, Guido D.

1994-01-01

Simulated climates using numerical atmospheric general circulation models (GCMs) have been shown to be highly sensitive to the fraction of GCM grid area assumed to be wetted during rain events. The model hydrologic cycle and land-surface water and energy balance are influenced by the parameter bar-kappa, which is the dimensionless fractional wetted area for GCM grids. Hourly precipitation records for over 1700 precipitation stations within the contiguous United States are used to obtain observation-based estimates of fractional wetting that exhibit regional and seasonal variations. The spatial parameter bar-kappa is estimated from the temporal raingauge data using conditional probability relations. Monthly bar-kappa values are estimated for rectangular grid areas over the contiguous United States as defined by the Goddard Institute for Space Studies 4 deg x 5 deg GCM. A bias in the estimates is evident due to the unavoidably sparse raingauge network density, which causes some storms to go undetected by the network. This bias is corrected by deriving the probability of a storm escaping detection by the network. A Monte Carlo simulation study is also conducted that consists of synthetically generated storm arrivals over an artificial grid area. It is used to confirm the bar-kappa estimation procedure and to test the nature of the bias and its correction. These monthly fractional wetting estimates, based on the analysis of station precipitation data, provide an observational basis for assigning the influential parameter bar-kappa in GCM land-surface hydrology parameterizations.

Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation

NASA Astrophysics Data System (ADS)

Akbar, Ruzbeh; Short Gianotti, Daniel; McColl, Kaighin A.; Haghighi, Erfan; Salvucci, Guido D.; Entekhabi, Dara

2018-03-01

The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface-only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth Δz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface-only soil moisture observations. To proceed, first an observation-based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry-downs and its successive negative increments. Then, the length scale Δz is obtained via an optimization process wherein the root-mean-squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation-driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge-corrected Climate Prediction Center daily global precipitation product. The length scale Δz exhibits a clear east-west gradient across the contiguous United States (CONUS), such that large Δz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Δz across CONUS is 135 mm. The spatial variance of Δz is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.

Performance of Precipitation Algorithms During IPHEx and Observations of Microphysical Characteristics in Complex Terrain

NASA Astrophysics Data System (ADS)

Erlingis, J. M.; Gourley, J. J.; Kirstetter, P.; Anagnostou, E. N.; Kalogiros, J. A.; Anagnostou, M.

2015-12-01

An Intensive Observation Period (IOP) for the Integrated Precipitation and Hydrology Experiment (IPHEx), part of NASA's Ground Validation campaign for the Global Precipitation Measurement Mission satellite took place from May-June 2014 in the Smoky Mountains of western North Carolina. The National Severe Storms Laboratory's mobile dual-pol X-band radar, NOXP, was deployed in the Pigeon River Basin during this time and employed various scanning strategies, including more than 1000 Range Height Indicator (RHI) scans in coordination with another radar and research aircraft. Rain gauges and disdrometers were also positioned within the basin to verify precipitation estimates and estimation of microphysical parameters. The performance of the SCOP-ME post-processing algorithm on NOXP data is compared with real-time and near real-time precipitation estimates with varying spatial resolutions and quality control measures (Stage IV gauge-corrected radar estimates, Multi-Radar/Multi-Sensor System Quantitative Precipitation Estimates, and CMORPH satellite estimates) to assess the utility of a gap-filling radar in complex terrain. Additionally, the RHI scans collected in this IOP provide a valuable opportunity to examine the evolution of microphysical characteristics of convective and stratiform precipitation as they impinge on terrain. To further the understanding of orographically enhanced precipitation, multiple storms for which RHI data are available are considered.

Improving Satellite-Based Snowfall Estimation: A New Method for Classifying Precipitation Phase and Estimating Snowfall Rate

NASA Astrophysics Data System (ADS)

Sims, Elizabeth M.

In order to study the impact of climate change on the Earth's hydrologic cycle, global information about snowfall is needed. To achieve global measurements of snowfall over both land and ocean, satellites are necessary. While satellites provide the best option for making measurements on a global scale, the task of estimating snowfall rate from these measurements is a complex problem. Satellite-based radar, for example, measures effective radar reflectivity, Ze, which can be converted to snowfall rate, S, via a Ze-S relation. Choosing the appropriate Ze-S relation to apply is a complicated problem, however, because quantities such as particle shape, size distribution, and terminal velocity are often unknown, and these quantities directly affect the Ze-S relation. Additionally, it is important to correctly classify the phase of precipitation. A misclassification can result in order-of-magnitude errors in the estimated precipitation rate. Using global ground-based observations over multiple years, the influence of different geophysical parameters on precipitation phase is investigated, with the goal of obtaining an improved method for determining precipitation phase. The parameters studied are near-surface air temperature, atmospheric moisture, low-level vertical temperature lapse rate, surface skin temperature, surface pressure, and land cover type. To combine the effects of temperature and moisture, wet-bulb temperature, instead of air temperature, is used as a key parameter for separating solid and liquid precipitation. Results show that in addition to wet-bulb temperature, vertical temperature lapse rate also affects the precipitation phase. For example, at a near-surface wet-bulb temperature of 0°C, a lapse rate of 6°C km-1 results in an 86 percent conditional probability of solid precipitation, while a lapse rate of -2°C km-1 results in a 45 percent probability. For near-surface wet-bulb temperatures less than 0°C, skin temperature affects precipitation

Use of Dual Polarization Radar in Validation of Satellite Precipitation Measurements: Rationale and Opportunities

NASA Technical Reports Server (NTRS)

Chandrasekar, V.; Hou, Arthur; Smith, Eric; Bringi, V. N.; Rutledge, S. A.; Gorgucci, E.; Petersen, W. A.; SkofronickJackson, Gail

2008-01-01

Dual-polarization weather radars have evolved significantly in the last three decades culminating in the operational deployment by the National Weather Service. In addition to operational applications in the weather service, dual-polarization radars have shown significant potential in contributing to the research fields of ground based remote sensing of rainfall microphysics, study of precipitation evolution and hydrometeor classification. Furthermore the dual-polarization radars have also raised the awareness of radar system aspects such as calibration. Microphysical characterization of precipitation and quantitative precipitation estimation are important applications that are critical in the validation of satellite borne precipitation measurements and also serves as a valuable tool in algorithm development. This paper presents the important role played by dual-polarization radar in validating space borne precipitation measurements. Starting from a historical evolution, the various configurations of dual-polarization radar are presented. Examples of raindrop size distribution retrievals and hydrometeor type classification are discussed. The quantitative precipitation estimation is a product of direct relevance to space borne observations. During the TRMM program substantial advancement was made with ground based polarization radars specially collecting unique observations in the tropics which are noted. The scientific accomplishments of relevance to space borne measurements of precipitation are summarized. The potential of dual-polarization radars and opportunities in the era of global precipitation measurement mission is also discussed.

Potential Utility of the Real-Time TMPA-RT Precipitation Estimates in Streamflow Prediction

NASA Technical Reports Server (NTRS)

Su, Fengge; Gao, Huilin; Huffman, George J.; Lettenmaier, Dennis P.

2010-01-01

We investigate the potential utility of the real-time Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA-RT) data for streamflow prediction, both through direct comparisons of TMPA-RT estimates with a gridded gauge product, and through evaluation of streamflow simulations over four tributaries of La Plata Basin (LPB) in South America using the two precipitation products. Our assessments indicate that the relative accuracy and the hydrologic performance of TMPA-RT-based streamflow simulations generally improved after February 2005. The improvements in TMPA-RT since 2005 are closely related to upgrades in the TMPA-RT algorithm in early February, 2005 which include use of additional microwave sensors (AMSR-E and AMSU-B) and implementation of different calibration schemes. Our work suggests considerable potential for hydrologic prediction using purely satellite-derived precipitation estimates (no adjustments by in situ gauges) in parts of the globe where in situ observations are sparse.

Precipitation areal-reduction factor estimation using an annual-maxima centered approach

NASA Astrophysics Data System (ADS)

Asquith, W. H.; Famiglietti, J. S.

2000-04-01

The adjustment of precipitation depth of a point storm to an effective (mean) depth over a watershed is important for characterizing rainfall-runoff relations and for cost-effective designs of hydraulic structures when design storms are considered. A design storm is the precipitation point depth having a specified duration and frequency (recurrence interval). Effective depths are often computed by multiplying point depths by areal-reduction factors (ARF). ARF range from 0 to 1, vary according to storm characteristics, such as recurrence interval; and are a function of watershed characteristics, such as watershed size, shape, and geographic location. This paper presents a new approach for estimating ARF and includes applications for the 1-day design storm in Austin, Dallas, and Houston, Texas. The approach, termed "annual-maxima centered," specifically considers the distribution of concurrent precipitation surrounding an annual-precipitation maxima, which is a feature not seen in other approaches. The approach does not require the prior spatial averaging of precipitation, explicit determination of spatial correlation coefficients, nor explicit definition of a representative area of a particular storm in the analysis. The annual-maxima centered approach was designed to exploit the wide availability of dense precipitation gauge data in many regions of the world. The approach produces ARF that decrease more rapidly than those from TP-29. Furthermore, the ARF from the approach decay rapidly with increasing recurrence interval of the annual-precipitation maxima.

Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System

NASA Astrophysics Data System (ADS)

Hong, Yang

Precipitation estimation from satellite information (VISIBLE , IR, or microwave) is becoming increasingly imperative because of its high spatial/temporal resolution and board coverage unparalleled by ground-based data. After decades' efforts of rainfall estimation using IR imagery as basis, it has been explored and concluded that the limitations/uncertainty of the existing techniques are: (1) pixel-based local-scale feature extraction; (2) IR temperature threshold to define rain/no-rain clouds; (3) indirect relationship between rain rate and cloud-top temperature; (4) lumped techniques to model high variability of cloud-precipitation processes; (5) coarse scales of rainfall products. As continuing studies, a new version of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), called Cloud Classification System (CCS), has been developed to cope with these limitations in this dissertation. CCS includes three consecutive components: (1) a hybrid segmentation algorithm, namely Hierarchically Topographical Thresholding and Stepwise Seeded Region Growing (HTH-SSRG), to segment satellite IR images into separated cloud patches; (2) a 3D feature extraction procedure to retrieve both pixel-based local-scale and patch-based large-scale features of cloud patch at various heights; (3) an ANN model, Self-Organizing Nonlinear Output (SONO) network, to classify cloud patches into similarity-based clusters, using Self-Organizing Feature Map (SOFM), and then calibrate hundreds of multi-parameter nonlinear functions to identify the relationship between every cloud types and their underneath precipitation characteristics using Probability Matching Method and Multi-Start Downhill Simplex optimization techniques. The model was calibrated over the Southwest of United States (100°--130°W and 25°--45°N) first and then adaptively adjusted to the study region of North America Monsoon Experiment (65°--135°W and 10°--50°N) using

Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model

NASA Technical Reports Server (NTRS)

Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long

2001-01-01

This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

Long-Term Large-Scale Bias-Adjusted Precipitation Estimates at High Spatial and Temporal Resolution Derived from the National Mosaic and Multi-Sensor QPE (NMQ/Q2) Precipitation Reanalysis over CONUS

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.; Stevens, S. E.; Seo, D. J.; Kim, B.

2014-12-01

The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor Quantitative (NMQ/Q2) based on the WSR-88D Next-generation Radar (Nexrad) network over Continental United States (CONUS) is nearly completed for the period covering from 2000 to 2012. This important milestone constitutes a unique opportunity to study precipitation processes at a 1-km spatial resolution for a 5-min temporal resolution. However, in order to be suitable for hydrological, meteorological and climatological applications, the radar-only product needs to be bias-adjusted and merged with in-situ rain gauge information. Rain gauge networks such as the Hydrometeorological Automated Data System (HADS), the Automated Surface Observing Systems (ASOS), the Climate Reference Network (CRN), and the Global Historical Climatology Network - Daily (GHCN-D) are used to adjust for those biases and to merge with the radar only product to provide a multi-sensor estimate. The challenges related to incorporating non-homogeneous networks over a vast area and for a long-term record are enormous. Among the challenges we are facing are the difficulties incorporating differing resolution and quality surface measurements to adjust gridded estimates of precipitation. Another challenge is the type of adjustment technique. After assessing the bias and applying reduction or elimination techniques, we are investigating the kriging method and its variants such as simple kriging (SK), ordinary kriging (OK), and conditional bias-penalized Kriging (CBPK) among others. In addition we hope to generate estimates of uncertainty for the gridded estimate. In this work the methodology is presented as well as a comparison between the radar-only product and the final multi-sensor QPE product. The comparison is performed at various time scales from the sub-hourly, to annual. In addition, comparisons over the same period with a suite of lower resolution QPEs derived from ground based radar

The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Arkin, Philip; Chang, Alfred; Ferraro, Ralph; Gruber, Arnold; Janowiak, John; McNab, Alan; Rudolf, Bruno; Schneider, Udo

1997-01-01

The Global Precipitation Climatology Project (GPCP) has released the GPCP Version 1 Combined Precipitation Data Set, a global, monthly precipitation dataset covering the period July 1987 through December 1995. The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit -satellite infrared data, and rain gauge observations. The dataset also contains the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. The data are provided on 2.5 deg x 2.5 deg latitude-longitude global grids. Preliminary analyses show general agreement with prior studies of global precipitation and extends prior studies of El Nino-Southern Oscillation precipitation patterns. At the regional scale there are systematic differences with standard climatologies.

Precipitation estimation using L-Band and C-Band soil moisture retrievals

USDA-ARS?s Scientific Manuscript database

An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterome...

Estimation of precipitable water at different locations using surface dew-point

NASA Astrophysics Data System (ADS)

Abdel Wahab, M.; Sharif, T. A.

1995-09-01

The Reitan (1963) regression equation of the form ln w = a + bT d has been examined and tested to estimate precipitable water vapor content from the surface dew point temperature at different locations. The results of this study indicate that the slope b of the above equation has a constant value of 0.0681, while the intercept a changes rapidly with latitude. The use of the variable intercept technique can improve the estimated result by about 2%.

Global Precipitation Measurement (GPM) Core Observatory Falling Snow Estimates

NASA Astrophysics Data System (ADS)

Skofronick Jackson, G.; Kulie, M.; Milani, L.; Munchak, S. J.; Wood, N.; Levizzani, V.

2017-12-01

Retrievals of falling snow from space represent an important data set for understanding and linking the Earth's atmospheric, hydrological, and energy cycles. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. This work focuses on comparing the first stable falling snow retrieval products (released May 2017) for the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO), which was launched February 2014, and carries both an active dual frequency (Ku- and Ka-band) precipitation radar (DPR) and a passive microwave radiometer (GPM Microwave Imager-GMI). Five separate GPM-CO falling snow retrieval algorithm products are analyzed including those from DPR Matched (Ka+Ku) Scan, DPR Normal Scan (Ku), DPR High Sensitivity Scan (Ka), combined DPR+GMI, and GMI. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new, the different on-orbit instruments don't capture all snow rates equally, and retrieval algorithms differ. Thus a detailed comparison among the GPM-CO products elucidates advantages and disadvantages of the retrievals. GPM and CloudSat global snowfall evaluation exercises are natural investigative pathways to explore, but caution must be undertaken when analyzing these datasets for comparative purposes. This work includes outlining the challenges associated with comparing GPM-CO to CloudSat satellite snow estimates due to the different sampling, algorithms, and instrument capabilities. We will highlight some factors and assumptions that can be altered or statistically normalized and applied in an effort to make comparisons between GPM and CloudSat global satellite falling snow products as equitable as possible.

Incorporating TRMM and Other High-Quality Estimates into the One-Degree Daily (1DD) Global Precipitation Product

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.

1999-01-01

The One-Degree Daily (1DD) precipitation dataset was recently developed for the Global Precipitation Climatology Project (GPCP). The IDD provides a globally-complete, observation-only estimate of precipitation on a daily 1 deg x 1 deg grid for the period 1997 through late 1999 (by the time of the conference). In the latitude band 40 N - 40 S the IDD uses the Threshold-Matched Precipitation Index (TMPI), a GPI-like IR product with the T(sub b) threshold and (single) conditional rain rate determined locally for each month by the frequency of precipitation in the GPROF SSNU product and by the precipitation amount in the GPCP satellite-gauge (SG) combination. Outside 40 N - 40 S the 1DD uses a scaled TOVS precipitation estimate that has adjustments based on the TMPI and the SG. This first-generation 1DD has been in beta test preparatory to release as an official GPCP product. In this paper we discuss further development of the 1DD framework to allow the direct incorporation of TRMM and other high-quality precipitation estimates. First, these data are generally sparse (typically from low-orbit satellites), so a fair amount of work was devoted to data boundaries. Second, these data are not the same as the original 1DD estimates, so we had to give careful consideration to the best scheme for forcing the 1DD to sum to the SG for the month. Finally, the non-sun-synchronous, low-inclination orbit occupied by TRMM creates interesting variations against the sun-synchronous, high-inclination orbits occupied by the Defense Meteorological Satellite Program satellites that carry the SSM/I. Examples will be given of each of the development issues, then comparisons will be made to daily raingauge analyses.

Smile line assessment comparing quantitative measurement and visual estimation.

PubMed

Van der Geld, Pieter; Oosterveld, Paul; Schols, Jan; Kuijpers-Jagtman, Anne Marie

2011-02-01

Esthetic analysis of dynamic functions such as spontaneous smiling is feasible by using digital videography and computer measurement for lip line height and tooth display. Because quantitative measurements are time-consuming, digital videography and semiquantitative (visual) estimation according to a standard categorization are more practical for regular diagnostics. Our objective in this study was to compare 2 semiquantitative methods with quantitative measurements for reliability and agreement. The faces of 122 male participants were individually registered by using digital videography. Spontaneous and posed smiles were captured. On the records, maxillary lip line heights and tooth display were digitally measured on each tooth and also visually estimated according to 3-grade and 4-grade scales. Two raters were involved. An error analysis was performed. Reliability was established with kappa statistics. Interexaminer and intraexaminer reliability values were high, with median kappa values from 0.79 to 0.88. Agreement of the 3-grade scale estimation with quantitative measurement showed higher median kappa values (0.76) than the 4-grade scale estimation (0.66). Differentiating high and gummy smile lines (4-grade scale) resulted in greater inaccuracies. The estimation of a high, average, or low smile line for each tooth showed high reliability close to quantitative measurements. Smile line analysis can be performed reliably with a 3-grade scale (visual) semiquantitative estimation. For a more comprehensive diagnosis, additional measuring is proposed, especially in patients with disproportional gingival display. Copyright © 2011 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

Q Conversion Factor Models for Estimating Precipitable Water Vapor for Turkey

NASA Astrophysics Data System (ADS)

Deniz, Ilke; Mekik, Cetin; Gurbuz, Gokhan

2015-04-01

precipitable water vapor is the conversion factor Q which is shown in Emardson and Derks' studies and also Jade and Vijayan's. Developing a regional model using either Tm-Ts equation or the conversion factor Q will provide a basis for GNSS Meteorology in Turkey which depends on the analysis of the radiosonde profile data. For this purpose, the radiosonde profiles from Istanbul, Ankara, Diyarbaki r, Samsun, Erzurum, Izmir, Isparta and Adana stations are analyzed with the radiosonde analysis algorithm in the context of the 'The Estimation of Atmospheric Water Vapour with GPS' Project which is funded by the Scientific and Technological Research Council of Turkey (TUBITAK). The Project is also in the COST Action ES1206: Advanced Global Navigation Satellite Systems tropospheric products for monitoring severe weather events and climate (GNSS4SWEC). In this study, regional models using the conversion factor Q are used for the determination of precipitable water vapor, and applied to the GNSS derived wet tropospheric zenith delays. Henceforth, the estimated precipitable water vapor and the precipitable water vapor obtained from the radiosonde station are compared. The average of the differences between RS and models for Istanbul and Ankara stations are obtained as 2.0±1.6 mm, 1.6±1.6 mm, respectively.

Evaluation of Fuzzy-Logic Framework for Spatial Statistics Preserving Methods for Estimation of Missing Precipitation Data

NASA Astrophysics Data System (ADS)

El Sharif, H.; Teegavarapu, R. S.

2012-12-01

Spatial interpolation methods used for estimation of missing precipitation data at a site seldom check for their ability to preserve site and regional statistics. Such statistics are primarily defined by spatial correlations and other site-to-site statistics in a region. Preservation of site and regional statistics represents a means of assessing the validity of missing precipitation estimates at a site. This study evaluates the efficacy of a fuzzy-logic methodology for infilling missing historical daily precipitation data in preserving site and regional statistics. Rain gauge sites in the state of Kentucky, USA, are used as a case study for evaluation of this newly proposed method in comparison to traditional data infilling techniques. Several error and performance measures will be used to evaluate the methods and trade-offs in accuracy of estimation and preservation of site and regional statistics.

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

Strategies for Near Real Time Estimates of Precipitable Water Vapor from GPS Ground Receivers

NASA Technical Reports Server (NTRS)

Y., Bar-Sever; Runge, T.; Kroger, P.

1995-01-01

GPS-based estimates of precipitable water vapor (PWV) may be useful in numerical weather models to improve short-term weather predictions. To be effective in numerical weather prediction models, GPS PWV estimates must be produced with sufficient accuracy in near real time. Several estimation strategies for the near real time processing of GPS data are investigated.

Evaluating the applicability of four recent satellite–gauge combined precipitation estimates for extreme precipitation and streamflow predictions over the upper Yellow river basin in China

USDA-ARS?s Scientific Manuscript database

This study aimed to statistically and hydrologically assess the performance of four latest and widely used satellite–gauge combined precipitation estimates (SGPEs), namely CRT, BLD, 3B42CDR, and 3B42 for the extreme precipitation and stream'ow scenarios over the upper Yellow river basin (UYRB) in ch...

Precipitation estimates and comparison of satellite rainfall data to in situ rain gauge observations to further develop the watershed-modeling capabilities for the Lower Mekong River Basin

NASA Astrophysics Data System (ADS)

Dandridge, C.; Lakshmi, V.; Sutton, J. R. P.; Bolten, J. D.

2017-12-01

This study focuses on the lower region of the Mekong River Basin (MRB), an area including Burma, Cambodia, Vietnam, Laos, and Thailand. This region is home to expansive agriculture that relies heavily on annual precipitation over the basin for its prosperity. Annual precipitation amounts are regulated by the global monsoon system and therefore vary throughout the year. This research will lead to improved prediction of floods and management of floodwaters for the MRB. We compare different satellite estimates of precipitation to each other and to in-situ precipitation estimates for the Mekong River Basin. These comparisons will help us determine which satellite precipitation estimates are better at predicting precipitation in the MRB and will help further our understanding of watershed-modeling capabilities for the basin. In this study we use: 1) NOAA's PERSIANN daily 0.25° precipitation estimate Climate Data Record (CDR), 2) NASA's Tropical Rainfall Measuring Mission (TRMM) daily 0.25° estimate, and 3) NASA's Global Precipitation Measurement (GPM) daily 0.1 estimate and 4) 488 in-situ stations located in the lower MRB provide daily precipitation estimates. The PERSIANN CDR precipitation estimate was able to provide the longest data record because it is available from 1983 to present. The TRMM precipitation estimate is available from 2000 to present and the GPM precipitation estimates are available from 2015 to present. It is for this reason that we provide several comparisons between our precipitation estimates. Comparisons were done between each satellite product and the in-situ precipitation estimates based on geographical location and date using the entire available data record for each satellite product for daily, monthly, and yearly precipitation estimates. We found that monthly PERSIANN precipitation estimates were able to explain up to 90% of the variability in station precipitation depending on station location.

An appraisal of precipitation distribution in the high-altitude catchments of the Indus basin.

PubMed

Dahri, Zakir Hussain; Ludwig, Fulco; Moors, Eddy; Ahmad, Bashir; Khan, Asif; Kabat, Pavel

2016-04-01

Scarcity of in-situ observations coupled with high orographic influences has prevented a comprehensive assessment of precipitation distribution in the high-altitude catchments of Indus basin. Available data are generally fragmented and scattered with different organizations and mostly cover the valleys. Here, we combine most of the available station data with the indirect precipitation estimates at the accumulation zones of major glaciers to analyse altitudinal dependency of precipitation in the high-altitude Indus basin. The available observations signified the importance of orography in each sub-hydrological basin but could not infer an accurate distribution of precipitation with altitude. We used Kriging with External Drift (KED) interpolation scheme with elevation as a predictor to appraise spatiotemporal distribution of mean monthly, seasonal and annual precipitation for the period of 1998-2012. The KED-based annual precipitation estimates are verified by the corresponding basin-wide observed specific runoffs, which show good agreement. In contrast to earlier studies, our estimates reveal substantially higher precipitation in most of the sub-basins indicating two distinct rainfall maxima; 1st along southern and lower most slopes of Chenab, Jhelum, Indus main and Swat basins, and 2nd around north-west corner of Shyok basin in the central Karakoram. The study demonstrated that the selected gridded precipitation products covering this region are prone to significant errors. In terms of quantitative estimates, ERA-Interim is relatively close to the observations followed by WFDEI and TRMM, while APHRODITE gives highly underestimated precipitation estimates in the study area. Basin-wide seasonal and annual correction factors introduced for each gridded dataset can be useful for lumped hydrological modelling studies, while the estimated precipitation distribution can serve as a basis for bias correction of any gridded precipitation products for the study area

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

Quantitative estimation of pesticide-likeness for agrochemical discovery.

PubMed

Avram, Sorin; Funar-Timofei, Simona; Borota, Ana; Chennamaneni, Sridhar Rao; Manchala, Anil Kumar; Muresan, Sorel

2014-12-01

The design of chemical libraries, an early step in agrochemical discovery programs, is frequently addressed by means of qualitative physicochemical and/or topological rule-based methods. The aim of this study is to develop quantitative estimates of herbicide- (QEH), insecticide- (QEI), fungicide- (QEF), and, finally, pesticide-likeness (QEP). In the assessment of these definitions, we relied on the concept of desirability functions. We found a simple function, shared by the three classes of pesticides, parameterized particularly, for six, easy to compute, independent and interpretable, molecular properties: molecular weight, logP, number of hydrogen bond acceptors, number of hydrogen bond donors, number of rotatable bounds and number of aromatic rings. Subsequently, we describe the scoring of each pesticide class by the corresponding quantitative estimate. In a comparative study, we assessed the performance of the scoring functions using extensive datasets of patented pesticides. The hereby-established quantitative assessment has the ability to rank compounds whether they fail well-established pesticide-likeness rules or not, and offer an efficient way to prioritize (class-specific) pesticides. These findings are valuable for the efficient estimation of pesticide-likeness of vast chemical libraries in the field of agrochemical discovery. Graphical AbstractQuantitative models for pesticide-likeness were derived using the concept of desirability functions parameterized for six, easy to compute, independent and interpretable, molecular properties: molecular weight, logP, number of hydrogen bond acceptors, number of hydrogen bond donors, number of rotatable bounds and number of aromatic rings.

The Mapping Model: A Cognitive Theory of Quantitative Estimation

ERIC Educational Resources Information Center

von Helversen, Bettina; Rieskamp, Jorg

2008-01-01

How do people make quantitative estimations, such as estimating a car's selling price? Traditionally, linear-regression-type models have been used to answer this question. These models assume that people weight and integrate all information available to estimate a criterion. The authors propose an alternative cognitive theory for quantitative…

The impact of reflectivity correction and accounting for raindrop size distribution variability to improve precipitation estimation by weather radar for an extreme low-land mesoscale convective system

NASA Astrophysics Data System (ADS)

Hazenberg, Pieter; Leijnse, Hidde; Uijlenhoet, Remko

2014-11-01

disdrometer information, the best results were obtained in case no differentiation between precipitation type (convective, stratiform and undefined) was made, increasing the event accumulations to more than 80% of those observed by gauges. For the randomly optimized procedure, radar precipitation estimates further improve and closely resemble observations in case one differentiates between precipitation type. However, the optimal parameter sets are very different from those derived from disdrometer observations. It is therefore questionable if single disdrometer observations are suitable for large-scale quantitative precipitation estimation, especially if the disdrometer is located relatively far away from the main rain event, which was the case in this study. In conclusion, this study shows the benefit of applying detailed error correction methods to improve the quality of the weather radar product, but also confirms the need to be cautious using locally obtained disdrometer measurements.

Utilizing the Vertical Variability of Precipitation to Improve Radar QPE

NASA Technical Reports Server (NTRS)

Gatlin, Patrick N.; Petersen, Walter A.

2016-01-01

Characteristics of the melting layer and raindrop size distribution can be exploited to further improve radar quantitative precipitation estimation (QPE). Using dual-polarimetric radar and disdrometers, we found that the characteristic size of raindrops reaching the ground in stratiform precipitation often varies linearly with the depth of the melting layer. As a result, a radar rainfall estimator was formulated using D(sub m) that can be employed by polarimetric as well as dual-frequency radars (e.g., space-based radars such as the GPM DPR), to lower the bias and uncertainty of conventional single radar parameter rainfall estimates by as much as 20%. Polarimetric radar also suffers from issues associated with sampling the vertical distribution of precipitation. Hence, we characterized the vertical profile of polarimetric parameters (VP3)-a radar manifestation of the evolving size and shape of hydrometeors as they fall to the ground-on dual-polarimetric rainfall estimation. The VP3 revealed that the profile of ZDR in stratiform rainfall can bias dual-polarimetric rainfall estimators by as much as 50%, even after correction for the vertical profile of reflectivity (VPR). The VP3 correction technique that we developed can improve operational dual-polarimetric rainfall estimates by 13% beyond that offered by a VPR correction alone.

Next-Generation Satellite Precipitation Products for Understanding Global and Regional Water Variability

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.

2011-01-01

A major challenge in understanding the space-time variability of continental water fluxes is the lack of accurate precipitation estimates over complex terrains. While satellite precipitation observations can be used to complement ground-based data to obtain improved estimates, space-based and ground-based estimates come with their own sets of uncertainties, which must be understood and characterized. Quantitative estimation of uncertainties in these products also provides a necessary foundation for merging satellite and ground-based precipitation measurements within a rigorous statistical framework. Global Precipitation Measurement (GPM) is an international satellite mission that will provide next-generation global precipitation data products for research and applications. It consists of a constellation of microwave sensors provided by NASA, JAXA, CNES, ISRO, EUMETSAT, DOD, NOAA, NPP, and JPSS. At the heart of the mission is the GPM Core Observatory provided by NASA and JAXA to be launched in 2013. The GPM Core, which will carry the first space-borne dual-frequency radar and a state-of-the-art multi-frequency radiometer, is designed to set new reference standards for precipitation measurements from space, which can then be used to unify and refine precipitation retrievals from all constellation sensors. The next-generation constellation-based satellite precipitation estimates will be characterized by intercalibrated radiometric measurements and physical-based retrievals using a common observation-derived hydrometeor database. For pre-launch algorithm development and post-launch product evaluation, NASA supports an extensive ground validation (GV) program in cooperation with domestic and international partners to improve (1) physics of remote-sensing algorithms through a series of focused field campaigns, (2) characterization of uncertainties in satellite and ground-based precipitation products over selected GV testbeds, and (3) modeling of atmospheric processes and

EFFECTS OF IMPROVED PRECIPITATION ESTIMATES ON AUTOMATED RUNOFF MAPPING: EASTERN UNITED STATES

EPA Science Inventory

We evaluated maps of runoff created by means of two automated procedures. We implemented each procedure using precipitation estimates of both 5-km and 10-km resolution from PRISM (Parameter-elevation Regressions on Independent Slopes Model). Our goal was to determine if using the...

Estimating past precipitation and temperature from fossil ostracodes

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

Smith, A.J.; Forester, R.M.

1994-12-31

The fossil records of certain aquatic organisms provide a way of obtaining meaningful estimates of past temperature and precipitation. These estimates of past environmental conditions are derived from multivariate statistical methods that are in turn based on the modern biogeographic distributions and environmental tolerances of the biota of interest. These estimates are helpful in conducting slimate studies as part of the Yucca Mountain site characterization. Ostracodes are microscopic crustaceans that produce bivalved calcite shells which are easily fossilized in the sediments of the lakes and wetlands in which the animals lived. The modern biogeographic distribution and environmental conditions of livingmore » ostracodes are the basis for the interpretation of the past environmental conditions of the fossil ostracodes. The major assumption in this method of interpretation is that the environmental tolerances of ostracodes have not changed substantially over thousands of years. Two methods using these modern analogs to determine past environmental conditions are the modern analog method and the range method. The range method also considers the information provided by fossil ostracode assemblages that have no modern analog in today`s world.« less

Preparations for Global Precipitation Measurement(GPM)Ground Validation

NASA Technical Reports Server (NTRS)

Bidwell, S. W.; Bibyk, I. K.; Duming, J. F.; Everett, D. F.; Smith, E. A.; Wolff, D. B.

2004-01-01

The Global Precipitation Measurement (GPM) program is an international partnership led by the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA). GPM will improve climate, weather, and hydro-meterorological forecasts through more frequent and more accurate measurement of precipitation across the globe. This paper describes the concept and the preparations for Ground Validation within the GPM program. Ground Validation (GV) plays a critical role in the program by investigating and quantitatively assessing the errors within the satellite retrievals. These quantitative estimates of retrieval errors will assist the scientific community by bounding the errors within their research products. The two fundamental requirements of the GPM Ground Validation program are: (1) error characterization of the precipitation retrievals and (2) continual improvement of the satellite retrieval algorithms. These two driving requirements determine the measurements, instrumentation, and location for ground observations. This paper describes GV plans for estimating the systematic and random components of retrieval error and for characterizing the spatial and temporal structure of the error. This paper describes the GPM program for algorithm improvement in which error models are developed and experimentally explored to uncover the physical causes of errors within the retrievals. GPM will ensure that information gained through Ground Validation is applied to future improvements in the spaceborne retrieval algorithms. This paper discusses the potential locations for validation measurement and research, the anticipated contributions of GPM's international partners, and the interaction of Ground Validation with other GPM program elements.

Evaluating the extreme precipitation events using a mesoscale atmopshere model

NASA Astrophysics Data System (ADS)

Yucel, I.; Onen, A.

2012-04-01

Evidence is showing that global warming or climate change has a direct influence on changes in precipitation and the hydrological cycle. Extreme weather events such as heavy rainfall and flooding are projected to become much more frequent as climate warms. Mesoscale atmospheric models coupled with land surface models provide efficient forecasts for meteorological events in high lead time and therefore they should be used for flood forecasting and warning issues as they provide more continuous monitoring of precipitation over large areas. This study examines the performance of the Weather Research and Forecasting (WRF) model in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in West Black Sea Region of Turkey. Extreme precipitation events usually resulted in flood conditions as an associated hydrologic response of the basin. The performance of the WRF system is further investigated by using the three dimensional variational (3D-VAR) data assimilation scheme within WRF. WRF performance with and without data assimilation at high spatial resolution (4 km) is evaluated by making comparison with gauge precipitation and satellite-estimated rainfall data from Multi Precipitation Estimates (MPE). WRF-derived precipitation showed capabilities in capturing the timing of the precipitation extremes and in some extent spatial distribution and magnitude of the heavy rainfall events. These precipitation characteristics are enhanced with the use of 3D-VAR scheme in WRF system. Data assimilation improved area-averaged precipitation forecasts by 9 percent and at some points there exists quantitative match in precipitation events, which are critical for hydrologic forecast application.

Relevance of the correlation between precipitation and the 0 °C isothermal altitude for extreme flood estimation

NASA Astrophysics Data System (ADS)

Zeimetz, Fraenz; Schaefli, Bettina; Artigue, Guillaume; García Hernández, Javier; Schleiss, Anton J.

2017-08-01

Extreme floods are commonly estimated with the help of design storms and hydrological models. In this paper, we propose a new method to take into account the relationship between precipitation intensity (P) and air temperature (T) to account for potential snow accumulation and melt processes during the elaboration of design storms. The proposed method is based on a detailed analysis of this P-T relationship in the Swiss Alps. The region, no upper precipitation intensity limit is detectable for increasing temperature. However, a relationship between the highest measured temperature before a precipitation event and the duration of the subsequent event could be identified. An explanation for this relationship is proposed here based on the temperature gradient measured before the precipitation events. The relevance of these results is discussed for an example of Probable Maximum Precipitation-Probable Maximum Flood (PMP-PMF) estimation for the high mountainous Mattmark dam catchment in the Swiss Alps. The proposed method to associate a critical air temperature to a PMP is easily transposable to similar alpine settings where meteorological soundings as well as ground temperature and precipitation measurements are available. In the future, the analyses presented here might be further refined by distinguishing between precipitation event types (frontal versus orographic).

Validation of Satellite Precipitation (trmm 3B43) in Ecuadorian Coastal Plains, Andean Highlands and Amazonian Rainforest

NASA Astrophysics Data System (ADS)

Ballari, D.; Castro, E.; Campozano, L.

2016-06-01

Precipitation monitoring is of utmost importance for water resource management. However, in regions of complex terrain such as Ecuador, the high spatio-temporal precipitation variability and the scarcity of rain gauges, make difficult to obtain accurate estimations of precipitation. Remotely sensed estimated precipitation, such as the Multi-satellite Precipitation Analysis TRMM, can cope with this problem after a validation process, which must be representative in space and time. In this work we validate monthly estimates from TRMM 3B43 satellite precipitation (0.25° x 0.25° resolution), by using ground data from 14 rain gauges in Ecuador. The stations are located in the 3 most differentiated regions of the country: the Pacific coastal plains, the Andean highlands, and the Amazon rainforest. Time series, between 1998 - 2010, of imagery and rain gauges were compared using statistical error metrics such as bias, root mean square error, and Pearson correlation; and with detection indexes such as probability of detection, equitable threat score, false alarm rate and frequency bias index. The results showed that precipitation seasonality is well represented and TRMM 3B43 acceptably estimates the monthly precipitation in the three regions of the country. According to both, statistical error metrics and detection indexes, the coastal and Amazon regions are better estimated quantitatively than the Andean highlands. Additionally, it was found that there are better estimations for light precipitation rates. The present validation of TRMM 3B43 provides important results to support further studies on calibration and bias correction of precipitation in ungagged watershed basins.

A method to reconstruct long precipitation series using systematic descriptive observations in weather diaries: the example of the precipitation series for Bern, Switzerland (1760-2003)

NASA Astrophysics Data System (ADS)

Gimmi, U.; Luterbacher, J.; Pfister, C.; Wanner, H.

2007-01-01

In contrast to barometric and thermometric records, early instrumental precipitation series are quite rare. Based on systematic descriptive daily records, a quantitative monthly precipitation series for Bern (Switzerland) was reconstructed back to the year 1760 (reconstruction based on documentary evidence). Since every observer had his own personal style to fill out his diary, the main focus was to avoid observer-specific bias in the reconstruction. An independent statistical monthly precipitation reconstruction was performed using instrumental data from European sites. Over most periods the reconstruction based on documentary evidence lies inside the 2 standard errors of the statistical estimates. The comparison between these two approaches enables an independent verification and a reliable error estimate. The analysis points to below normal rainfall totals in all seasons during the late 18th century and in the 1820s and 1830s. Increased precipitation occurred in the early 1850s and the late 1870s, particularly from spring to autumn. The annual precipitation totals generally tend to be higher in the 20th century than in the late 18th and 19th century. Precipitation changes are discussed in the context of socioeconomic impacts and Alpine glacier dynamics. The conceptual design of the reconstruction procedure is aimed at application for similar descriptive precipitation series, which are known to be abundant from the mid-18th century in Europe and the U.S.

Development of a methodology for probable maximum precipitation estimation over the American River watershed using the WRF model

NASA Astrophysics Data System (ADS)

Tan, Elcin

A new physically-based methodology for probable maximum precipitation (PMP) estimation is developed over the American River Watershed (ARW) using the Weather Research and Forecast (WRF-ARW) model. A persistent moisture flux convergence pattern, called Pineapple Express, is analyzed for 42 historical extreme precipitation events, and it is found that Pineapple Express causes extreme precipitation over the basin of interest. An average correlation between moisture flux convergence and maximum precipitation is estimated as 0.71 for 42 events. The performance of the WRF model is verified for precipitation by means of calibration and independent validation of the model. The calibration procedure is performed only for the first ranked flood event 1997 case, whereas the WRF model is validated for 42 historical cases. Three nested model domains are set up with horizontal resolutions of 27 km, 9 km, and 3 km over the basin of interest. As a result of Chi-square goodness-of-fit tests, the hypothesis that "the WRF model can be used in the determination of PMP over the ARW for both areal average and point estimates" is accepted at the 5% level of significance. The sensitivities of model physics options on precipitation are determined using 28 microphysics, atmospheric boundary layer, and cumulus parameterization schemes combinations. It is concluded that the best triplet option is Thompson microphysics, Grell 3D ensemble cumulus, and YSU boundary layer (TGY), based on 42 historical cases, and this TGY triplet is used for all analyses of this research. Four techniques are proposed to evaluate physically possible maximum precipitation using the WRF: 1. Perturbations of atmospheric conditions; 2. Shift in atmospheric conditions; 3. Replacement of atmospheric conditions among historical events; and 4. Thermodynamically possible worst-case scenario creation. Moreover, climate change effect on precipitation is discussed by emphasizing temperature increase in order to determine the

Application of the LEPS technique for Quantitative Precipitation Forecasting (QPF) in Southern Italy: a preliminary study

NASA Astrophysics Data System (ADS)

Federico, S.; Avolio, E.; Bellecci, C.; Colacino, M.; Walko, R. L.

2006-03-01

This paper reports preliminary results for a Limited area model Ensemble Prediction System (LEPS), based on RAMS (Regional Atmospheric Modelling System), for eight case studies of moderate-intense precipitation over Calabria, the southernmost tip of the Italian peninsula. LEPS aims to transfer the benefits of a probabilistic forecast from global to regional scales in countries where local orographic forcing is a key factor to force convection. To accomplish this task and to limit computational time in an operational implementation of LEPS, we perform a cluster analysis of ECMWF-EPS runs. Starting from the 51 members that form the ECMWF-EPS we generate five clusters. For each cluster a representative member is selected and used to provide initial and dynamic boundary conditions to RAMS, whose integrations generate LEPS. RAMS runs have 12-km horizontal resolution. To analyze the impact of enhanced horizontal resolution on quantitative precipitation forecasts, LEPS forecasts are compared to a full Brute Force (BF) ensemble. This ensemble is based on RAMS, has 36 km horizontal resolution and is generated by 51 members, nested in each ECMWF-EPS member. LEPS and BF results are compared subjectively and by objective scores. Subjective analysis is based on precipitation and probability maps of case studies whereas objective analysis is made by deterministic and probabilistic scores. Scores and maps are calculated by comparing ensemble precipitation forecasts against reports from the Calabria regional raingauge network. Results show that LEPS provided better rainfall predictions than BF for all case studies selected. This strongly suggests the importance of the enhanced horizontal resolution, compared to ensemble population, for Calabria for these cases. To further explore the impact of local physiographic features on QPF (Quantitative Precipitation Forecasting), LEPS results are also compared with a 6-km horizontal resolution deterministic forecast. Due to local and

Uncertainty Propagation of Non-Parametric-Derived Precipitation Estimates into Multi-Hydrologic Model Simulations

NASA Astrophysics Data System (ADS)

Bhuiyan, M. A. E.; Nikolopoulos, E. I.; Anagnostou, E. N.

2017-12-01

Quantifying the uncertainty of global precipitation datasets is beneficial when using these precipitation products in hydrological applications, because precipitation uncertainty propagation through hydrologic modeling can significantly affect the accuracy of the simulated hydrologic variables. In this research the Iberian Peninsula has been used as the study area with a study period spanning eleven years (2000-2010). This study evaluates the performance of multiple hydrologic models forced with combined global rainfall estimates derived based on a Quantile Regression Forests (QRF) technique. In QRF technique three satellite precipitation products (CMORPH, PERSIANN, and 3B42 (V7)); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset are being utilized in this study. A high-resolution, ground-based observations driven precipitation dataset (named SAFRAN) available at 5 km/1 h resolution is used as reference. Through the QRF blending framework the stochastic error model produces error-adjusted ensemble precipitation realizations, which are used to force four global hydrological models (JULES (Joint UK Land Environment Simulator), WaterGAP3 (Water-Global Assessment and Prognosis), ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) and SURFEX (Stands for Surface Externalisée) ) to simulate three hydrologic variables (surface runoff, subsurface runoff and evapotranspiration). The models are forced with the reference precipitation to generate reference-based hydrologic simulations. This study presents a comparative analysis of multiple hydrologic model simulations for different hydrologic variables and the impact of the blending algorithm on the simulated hydrologic variables. Results show how precipitation uncertainty propagates through the different hydrologic model structures to manifest in reduction of error in hydrologic variables.

Quantitative characterization and comparison of precipitate and grain shape in Nickel -base superalloys using moment invariants

NASA Astrophysics Data System (ADS)

Callahan, Patrick Gregory

A fundamental objective of materials science and engineering is to understand the structure-property-processing-performance relationship. We need to know the true 3-D microstructure of a material to understand certain geometric properties of a material, and thus fulfill this objective. Focused ion beam (FIB) serial sectioning allows us to find the true 3-D microstructure of Ni-base superalloys. Once the true 3-D microstructure is obtained, an accurate quantitative description and characterization of precipitate and/or grain shapes is needed to understand the microstructure and describe it in an unbiased way. In this thesis, second order moment invariants, the shape quotient Q, a convexity measure relating the volume of an object to the volume of its convex hull, V/Vconv, and Gaussian curvature have been used to compare an experimentally observed polycrystalline IN100 microstructure to three synthetic microstructures. The three synthetic microstructures used different shape classes to produce starting grain shapes. The three shape classes are ellipsoids, superellipsoids, and the shapes generated when truncating a cube with an octahedron. The microstructures are compared using a distance measure, the Hellinger distance. The Hellinger distance is used to compare distributions of shape descriptors for the grains in each microstructure. The synthetic microstructure that has the smallest Hellinger distance, and so best matched the experimentally observed microstructure is the microstructure that used superellipsoids as a starting grain shape. While it has the smallest Hellinger distance, and is approaching realistic grain morphologies, the superellipsoidal microstructure is still not realistic. Second order moment invariants, Q, and V/V conv have also been used to characterize the γ' precipitate shapes from four experimental Ru-containing Ni-base superalloys with differences in alloying additions. The superalloys are designated UM-F9, UM-F18, UM-F19, and UM-F22. The

Quantitative measurement for the microstructural parameters of nano-precipitates in Al-Mg-Si-Cu alloys

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

Li, Kai

Size, number density and volume fraction of nano-precipitates are important microstructural parameters controlling the strengthening of materials. In this work a widely accessible, convenient, moderately time efficient method with acceptable accuracy and precision has been provided for measurement of volume fraction of nano-precipitates in crystalline materials. The method is based on the traditional but highly accurate technique of measuring foil thickness via convergent beam electron diffraction. A new equation is proposed and verified with the aid of 3-dimensional atom probe (3DAP) analysis, to compensate for the additional error resulted from the hardly distinguishable contrast of too short incomplete precipitates cutmore » by the foil surface. The method can be performed on a regular foil specimen with a modern LaB{sub 6} or field-emission-gun transmission electron microscope. Precisions around ± 16% have been obtained for precipitate volume fractions of needle-like β″/C and Q precipitates in an aged Al-Mg-Si-Cu alloy. The measured number density is close to that directly obtained using 3DAP analysis by a misfit of 4.5%, and the estimated precision for number density measurement is about ± 11%. The limitations of the method are also discussed. - Highlights: •A facile method for measuring volume fraction of nano-precipitates based on CBED •An equation to compensate for small invisible precipitates, with 3DAP verification •Precisions around ± 16% for volume fraction and ± 11% for number density.« less

Does GPM-based multi-satellite precipitation enhance rainfall estimates over Pakistan and Bolivia arid regions?

NASA Astrophysics Data System (ADS)

Hussain, Y.; Satgé, F.; Bonnet, M. P.; Pillco, R.; Molina, J.; Timouk, F.; Roig, H.; Martinez-Carvajal, H., Sr.; Gulraiz, A.

2016-12-01

Arid regions are sensitive to rainfall variations which are expressed in the form of flooding and droughts. Unfortunately, those regions are poorly monitored and high quality rainfall estimates are still needed. The Global Precipitation Measurement (GPM) mission released two new satellite rainfall products named Integrated Multisatellite Retrievals GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) bringing the possibility of accurate rainfall monitoring over these countries. This study assessed both products at monthly scale over Pakistan considering dry and wet season over the 4 main climatic zones from 2014 to 2016. With similar climatic conditions, the Altiplano region of Bolivia is considered to quantify the influence of big lakes (Titicaca and Poopó) in rainfall estimates. For comparison, the widely used TRMM-Multisatellite Precipitation Analysis 3B43 (TMPA-3B43) version 7 is also involved in the analysis to observe the potential enhancement in rainfall estimate brought by GPM products. Rainfall estimates derived from 110 rain-gauges are used as reference to compare IMERG, GSMaP-v6 and TMPA-3B43 at the 0.1° and 0.25° spatial resolution. Over both regions, IMERG and GSMaP-v6 capture the spatial pattern of precipitation as well as TMPA-3B43. All products tend to over estimates rainfall over very arid regions. This feature is even more marked during dry season. However, during this season, both reference and estimated rainfall remain very low and do not impact seasonal water budget computation. On a general way, IMERG slightly outperforms TMPA-3B43 and GSMaP-v6 which provides the less accurate rainfall estimate. The TMPA-3B43 rainfall underestimation previously found over Lake Titicaca is still observed in IMERG estimates. However, GSMaP-v6 considerably decreases the underestimation providing the most accurate rainfall estimate over the lake. MOD11C3 Land Surface Temperature (LST) and ASTER Global Emissivity Dataset reveal strong

Impact of air temperature on physically-based maximum precipitation estimation through change in moisture holding capacity of air

NASA Astrophysics Data System (ADS)

Ishida, K.; Ohara, N.; Kavvas, M. L.; Chen, Z. Q.; Anderson, M. L.

2018-01-01

Impact of air temperature on the Maximum Precipitation (MP) estimation through change in moisture holding capacity of air was investigated. A series of previous studies have estimated the MP of 72-h basin-average precipitation over the American River watershed (ARW) in Northern California by means of the Maximum Precipitation (MP) estimation approach, which utilizes a physically-based regional atmospheric model. For the MP estimation, they have selected 61 severe storm events for the ARW, and have maximized them by means of the atmospheric boundary condition shifting (ABCS) and relative humidity maximization (RHM) methods. This study conducted two types of numerical experiments in addition to the MP estimation by the previous studies. First, the air temperature on the entire lateral boundaries of the outer model domain was increased uniformly by 0.0-8.0 °C with 0.5 °C increments for the two severest maximized historical storm events in addition to application of the ABCS + RHM method to investigate the sensitivity of the basin-average precipitation over the ARW to air temperature rise. In this investigation, a monotonous increase was found in the maximum 72-h basin-average precipitation over the ARW with air temperature rise for both of the storm events. The second numerical experiment used specific amounts of air temperature rise that is assumed to happen under future climate change conditions. Air temperature was increased by those specified amounts uniformly on the entire lateral boundaries in addition to application of the ABCS + RHM method to investigate the impact of air temperature on the MP estimate over the ARW under changing climate. The results in the second numerical experiment show that temperature increases in the future climate may amplify the MP estimate over the ARW. The MP estimate may increase by 14.6% in the middle of the 21st century and by 27.3% in the end of the 21st century compared to the historical period.

Precipitation estimation in mountainous terrain using multivariate geostatistics. Part I: structural analysis

USGS Publications Warehouse

Hevesi, Joseph A.; Istok, Jonathan D.; Flint, Alan L.

1992-01-01

Values of average annual precipitation (AAP) are desired for hydrologic studies within a watershed containing Yucca Mountain, Nevada, a potential site for a high-level nuclear-waste repository. Reliable values of AAP are not yet available for most areas within this watershed because of a sparsity of precipitation measurements and the need to obtain measurements over a sufficient length of time. To estimate AAP over the entire watershed, historical precipitation data and station elevations were obtained from a network of 62 stations in southern Nevada and southeastern California. Multivariate geostatistics (cokriging) was selected as an estimation method because of a significant (p = 0.05) correlation of r = .75 between the natural log of AAP and station elevation. A sample direct variogram for the transformed variable, TAAP = ln [(AAP) 1000], was fitted with an isotropic, spherical model defined by a small nugget value of 5000, a range of 190 000 ft, and a sill value equal to the sample variance of 163 151. Elevations for 1531 additional locations were obtained from topographic maps to improve the accuracy of cokriged estimates. A sample direct variogram for elevation was fitted with an isotropic model consisting of a nugget value of 5500 and three nested transition structures: a Gaussian structure with a range of 61 000 ft, a spherical structure with a range of 70 000 ft, and a quasi-stationary, linear structure. The use of an isotropic, stationary model for elevation was considered valid within a sliding-neighborhood radius of 120 000 ft. The problem of fitting a positive-definite, nonlinear model of coregionalization to an inconsistent sample cross variogram for TAAP and elevation was solved by a modified use of the Cauchy-Schwarz inequality. A selected cross-variogram model consisted of two nested structures: a Gaussian structure with a range of 61 000 ft and a spherical structure with a range of 190 000 ft. Cross validation was used for model selection and for

Inter-comparison of Precipitation Estimation Derived from GPM Dual-frequency Radar and CSU-CHILL Radar

NASA Astrophysics Data System (ADS)

Chen, S.; Chen, H.; Hu, J.; Zhang, A.; Min, C.

2017-12-01

It is more than 3 years since the launch of Global Precipitation Measurement (GPM) core satellite on February 27 2014. This satellite carries two core sensors, i.e. dual-frequency precipitation radar (DPR) and microwave imager (GMI). These two sensors are of the state-of- the-art sensors that observe the precipitation over the globe. The DPR level-2 product provides both precipitation rates and phases. The precipitation phase information can help advance global hydrological cycle modeling, particularly crucial for high-altitude and high latitude regions where solid precipitation is the dominated source of water. However, people are still in short of the reliability and accuracy of DPR level-2 product. Assess the performance and uncertainty of precipitation retrievals derived from the core sensor dual-frequency precipitation radar (DPR) on board the satellite is needed for the precipitation algorithm developers and the end users in hydrology, weather, meteorology, and hydro-related communities. In this study, the precipitation estimation derived from DPR is compared with that derived from CSU-CHILL National Weather Radar from March 2014 to October 2017. The CSU-CHILL radar is located in Greeley, CO, and is an advanced, transportable dual-polarized dual-wavelength (S- and X-band) weather radar. The system and random errors of DPR in measuring precipitation will be analyzed as a function of the precipitation rate and precipitation type (liquid and solid). This study is expected to offer insights into performance of the most advanced sensor and thus provide useful feedback to the algorithm developers as well as the GPM data end users.

On sweat analysis for quantitative estimation of dehydration during physical exercise.

PubMed

Ring, Matthias; Lohmueller, Clemens; Rauh, Manfred; Eskofier, Bjoern M

2015-08-01

Quantitative estimation of water loss during physical exercise is of importance because dehydration can impair both muscular strength and aerobic endurance. A physiological indicator for deficit of total body water (TBW) might be the concentration of electrolytes in sweat. It has been shown that concentrations differ after physical exercise depending on whether water loss was replaced by fluid intake or not. However, to the best of our knowledge, this fact has not been examined for its potential to quantitatively estimate TBW loss. Therefore, we conducted a study in which sweat samples were collected continuously during two hours of physical exercise without fluid intake. A statistical analysis of these sweat samples revealed significant correlations between chloride concentration in sweat and TBW loss (r = 0.41, p <; 0.01), and between sweat osmolality and TBW loss (r = 0.43, p <; 0.01). A quantitative estimation of TBW loss resulted in a mean absolute error of 0.49 l per estimation. Although the precision has to be improved for practical applications, the present results suggest that TBW loss estimation could be realizable using sweat samples.

Application of Radar-Rainfall Estimates to Probable Maximum Precipitation in the Carolinas

NASA Astrophysics Data System (ADS)

England, J. F.; Caldwell, R. J.; Sankovich, V.

2011-12-01

Extreme storm rainfall data are essential in the assessment of potential impacts on design precipitation amounts, which are used in flood design criteria for dams and nuclear power plants. Probable Maximum Precipitation (PMP) from National Weather Service Hydrometeorological Report 51 (HMR51) is currently used for design rainfall estimates in the eastern U.S. The extreme storm database associated with the report has not been updated since the early 1970s. In the past several decades, several extreme precipitation events have occurred that have the potential to alter the PMP values, particularly across the Southeast United States (e.g., Hurricane Floyd 1999). Unfortunately, these and other large precipitation-producing storms have not been analyzed with the detail required for application in design studies. This study focuses on warm-season tropical cyclones (TCs) in the Carolinas, as these systems are the critical maximum rainfall mechanisms in the region. The goal is to discern if recent tropical events may have reached or exceeded current PMP values. We have analyzed 10 storms using modern datasets and methodologies that provide enhanced spatial and temporal resolution relative to point measurements used in past studies. Specifically, hourly multisensor precipitation reanalysis (MPR) data are used to estimate storm total precipitation accumulations at various durations throughout each storm event. The accumulated grids serve as input to depth-area-duration calculations. Individual storms are then maximized using back-trajectories to determine source regions for moisture. The development of open source software has made this process time and resource efficient. Based on the current methodology, two of the ten storms analyzed have the potential to challenge HMR51 PMP values. Maximized depth-area curves for Hurricane Floyd indicate exceedance at 24- and 72-hour durations for large area sizes, while Hurricane Fran (1996) appears to exceed PMP at large area sizes for

Comprehensive evaluation of multisatellite precipitation estimates over India using gridded rainfall data

NASA Astrophysics Data System (ADS)

Sunilkumar, K.; Narayana Rao, T.; Saikranthi, K.; Purnachandra Rao, M.

2015-09-01

This study presents a comprehensive evaluation of five widely used multisatellite precipitation estimates (MPEs) against 1° × 1° gridded rain gauge data set as ground truth over India. One decade observations are used to assess the performance of various MPEs (Climate Prediction Center (CPC)-South Asia data set, CPC Morphing Technique (CMORPH), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks, Tropical Rainfall Measuring Mission's Multisatellite Precipitation Analysis (TMPA-3B42), and Global Precipitation Climatology Project). All MPEs have high detection skills of rain with larger probability of detection (POD) and smaller "missing" values. However, the detection sensitivity differs from one product (and also one region) to the other. While the CMORPH has the lowest sensitivity of detecting rain, CPC shows highest sensitivity and often overdetects rain, as evidenced by large POD and false alarm ratio and small missing values. All MPEs show higher rain sensitivity over eastern India than western India. These differential sensitivities are found to alter the biases in rain amount differently. All MPEs show similar spatial patterns of seasonal rain bias and root-mean-square error, but their spatial variability across India is complex and pronounced. The MPEs overestimate the rainfall over the dry regions (northwest and southeast India) and severely underestimate over mountainous regions (west coast and northeast India), whereas the bias is relatively small over the core monsoon zone. Higher occurrence of virga rain due to subcloud evaporation and possible missing of small-scale convective events by gauges over the dry regions are the main reasons for the observed overestimation of rain by MPEs. The decomposed components of total bias show that the major part of overestimation is due to false precipitation. The severe underestimation of rain along the west coast is attributed to the predominant occurrence of shallow rain

Precipitation estimation in mountainous terrain using multivariate geostatistics. Part II: isohyetal maps

USGS Publications Warehouse

Hevesi, Joseph A.; Flint, Alan L.; Istok, Jonathan D.

1992-01-01

Values of average annual precipitation (AAP) may be important for hydrologic characterization of a potential high-level nuclear-waste repository site at Yucca Mountain, Nevada. Reliable measurements of AAP are sparse in the vicinity of Yucca Mountain, and estimates of AAP were needed for an isohyetal mapping over a 2600-square-mile watershed containing Yucca Mountain. Estimates were obtained with a multivariate geostatistical model developed using AAP and elevation data from a network of 42 precipitation stations in southern Nevada and southeastern California. An additional 1531 elevations were obtained to improve estimation accuracy. Isohyets representing estimates obtained using univariate geostatistics (kriging) defined a smooth and continuous surface. Isohyets representing estimates obtained using multivariate geostatistics (cokriging) defined an irregular surface that more accurately represented expected local orographic influences on AAP. Cokriging results included a maximum estimate within the study area of 335 mm at an elevation of 7400 ft, an average estimate of 157 mm for the study area, and an average estimate of 172 mm at eight locations in the vicinity of the potential repository site. Kriging estimates tended to be lower in comparison because the increased AAP expected for remote mountainous topography was not adequately represented by the available sample. Regression results between cokriging estimates and elevation were similar to regression results between measured AAP and elevation. The position of the cokriging 250-mm isohyet relative to the boundaries of pinyon pine and juniper woodlands provided indirect evidence of improved estimation accuracy because the cokriging result agreed well with investigations by others concerning the relationship between elevation, vegetation, and climate in the Great Basin. Calculated estimation variances were also mapped and compared to evaluate improvements in estimation accuracy. Cokriging estimation variances

Analyses of Chinese Hourly Precipitation Using Gauge Observations and Satellite Estimates Products

NASA Astrophysics Data System (ADS)

Pan, Y.; Yu, J.; Shen, Y.

2010-12-01

Highly spatial-temporal and accurate precipitation analyses are essential for monitoring the catastrophic mesoscale weather systems, examining numerical model outputs, and doing dynamic researches on mesoscale meteorology. In recent years, Chinese government has gradually developed a ground-based observational net of 30000 auto-weather-stations (AWS) all over the country, most of which are in the eastern and southern China. The real-time data of gauged rainfall is transported to National Meteorological Information of China (NMIC) every hour, and its quality has been strictly and effectually controlled. Taking advantage of these resources, an hourly Chinese Precipitation Analyses Products (CPAP) with fine resolution is developed. But on the Tibetan Plateau where the AWS is still sparse, the accuracy of precipitation can not satisfy the operational needs yet. Otherwise, CMORPH has a well performance on the space structure of rainfall over China in warm season, but loses on intensity. Thus, we make a merge test analysis at resolution of 0.1 ×0.1 degree , using Optimum Interpolation (OI) to combine hourly CPAP with CMORPH estimates precipitation products. Before OI,the systematic bias in CMORPH have been partly corrected by gauge data through PDF adjustments. The validation of the merge test from June to August 2009 shows that, the combined products can obviously reduce the bias to the gauge analyses CPAP, and also have highly coefficient with it. It is more important that, the combined products provide a reasonable and full-covered precipitation structure over Tibetan Plateau.

Atmospheric water vapor transport: Estimation of continental precipitation recycling and parameterization of a simple climate model. M.S. Thesis

NASA Technical Reports Server (NTRS)

Brubaker, Kaye L.; Entekhabi, Dara; Eagleson, Peter S.

1991-01-01

The advective transport of atmospheric water vapor and its role in global hydrology and the water balance of continental regions are discussed and explored. The data set consists of ten years of global wind and humidity observations interpolated onto a regular grid by objective analysis. Atmospheric water vapor fluxes across the boundaries of selected continental regions are displayed graphically. The water vapor flux data are used to investigate the sources of continental precipitation. The total amount of water that precipitates on large continental regions is supplied by two mechanisms: (1) advection from surrounding areas external to the region; and (2) evaporation and transpiration from the land surface recycling of precipitation over the continental area. The degree to which regional precipitation is supplied by recycled moisture is a potentially significant climate feedback mechanism and land surface-atmosphere interaction, which may contribute to the persistence and intensification of droughts. A simplified model of the atmospheric moisture over continents and simultaneous estimates of regional precipitation are employed to estimate, for several large continental regions, the fraction of precipitation that is locally derived. In a separate, but related, study estimates of ocean to land water vapor transport are used to parameterize an existing simple climate model, containing both land and ocean surfaces, that is intended to mimic the dynamics of continental climates.

Evaluation of two "integrated" polarimetric Quantitative Precipitation Estimation (QPE) algorithms at C-band

NASA Astrophysics Data System (ADS)

Tabary, Pierre; Boumahmoud, Abdel-Amin; Andrieu, Hervé; Thompson, Robert J.; Illingworth, Anthony J.; Le Bouar, Erwan; Testud, Jacques

2011-08-01

SummaryTwo so-called "integrated" polarimetric rate estimation techniques, ZPHI ( Testud et al., 2000) and ZZDR ( Illingworth and Thompson, 2005), are evaluated using 12 episodes of the year 2005 observed by the French C-band operational Trappes radar, located near Paris. The term "integrated" means that the concentration parameter of the drop size distribution is assumed to be constant over some area and the algorithms retrieve it using the polarimetric variables in that area. The evaluation is carried out in ideal conditions (no partial beam blocking, no ground-clutter contamination, no bright band contamination, a posteriori calibration of the radar variables ZH and ZDR) using hourly rain gauges located at distances less than 60 km from the radar. Also included in the comparison, for the sake of benchmarking, is a conventional Z = 282 R1.66 estimator, with and without attenuation correction and with and without adjustment by rain gauges as currently done operationally at Météo France. Under those ideal conditions, the two polarimetric algorithms, which rely solely on radar data, appear to perform as well if not better, pending on the measurements conditions (attenuation, rain rates, …), than the conventional algorithms, even when the latter take into account rain gauges through the adjustment scheme. ZZDR with attenuation correction is the best estimator for hourly rain gauge accumulations lower than 5 mm h -1 and ZPHI is the best one above that threshold. A perturbation analysis has been conducted to assess the sensitivity of the various estimators with respect to biases on ZH and ZDR, taking into account the typical accuracy and stability that can be reasonably achieved with modern operational radars these days (1 dB on ZH and 0.2 dB on ZDR). A +1 dB positive bias on ZH (radar too hot) results in a +14% overestimation of the rain rate with the conventional estimator used in this study (Z = 282R1.66), a -19% underestimation with ZPHI and a +23

Improving the Canadian Precipitation Analysis Estimates through an Observing System Simulation Experiment

NASA Astrophysics Data System (ADS)

Abbasnezhadi, K.; Rasmussen, P. F.; Stadnyk, T.

2014-12-01

To gain a better understanding of the spatiotemporal distribution of rainfall over the Churchill River basin, this study was undertaken. The research incorporates gridded precipitation data from the Canadian Precipitation Analysis (CaPA) system. CaPA has been developed by Environment Canada and provides near real-time precipitation estimates on a 10 km by 10 km grid over North America at a temporal resolution of 6 hours. The spatial fields are generated by combining forecasts from the Global Environmental Multiscale (GEM) model with precipitation observations from the network of synoptic weather stations. CaPA's skill is highly influenced by the number of weather stations in the region of interest as well as by the quality of the observations. In an attempt to evaluate the performance of CaPA as a function of the density of the weather station network, a dual-stage design algorithm to simulate CaPA is proposed which incorporates generated weather fields. More specifically, we are adopting a controlled design algorithm which is generally known as Observing System Simulation Experiment (OSSE). The advantage of using the experiment is that one can define reference precipitation fields assumed to represent the true state of rainfall over the region of interest. In the first stage of the defined OSSE, a coupled stochastic model of precipitation and temperature gridded fields is calibrated and validated. The performance of the generator is then validated by comparing model statistics with observed statistics and by using the generated samples as input to the WATFLOOD™ hydrologic model. In the second stage of the experiment, in order to account for the systematic error of station observations and GEM fields, representative errors are to be added to the reference field using by-products of CaPA's variographic analysis. These by-products explain the variance of station observations and background errors.

Bayesian parameter estimation in spectral quantitative photoacoustic tomography

NASA Astrophysics Data System (ADS)

Pulkkinen, Aki; Cox, Ben T.; Arridge, Simon R.; Kaipio, Jari P.; Tarvainen, Tanja

2016-03-01

Photoacoustic tomography (PAT) is an imaging technique combining strong contrast of optical imaging to high spatial resolution of ultrasound imaging. These strengths are achieved via photoacoustic effect, where a spatial absorption of light pulse is converted into a measurable propagating ultrasound wave. The method is seen as a potential tool for small animal imaging, pre-clinical investigations, study of blood vessels and vasculature, as well as for cancer imaging. The goal in PAT is to form an image of the absorbed optical energy density field via acoustic inverse problem approaches from the measured ultrasound data. Quantitative PAT (QPAT) proceeds from these images and forms quantitative estimates of the optical properties of the target. This optical inverse problem of QPAT is illposed. To alleviate the issue, spectral QPAT (SQPAT) utilizes PAT data formed at multiple optical wavelengths simultaneously with optical parameter models of tissue to form quantitative estimates of the parameters of interest. In this work, the inverse problem of SQPAT is investigated. Light propagation is modelled using the diffusion equation. Optical absorption is described with chromophore concentration weighted sum of known chromophore absorption spectra. Scattering is described by Mie scattering theory with an exponential power law. In the inverse problem, the spatially varying unknown parameters of interest are the chromophore concentrations, the Mie scattering parameters (power law factor and the exponent), and Gruneisen parameter. The inverse problem is approached with a Bayesian method. It is numerically demonstrated, that estimation of all parameters of interest is possible with the approach.

A Parameter Estimation Scheme for Multiscale Kalman Smoother (MKS) Algorithm Used in Precipitation Data Fusion

NASA Technical Reports Server (NTRS)

Wang, Shugong; Liang, Xu

2013-01-01

A new approach is presented in this paper to effectively obtain parameter estimations for the Multiscale Kalman Smoother (MKS) algorithm. This new approach has demonstrated promising potentials in deriving better data products based on data of different spatial scales and precisions. Our new approach employs a multi-objective (MO) parameter estimation scheme (called MO scheme hereafter), rather than using the conventional maximum likelihood scheme (called ML scheme) to estimate the MKS parameters. Unlike the ML scheme, the MO scheme is not simply built on strict statistical assumptions related to prediction errors and observation errors, rather, it directly associates the fused data of multiple scales with multiple objective functions in searching best parameter estimations for MKS through optimization. In the MO scheme, objective functions are defined to facilitate consistency among the fused data at multiscales and the input data at their original scales in terms of spatial patterns and magnitudes. The new approach is evaluated through a Monte Carlo experiment and a series of comparison analyses using synthetic precipitation data. Our results show that the MKS fused precipitation performs better using the MO scheme than that using the ML scheme. Particularly, improvements are significant compared to that using the ML scheme for the fused precipitation associated with fine spatial resolutions. This is mainly due to having more criteria and constraints involved in the MO scheme than those included in the ML scheme. The weakness of the original ML scheme that blindly puts more weights onto the data associated with finer resolutions is overcome in our new approach.

Global Precipitation Measurement (GPM) Ground Validation: Plans and Preparations

NASA Technical Reports Server (NTRS)

Schwaller, M.; Bidwell, S.; Durning, F. J.; Smith, E.

2004-01-01

The Global Precipitation Measurement (GPM) program is an international partnership led by the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA). GPM will improve climate, weather, and hydro-meteorological forecasts through more frequent and more accurate measurement of precipitation across the globe. This paper describes the concept, the planning, and the preparations for Ground Validation within the GPM program. Ground Validation (GV) plays an important role in the program by investigating and quantitatively assessing the errors within the satellite retrievals. These quantitative estimates of retrieval errors will assist the scientific community by bounding the errors within their research products. The two fundamental requirements of the GPM Ground Validation program are: (1) error characterization of the precipitation retrievals and (2) continual improvement of the satellite retrieval algorithms. These two driving requirements determine the measurements, instrumentation, and location for ground observations. This paper outlines GV plans for estimating the systematic and random components of retrieval error and for characterizing the spatial p d temporal structure of the error and plans for algorithm improvement in which error models are developed and experimentally explored to uncover the physical causes of errors within the retrievals. This paper discusses NASA locations for GV measurements as well as anticipated locations from international GPM partners. NASA's primary locations for validation measurements are an oceanic site at Kwajalein Atoll in the Republic of the Marshall Islands and a continental site in north-central Oklahoma at the U.S. Department of Energy's Atmospheric Radiation Measurement Program site.

Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites

NASA Astrophysics Data System (ADS)

Bai, Heming; Gong, Cheng; Wang, Minghuai; Zhang, Zhibo; L'Ecuyer, Tristan

2018-02-01

Precipitation susceptibility to aerosol perturbation plays a key role in understanding aerosol-cloud interactions and constraining aerosol indirect effects. However, large discrepancies exist in the previous satellite estimates of precipitation susceptibility. In this paper, multi-sensor aerosol and cloud products, including those from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), CloudSat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) from June 2006 to April 2011 are analyzed to estimate precipitation frequency susceptibility SPOP, precipitation intensity susceptibility SI, and precipitation rate susceptibility SR in warm marine clouds. We find that SPOP strongly depends on atmospheric stability, with larger values under more stable environments. Our results show that precipitation susceptibility for drizzle (with a -15 dBZ rainfall threshold) is significantly different than that for rain (with a 0 dBZ rainfall threshold). Onset of drizzle is not as readily suppressed in warm clouds as rainfall while precipitation intensity susceptibility is generally smaller for rain than for drizzle. We find that SPOP derived with respect to aerosol index (AI) is about one-third of SPOP derived with respect to cloud droplet number concentration (CDNC). Overall, SPOP demonstrates relatively robust features throughout independent liquid water path (LWP) products and diverse rain products. In contrast, the behaviors of SI and SR are subject to LWP or rain products used to derive them. Recommendations are further made for how to better use these metrics to quantify aerosol-cloud-precipitation interactions in observations and models.

Assessment of extreme quantitative precipitation forecasts and development of regional extreme event thresholds using data from HMT-2006 and COOP observers

USGS Publications Warehouse

Ralph, F.M.; Sukovich, E.; Reynolds, D.; Dettinger, M.; Weagle, S.; Clark, W.; Neiman, P.J.

2010-01-01

Extreme precipitation events, and the quantitative precipitation forecasts (QPFs) associated with them, are examined. The study uses data from the Hydrometeorology Testbed (HMT), which conducted its first field study in California during the 2005/06 cool season. National Weather Service River Forecast Center (NWS RFC) gridded QPFs for 24-h periods at 24-h (day 1), 48-h (day 2), and 72-h (day 3) forecast lead times plus 24-h quantitative precipitation estimates (QPEs) fromsites in California (CA) and Oregon-Washington (OR-WA) are used. During the 172-day period studied, some sites received more than 254 cm (100 in.) of precipitation. The winter season produced many extreme precipitation events, including 90 instances when a site received more than 7.6 cm (3.0 in.) of precipitation in 24 h (i.e., an "event") and 17 events that exceeded 12.7 cm (24 h)-1 [5.0 in. (24 h)-1]. For the 90 extreme events f.7.6 cm (24 h)-1 [3.0 in. (24 h)-1]g, almost 90% of all the 270 QPFs (days 1-3) were biased low, increasingly so with greater lead time. Of the 17 observed events exceeding 12.7 cm (24 h)-1 [5.0 in. (24 h)-1], only 1 of those events was predicted to be that extreme. Almost all of the extreme events correlated with the presence of atmospheric river conditions. Total seasonal QPF biases for all events fi.e., $0.025 cm (24 h)-1 [0.01 in. (24 h)-1]g were sensitive to local geography and were generally biased low in the California-Nevada River Forecast Center (CNRFC) region and high in the Northwest River Forecast Center(NWRFC) domain. The low bias in CA QPFs improved with shorter forecast lead time and worsened for extreme events. Differences were also noted between the CNRFC and NWRFC in terms of QPF and the frequency of extreme events. A key finding from this study is that there were more precipitation events .7.6 cm (24 h)-1 [3.0 in. (24 h)21] in CA than in OR-WA. Examination of 422 Cooperative Observer Program (COOP) sites in the NWRFC domain and 400 in the CNRFC domain

A New Approach for Validating Satellite Estimates of Soil Moisture Using Large-Scale Precipitation: Comparing AMSR-E Products

NASA Astrophysics Data System (ADS)

Tuttle, S. E.; Salvucci, G.

2012-12-01

Soil moisture influences many hydrological processes in the water and energy cycles, such as runoff generation, groundwater recharge, and evapotranspiration, and thus is important for climate modeling, water resources management, agriculture, and civil engineering. Large-scale estimates of soil moisture are produced almost exclusively from remote sensing, while validation of remotely sensed soil moisture has relied heavily on ground truthing, which is at an inherently smaller scale. Here we present a complementary method to determine the information content in different soil moisture products using only large-scale precipitation data (i.e. without modeling). This study builds on the work of Salvucci [2001], Saleem and Salvucci [2002], and Sun et al. [2011], in which precipitation was conditionally averaged according to soil moisture level, resulting in moisture-outflow curves that estimate the dependence of drainage, runoff, and evapotranspiration on soil moisture (i.e. sigmoidal relations that reflect stressed evapotranspiration for dry soils, roughly constant flux equal to potential evaporation minus capillary rise for moderately dry soils, and rapid drainage for very wet soils). We postulate that high quality satellite estimates of soil moisture, using large-scale precipitation data, will yield similar sigmoidal moisture-outflow curves to those that have been observed at field sites, while poor quality estimates will yield flatter, less informative curves that explain less of the precipitation variability. Following this logic, gridded ¼ degree NLDAS precipitation data were compared to three AMSR-E derived soil moisture products (VUA-NASA, or LPRM [Owe et al., 2001], NSIDC [Njoku et al., 2003], and NSIDC-LSP [Jones & Kimball, 2011]) for a period of nine years (2001-2010) across the contiguous United States. Gaps in the daily soil moisture data were filled using a multiple regression model reliant on past and future soil moisture and precipitation, and soil

The Role of Combination Techniques in Maximizing the Utility of Precipitation Estimates from Several Multi-Purpose Remote-Sensing Systems

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.; Curtis, Scott; Einaudi, Franco (Technical Monitor)

2001-01-01

Multi-purpose remote-sensing products from various satellites have proved crucial in developing global estimates of precipitation. Examples of these products include low-earth-orbit and geosynchronous-orbit infrared (leo- and geo-IR), Outgoing Longwave Radiation (OLR), Television Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) data, and passive microwave data such as that from the Special Sensor Microwave/ Imager (SSM/I). Each of these datasets has served as the basis for at least one useful quasi-global precipitation estimation algorithm; however, the quality of estimates varies tremendously among the algorithms for the different climatic regions around the globe.

Strategies for Near Real Time Estimation of Precipitable Water Vapor

NASA Technical Reports Server (NTRS)

Bar-Sever, Yoaz E.

1996-01-01

Traditionally used for high precision geodesy, the GPS system has recently emerged as an equally powerful tool in atmospheric studies, in particular, climatology and meteorology. There are several products of GPS-based systems that are of interest to climatologists and meteorologists. One of the most useful is the GPS-based estimate of the amount of Precipitable Water Vapor (PWV) in the troposphere. Water vapor is an important variable in the study of climate changes and atmospheric convection (Yuan et al., 1993), and is of crucial importance for severe weather forecasting and operational numerical weather prediction (Kuo et al., 1993).

Global Precipitation Measurement. Report 7; Bridging from TRMM to GPM to 3-Hourly Precipitation Estimates

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Smith, Eric A.; Adams, W. James (Editor)

2002-01-01

Historically, multi-decadal measurements of precipitation from surface-based rain gauges have been available over continents. However oceans remained largely unobserved prior to the beginning of the satellite era. Only after the launch of the first Defense Meteorological Satellite Program (DMSP) satellite in 1987 carrying a well-calibrated and multi-frequency passive microwave radiometer called Special Sensor Microwave/Imager (SSM/I) have systematic and accurate precipitation measurements over oceans become available on a regular basis; see Smith et al. (1994, 1998). Recognizing that satellite-based data are a foremost tool for measuring precipitation, NASA initiated a new research program to measure precipitation from space under its Mission to Planet Earth program in the 1990s. As a result, the Tropical Rainfall Measuring Mission (TRMM), a collaborative mission between NASA and NASDA, was launched in 1997 to measure tropical and subtropical rain. See Simpson et al. (1996) and Kummerow et al. (2000). Motivated by the success of TRMM, and recognizing the need for more comprehensive global precipitation measurements, NASA and NASDA have now planned a new mission, i.e., the Global Precipitation Measurement (GPM) mission. The primary goal of GPM is to extend TRMM's rainfall time series while making substantial improvements in precipitation observations, specifically in terms of measurement accuracy, sampling frequency, Earth coverage, and spatial resolution. This report addresses four fundamental questions related to the transition from current to future global precipitation observations as denoted by the TRMM and GPM eras, respectively.

Evaluation of precipitation nowcasting techniques for the Alpine region

NASA Astrophysics Data System (ADS)

Panziera, L.; Mandapaka, P.; Atencia, A.; Hering, A.; Germann, U.; Gabella, M.; Buzzi, M.

2010-09-01

This study presents a large sample evaluation of different nowcasting systems over the Southern Swiss Alps. Radar observations are taken as a reference against which to assess the performance of the following short-term quantitative precipitation forecasting methods: -Eulerian persistence: the current radar image is taken as forecast. -Lagrangian persistence: precipitation patterns are advected following the field of storm motion (the MAPLE algorithm is used). -NORA: novel nowcasting system which exploits the presence of the orographic forcing; by comparing meteorological predictors estimated in real-time with those from the large historical data set, the events with the highest resemblance are picked to produce the forecast. -COSMO2, the limited area numerical model operationally used at MeteoSwiss -Blending of the aforementioned nowcasting tools precipitation forecasts. The investigation is aimed to set up a probabilistic radar rainfall runoff model experiment for steep Alpine catchments as part of the European research project IMPRINTS.

The Potential for Predicting Precipitation on Seasonal-to-Interannual Timescales

NASA Technical Reports Server (NTRS)

Koster, R. D.

1999-01-01

The ability to predict precipitation several months in advance would have a significant impact on water resource management. This talk provides an overview of a project aimed at developing this prediction capability. NASA's Seasonal-to-Interannual Prediction Project (NSIPP) will generate seasonal-to-interannual sea surface temperature predictions through detailed ocean circulation modeling and will then translate these SST forecasts into forecasts of continental precipitation through the application of an atmospheric general circulation model and a "SVAT"-type land surface model. As part of the process, ocean variables (e.g., height) and land variables (e.g., soil moisture) will be updated regularly via data assimilation. The overview will include a discussion of the variability inherent in such a modeling system and will provide some quantitative estimates of the absolute upper limits of seasonal-to-interannual precipitation predictability.

Model Parameter Estimation Using Ensemble Data Assimilation: A Case with the Nonhydrostatic Icosahedral Atmospheric Model NICAM and the Global Satellite Mapping of Precipitation Data

NASA Astrophysics Data System (ADS)

Kotsuki, Shunji; Terasaki, Koji; Yashiro, Hasashi; Tomita, Hirofumi; Satoh, Masaki; Miyoshi, Takemasa

2017-04-01

This study aims to improve precipitation forecasts from numerical weather prediction (NWP) models through effective use of satellite-derived precipitation data. Kotsuki et al. (2016, JGR-A) successfully improved the precipitation forecasts by assimilating the Japan Aerospace eXploration Agency (JAXA)'s Global Satellite Mapping of Precipitation (GSMaP) data into the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) at 112-km horizontal resolution. Kotsuki et al. mitigated the non-Gaussianity of the precipitation variables by the Gaussian transform method for observed and forecasted precipitation using the previous 30-day precipitation data. This study extends the previous study by Kotsuki et al. and explores an online estimation of model parameters using ensemble data assimilation. We choose two globally-uniform parameters, one is the cloud-to-rain auto-conversion parameter of the Berry's scheme for large scale condensation and the other is the relative humidity threshold of the Arakawa-Schubert cumulus parameterization scheme. We perform the online-estimation of the two model parameters with an ensemble transform Kalman filter by assimilating the GSMaP precipitation data. The estimated parameters improve the analyzed and forecasted mixing ratio in the lower troposphere. Therefore, the parameter estimation would be a useful technique to improve the NWP models and their forecasts. This presentation will include the most recent progress up to the time of the symposium.

Steps toward a CONUS-wide reanalysis with archived NEXRAD data using National Mosaic and Multisensor Quantitative Precipitation Estimation (NMQ/Q2) algorithms

NASA Astrophysics Data System (ADS)

Stevens, S. E.; Nelson, B. R.; Langston, C.; Qi, Y.

2012-12-01

The National Mosaic and Multisensor QPE (NMQ/Q2) software suite, developed at NOAA's National Severe Storms Laboratory (NSSL) in Norman, OK, addresses a large deficiency in the resolution of currently archived precipitation datasets. Current standards, both radar- and satellite-based, provide for nationwide precipitation data with a spatial resolution of up to 4-5 km, with a temporal resolution as fine as one hour. Efforts are ongoing to process archived NEXRAD data for the period of record (1996 - present), producing a continuous dataset providing precipitation data at a spatial resolution of 1 km, on a timescale of only five minutes. In addition, radar-derived precipitation data are adjusted hourly using a wide variety of automated gauge networks spanning the United States. Applications for such a product range widely, from emergency management and flash flood guidance, to hydrological studies and drought monitoring. Results are presented from a subset of the NEXRAD dataset, providing basic statistics on the distribution of rainrates, relative frequency of precipitation types, and several other variables which demonstrate the variety of output provided by the software. Precipitation data from select case studies are also presented to highlight the increased resolution provided by this reanalysis and the possibilities that arise from the availability of data on such fine scales. A previously completed pilot project and steps toward a nationwide implementation are presented along with proposed strategies for managing and processing such a large dataset. Reprocessing efforts span several institutions in both North Carolina and Oklahoma, and data/software coordination are key in producing a homogeneous record of precipitation to be archived alongside NOAA's other Climate Data Records. Methods are presented for utilizing supercomputing capability in expediting processing, to allow for the iterative nature of a reanalysis effort.

Contrasts Between Precipitation over Mediterranean Sea and Adjacent Continental Areas Based on Decadal Scale Satellite Estimates

NASA Technical Reports Server (NTRS)

Smith, Eric A.

2007-01-01

Most knowledge concerning the last century's climatology and climate dynamics of precipitation over the Mediterranean Sea basin is based on observations taken from rain gauges surrounding the sea itself. In turn, most of the observations come from Southern Europe, with many fewer measurements taken from widely scattered sites situated over North Africa, the Middle East, and the Balkans. This aspect of research on the Mediterranean Sea basin is apparent in a recent compilation of studies presented in book form concerning climate variability of the Mediterranean region [Lionello, P., P. Malanotte-Rizzoli, and R. Boscolo (eds.), 2006: Mediterranean Climate Variability. Elsevier, Amsterdam, 9 chapters.] In light of this missing link to over-water observations, this study (in conjunction with four companion studies by Z. Haddad, A. Mugnai, T. Nakazawa, and G. Stephens) will contrast the nature of precipitation variability directly over the Mediterranean Sea to precipitation variability over the surrounding land areas based on three decades of satellite-based precipitation estimates which have stood up well to validation scrutiny. The satellite observations are drawn from the Global Precipitation Climatology Project (GPCP) dataset extending back to 1979 and the TRMM Merged Algorithm 3b42 dataset extending back to 1998. Both datasets are mostly produced from microwave measurements, excepting the period from 1979 to mid-1987 when only infrared satellite measurements were available for the GPCP estimates. The purpose of this study is to emphasize how the salient properties of precipitation variability over land and sea across a hierarchy of space and time scales, and the salient differences in these properties, might be used in guiding short-term climate models to better predictions of future climate states under different regional temperature-change scenarios.

a Climatology of Global Precipitation.

NASA Astrophysics Data System (ADS)

Legates, David Russell

A global climatology of mean monthly precipitation has been developed using traditional land-based gage measurements as well as derived oceanic data. These data have been screened for coding errors and redundant entries have been removed. Oceanic precipitation estimates are most often extrapolated from coastal and island observations because few gage estimates of oceanic precipitation exist. One such procedure, developed by Dorman and Bourke and used here, employs a derived relationship between observed rainfall totals and the "current weather" at coastal stations. The combined data base contains 24,635 independent terrestial station records and 2223 oceanic grid-point records. Raingage catches are known to underestimate actual precipitation. Errors in the gage catch result from wind -field deformation, wetting losses, and evaporation from the gage and can amount to nearly 8, 2, and 1 percent of the global catch, respectively. A procedure has been developed to correct many of these errors and has been used to adjust the gage estimates of global precipitation. Space-time variations in gage type, air temperature, wind speed, and natural vegetation were incorporated into the correction procedure. Corrected data were then interpolated to the nodes of a 0.5^circ of latitude by 0.5^circ of longitude lattice using a spherically-based interpolation algorithm. Interpolation errors are largest in areas of low station density, rugged topography, and heavy precipitation. Interpolated estimates also were compared with a digital filtering technique to access the aliasing of high-frequency "noise" into the lower frequency signals. Isohyetal maps displaying the mean annual, seasonal, and monthly precipitation are presented. Gage corrections and the standard error of the corrected estimates also are mapped. Results indicate that mean annual global precipitation is 1123 mm with 1251 mm falling over the oceans and 820 mm over land. Spatial distributions of monthly precipitation

A precipitation organization climatology for North Carolina: Development and GIS-based analysis

NASA Astrophysics Data System (ADS)

Zarzar, Christopher M.

A climatology of precipitation organization is developed for the Southeast United States and is analyzed in a GIS framework. This climatology is created using four years (2009-2012) of daily-averaged data from the NOAA high-resolution multi-sensor precipitation estimation (MPE) dataset, specifically the radar-based quantitative precipitation estimation (QPE) product and the mosaic reflectivity. The analysis associates precipitation at each pixel with the spatial scale of precipitation organization, either a mesoscale precipitation feature (MPF) or isolated storm. While the long-term averaged precipitation totals of these systems may be similar, their hydrological and climatological impacts are very different, especially at a local scale. The classification of these modes of precipitation organization in the current precipitation climatology provides information beyond standard precipitation climatologies that will benefit a range of hydrological and climatological applications. This study focuses on North Carolina and takes advantage of a GIS framework to examine hydrological responses to different modes of precipitation organization. Specifically, the following questions are addressed: First, what are the discharge response characteristics to precipitation events in different watersheds across the state, from the mountains to the coastal plain? Second, what are the different impacts on watershed discharge between MPF precipitation and isolated precipitation? We first present seasonal and annual composites of precipitation and duration of MPF and isolated storms across three regions of North Carolina: the western mountains, the central Piedmont, and the eastern coastal plain. Further analysis in a GIS framework provides information about the impacts this seasonal and geographic variability in precipitation has on watershed discharge. This analysis defines five watersheds in North Carolina based on five North Carolina river basins using ArcGIS watershed delineation

Monitoring Precipitation from Space: targeting Hydrology Community?

NASA Astrophysics Data System (ADS)

Hong, Y.; Turk, J.

2005-12-01

During the past decades, advances in space, sensor and computer technology have made it possible to estimate precipitation nearly globally from a variety of observations in a relatively direct manner. The success of Tropical Precipitation Measuring Mission (TRMM) has been a significant advance for modern precipitation estimation algorithms to move toward daily quarter degree measurements, while the need for precipitation data at temporal-spatial resolutions compatible with hydrologic modeling has been emphasized by the end user: hydrology community. Can the future deployment of Global Precipitation Measurement constellation of low-altitude orbiting satellites (covering 90% of the global with a sampling interval of less than 3-hours), in conjunction with the existing suite of geostationary satellites, results in significant improvements in scale and accuracy of precipitation estimates suitable for hydrology applications? This presentation will review the current state of satellite-derived precipitation estimation and demonstrate the early results and primary barriers to full global high-resolution precipitation coverage. An attempt to facilitate the communication between data producers and users will be discussed by developing an 'end-to-end' uncertainty propagation analysis framework to quantify both the precipitation estimation error structure and the error influence on hydrological modeling.

The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present)

NASA Technical Reports Server (NTRS)

Adler, Robert F.; Huffman, George J.; Chang, Alfred; Ferraro, Ralph; Xie, Ping-Ping; Janowiak, John; Rudolf, Bruno; Schneider, Udo; Curtis, Scott; Bolvin, David

2003-01-01

The Global Precipitation Climatology Project (GPCP) Version 2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5 degrees x 2.5 degrees latitude-longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The data set is extended back into the premicrowave era (before 1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the raingauge analysis. This monthly analysis is the foundation for the GPCP suite of products including those at finer temporal resolution, satellite estimate, and error estimates for each field. The 23-year GPCP climatology is characterized, along with time and space variations of precipitation.

Ranking GCM Estimates of Twentieth Century Precipitation Seasonality in the Western U.S. and its Influence on Floristic Provinces.

NASA Astrophysics Data System (ADS)

Cole, K. L.; Eischeid, J. K.; Garfin, G. M.; Ironside, K.; Cobb, N. S.

2008-12-01

Floristic provinces of the western United States (west of 100W) can be segregated into three regions defined by significant seasonal precipitation during the months of: 1) November-March (Mediterranean); 2) July- September (Monsoonal); or, 3) May-June (Rocky Mountain). This third region is best defined by the absence of the late spring-early summer drought that affects regions 1 and 2. Each of these precipitation regimes is characterized by distinct vegetation types and fire seasonality adapted to that particular cycle of seasonal moisture availability and deficit. Further, areas where these regions blend from one to another can support even more complex seasonal patterns and resulting distinctive vegetation types. As a result, modeling the effects of climates on these ecosystems requires confidence that GCMs can at least approximate these sub- continental seasonal precipitation patterns. We evaluated the late Twentieth Century (1950-1999 AD) estimates of annual precipitation seasonality produced by 22 GCMs contained within the IPCC Fourth Assessment (AR4). These modeled estimates were compared to values from the PRISM dataset, extrapolated from station data, over the same historical period for the 3 seasonal periods defined above. The correlations between GCM estimates and PRISM values were ranked using 4 measures: 1) A map pattern relationship based on the correlation coefficient, 2) A map pattern relationship based on the congruence coefficient, 3) The ratio of simulated/observed area averaged precipitation based on the seasonal precipitation amounts, and, 4) The ratio of simulated/observed area averaged precipitation based on the seasonal precipitation percentages of the annual total. For each of the four metrics, the rank order of models was very similar. The ranked order of the performance of the different models quantified aspects of the model performance visible in the mapped results. While some models represented the seasonal patterns very well, others

Contribution of long-term accounting for raindrop size distribution variations on quantitative precipitation estimation by weather radar: Disdrometers vs parameter optimization

NASA Astrophysics Data System (ADS)

Hazenberg, P.; Uijlenhoet, R.; Leijnse, H.

2015-12-01

Volumetric weather radars provide information on the characteristics of precipitation at high spatial and temporal resolution. Unfortunately, rainfall measurements by radar are affected by multiple error sources, which can be subdivided into two main groups: 1) errors affecting the volumetric reflectivity measurements (e.g. ground clutter, vertical profile of reflectivity, attenuation, etc.), and 2) errors related to the conversion of the observed reflectivity (Z) values into rainfall intensity (R) and specific attenuation (k). Until the recent wide-scale implementation of dual-polarimetric radar, this second group of errors received relatively little attention, focusing predominantly on precipitation type-dependent Z-R and Z-k relations. The current work accounts for the impact of variations of the drop size distribution (DSD) on the radar QPE performance. We propose to link the parameters of the Z-R and Z-k relations directly to those of the normalized gamma DSD. The benefit of this procedure is that it reduces the number of unknown parameters. In this work, the DSD parameters are obtained using 1) surface observations from a Parsivel and Thies LPM disdrometer, and 2) a Monte Carlo optimization procedure using surface rain gauge observations. The impact of both approaches for a given precipitation type is assessed for 45 days of summertime precipitation observed within The Netherlands. Accounting for DSD variations using disdrometer observations leads to an improved radar QPE product as compared to applying climatological Z-R and Z-k relations. However, overall precipitation intensities are still underestimated. This underestimation is expected to result from unaccounted errors (e.g. transmitter calibration, erroneous identification of precipitation as clutter, overshooting and small-scale variability). In case the DSD parameters are optimized, the performance of the radar is further improved, resulting in the best performance of the radar QPE product. However

Component Analysis of Errors on PERSIANN Precipitation Estimates over Urmia Lake Basin, IRAN

NASA Astrophysics Data System (ADS)

Ghajarnia, N.; Daneshkar Arasteh, P.; Liaghat, A. M.; Araghinejad, S.

2016-12-01

In this study, PERSIANN daily dataset is evaluated from 2000 to 2011 in 69 pixels over Urmia Lake basin in northwest of Iran. Different analytical approaches and indexes are used to examine PERSIANN precision in detection and estimation of rainfall rate. The residuals are decomposed into Hit, Miss and FA estimation biases while continues decomposition of systematic and random error components are also analyzed seasonally and categorically. New interpretation of estimation accuracy named "reliability on PERSIANN estimations" is introduced while the changing manners of existing categorical/statistical measures and error components are also seasonally analyzed over different rainfall rate categories. This study yields new insights into the nature of PERSIANN errors over Urmia lake basin as a semi-arid region in the middle-east, including the followings: - The analyzed contingency table indexes indicate better detection precision during spring and fall. - A relatively constant level of error is generally observed among different categories. The range of precipitation estimates at different rainfall rate categories is nearly invariant as a sign for the existence of systematic error. - Low level of reliability is observed on PERSIANN estimations at different categories which are mostly associated with high level of FA error. However, it is observed that as the rate of precipitation increase, the ability and precision of PERSIANN in rainfall detection also increases. - The systematic and random error decomposition in this area shows that PERSIANN has more difficulty in modeling the system and pattern of rainfall rather than to have bias due to rainfall uncertainties. The level of systematic error also considerably increases in heavier rainfalls. It is also important to note that PERSIANN error characteristics at each season varies due to the condition and rainfall patterns of that season which shows the necessity of seasonally different approach for the calibration of

Data Analysis of GPM Constellation Satellites-IMERG and ERA-Interim precipitation products over West of Iran

NASA Astrophysics Data System (ADS)

Sharifi, Ehsan; Steinacker, Reinhold; Saghafian, Bahram

2016-04-01

Precipitation is a critical component of the Earth's hydrological cycle. The primary requirement in precipitation measurement is to know where and how much precipitation is falling at any given time. Especially in data sparse regions with insufficient radar coverage, satellite information can provide a spatial and temporal context. Nonetheless, evaluation of satellite precipitation is essential prior to operational use. This is why many previous studies are devoted to the validation of satellite estimation. Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards. In situ observations over mountainous areas are mostly limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for meteorological and hydrological applications. One of the newest and blended methods that use multi-satellites and multi-sensors has been developed for estimating global precipitation. The considered data set known as Integrated Multi-satellitE Retrievals (IMERG) for GPM (Global Precipitation Measurement) is routinely produced by the GPM constellation satellites. Moreover, recent efforts have been put into the improvement of the precipitation products derived from reanalysis systems, which has led to significant progress. One of the best and a worldwide used model is developed by the European Centre for Medium Range Weather Forecasts (ECMWF). They have produced global reanalysis daily precipitation, known as ERA-Interim. This study has evaluated one year of precipitation data from the GPM-IMERG and ERA-Interim reanalysis daily time series over West of Iran. IMERG and ERA-Interim yield underestimate the observed values while IMERG underestimated slightly and performed better when precipitation is greater than 10mm. Furthermore, with respect to evaluation of probability of detection (POD), threat score (TS), false alarm ratio (FAR) and probability

A data centred method to estimate and map how the local distribution of daily precipitation is changing

NASA Astrophysics Data System (ADS)

Chapman, Sandra; Stainforth, David; Watkins, Nick

2014-05-01

Estimates of how our climate is changing are needed locally in order to inform adaptation planning decisions. This requires quantifying the geographical patterns in changes at specific quantiles in distributions of variables such as daily temperature or precipitation. Here we focus on these local changes and on a method to transform daily observations of precipitation into patterns of local climate change. We develop a method[1] for analysing local climatic timeseries to assess which quantiles of the local climatic distribution show the greatest and most robust changes, to specifically address the challenges presented by daily precipitation data. We extract from the data quantities that characterize the changes in time of the likelihood of daily precipitation above a threshold and of the relative amount of precipitation in those days. Our method is a simple mathematical deconstruction of how the difference between two observations from two different time periods can be assigned to the combination of natural statistical variability and/or the consequences of secular climate change. This deconstruction facilitates an assessment of how fast different quantiles of precipitation distributions are changing. This involves both determining which quantiles and geographical locations show the greatest change but also, those at which any change is highly uncertain. We demonstrate this approach using E-OBS gridded data[2] timeseries of local daily precipitation from specific locations across Europe over the last 60 years. We treat geographical location and precipitation as independent variables and thus obtain as outputs the pattern of change at a given threshold of precipitation and with geographical location. This is model- independent, thus providing data of direct value in model calibration and assessment. Our results show regionally consistent patterns of systematic increase in precipitation on the wettest days, and of drying across all days which is of potential value in

A Texture-Polarization Method for Estimating Convective/Stratiform Precipitation Area Coverage from Passive Microwave Radiometer Data

NASA Technical Reports Server (NTRS)

Olson, William S.; Hong, Ye; Kummerow, Christian D.; Turk, Joseph; Einaudi, Franco (Technical Monitor)

2000-01-01

Observational and modeling studies have described the relationships between convective/stratiform rain proportion and the vertical distributions of vertical motion, latent heating, and moistening in mesoscale convective systems. Therefore, remote sensing techniques which can quantify the relative areal proportion of convective and stratiform, rainfall can provide useful information regarding the dynamic and thermodynamic processes in these systems. In the present study, two methods for deducing the convective/stratiform areal extent of precipitation from satellite passive microwave radiometer measurements are combined to yield an improved method. If sufficient microwave scattering by ice-phase precipitating hydrometeors is detected, the method relies mainly on the degree of polarization in oblique-view, 85.5 GHz radiances to estimate the area fraction of convective rain within the radiometer footprint. In situations where ice scattering is minimal, the method draws mostly on texture information in radiometer imagery at lower microwave frequencies to estimate the convective area fraction. Based upon observations of ten convective systems over ocean and nine systems over land, instantaneous 0.5 degree resolution estimates of convective area fraction from the Tropical Rainfall Measuring Mission Microwave Imager (TRMM TMI) are compared to nearly coincident estimates from the TRMM Precipitation Radar (TRMM PR). The TMI convective area fraction estimates are slightly low-biased with respect to the PR, with TMI-PR correlations of 0.78 and 0.84 over ocean and land backgrounds, respectively. TMI monthly-average convective area percentages in the tropics and subtropics from February 1998 exhibit the greatest values along the ITCZ and in continental regions of the summer (southern) hemisphere. Although convective area percentages. from the TMI are systematically lower than those from the PR, monthly rain patterns derived from the TMI and PR rain algorithms are very similar

Demonstrating Improvements from a NWP-based Satellite Precipitation Adjustment Technique in Tropical Mountainous Regions

NASA Astrophysics Data System (ADS)

Zhang, X.; Anagnostou, E. N.

2016-12-01

This research contributes to the improvement of high resolution satellite applications in tropical regions with mountainous topography. Such mountainous regions are usually covered by sparse networks of in-situ observations while quantitative precipitation estimation from satellite sensors exhibits strong underestimation of heavy orographically enhanced storm events. To address this issue, our research applies a satellite error correction technique based solely on high-resolution numerical weather predictions (NWP). Our previous work has demonstrated the accuracy of this method in two mid-latitude mountainous regions (Zhang et al. 2013*1, Zhang et al. 2016*2), while the current research focuses on a comprehensive evaluation in three topical mountainous regions: Colombia, Peru and Taiwan. In addition, two different satellite precipitation products, NOAA Climate Prediction Center morphing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), are considered. The study includes a large number of heavy precipitation events (68 events over the three regions) in the period 2004 to 2012. The NWP-based adjustments of the two satellite products are contrasted to their corresponding gauge-adjusted post-processing products. Preliminary results show that the NWP-based adjusted CMORPH product is consistently improved relative to both original and gauge-adjusted precipitation products for all regions and storms examined. The improvement of PERSIANN-CCS product is less significant and less consistent relative to the CMORPH performance improvements from the NWP-based adjustment. *1Zhang, Xinxuan, Emmanouil N. Anagnostou, Maria Frediani, Stavros Solomos, and George Kallos. "Using NWP simulations in satellite rainfall estimation of heavy precipitation events over mountainous areas." Journal of Hydrometeorology 14, no. 6 (2013): 1844-1858.*2 Zhang, Xinxuan, Emmanouil N. Anagnostou

An evaluation of procedures to estimate monthly precipitation probabilities

NASA Astrophysics Data System (ADS)

Legates, David R.

1991-01-01

Many frequency distributions have been used to evaluate monthly precipitation probabilities. Eight of these distributions (including Pearson type III, extreme value, and transform normal probability density functions) are comparatively examined to determine their ability to represent accurately variations in monthly precipitation totals for global hydroclimatological analyses. Results indicate that a modified version of the Box-Cox transform-normal distribution more adequately describes the 'true' precipitation distribution than does any of the other methods. This assessment was made using a cross-validation procedure for a global network of 253 stations for which at least 100 years of monthly precipitation totals were available.

Three models intercomparison for Quantitative Precipitation Forecast over Calabria

NASA Astrophysics Data System (ADS)

Federico, S.; Avolio, E.; Bellecci, C.; Colacino, M.; Lavagnini, A.; Accadia, C.; Mariani, S.; Casaioli, M.

2004-11-01

In the framework of the National Project “Sviluppo di distretti industriali per le Osservazioni della Terra” (Development of Industrial Districts for Earth Observations) funded by MIUR (Ministero dell'Università e della Ricerca Scientifica --Italian Ministry of the University and Scientific Research) two operational mesoscale models were set-up for Calabria, the southernmost tip of the Italian peninsula. Models are RAMS (Regional Atmospheric Modeling System) and MM5 (Mesoscale Modeling 5) that are run every day at Crati scrl to produce weather forecast over Calabria (http://www.crati.it). This paper reports model intercomparison for Quantitative Precipitation Forecast evaluated for a 20 month period from 1th October 2000 to 31th May 2002. In addition to RAMS and MM5 outputs, QBOLAM rainfall fields are available for the period selected and included in the comparison. This model runs operationally at “Agenzia per la Protezione dell'Ambiente e per i Servizi Tecnici”. Forecasts are verified comparing models outputs with raingauge data recorded by the regional meteorological network, which has 75 raingauges. Large-scale forcing is the same for all models considered and differences are due to physical/numerical parameterizations and horizontal resolutions. QPFs show differences between models. Largest differences are for BIA compared to the other considered scores. Performances decrease with increasing forecast time for RAMS and MM5, whilst QBOLAM scores better for second day forecast.

Site Specific Probable Maximum Precipitation Estimates and Professional Judgement

NASA Astrophysics Data System (ADS)

Hayes, B. D.; Kao, S. C.; Kanney, J. F.; Quinlan, K. R.; DeNeale, S. T.

2015-12-01

State and federal regulatory authorities currently rely upon the US National Weather Service Hydrometeorological Reports (HMRs) to determine probable maximum precipitation (PMP) estimates (i.e., rainfall depths and durations) for estimating flooding hazards for relatively broad regions in the US. PMP estimates for the contributing watersheds upstream of vulnerable facilities are used to estimate riverine flooding hazards while site-specific estimates for small water sheds are appropriate for individual facilities such as nuclear power plants. The HMRs are often criticized due to their limitations on basin size, questionable applicability in regions affected by orographic effects, their lack of consist methods, and generally by their age. HMR-51 for generalized PMP estimates for the United States east of the 105th meridian, was published in 1978 and is sometimes perceived as overly conservative. The US Nuclear Regulatory Commission (NRC), is currently reviewing several flood hazard evaluation reports that rely on site specific PMP estimates that have been commercially developed. As such, NRC has recently investigated key areas of expert judgement via a generic audit and one in-depth site specific review as they relate to identifying and quantifying actual and potential storm moisture sources, determining storm transposition limits, and adjusting available moisture during storm transposition. Though much of the approach reviewed was considered a logical extension of HMRs, two key points of expert judgement stood out for further in-depth review. The first relates primarily to small storms and the use of a heuristic for storm representative dew point adjustment developed for the Electric Power Research Institute by North American Weather Consultants in 1993 in order to harmonize historic storms for which only 12 hour dew point data was available with more recent storms in a single database. The second issue relates to the use of climatological averages for spatially

The outlook for precipitation measurements from space

NASA Technical Reports Server (NTRS)

Atlas, D.; Eckerman, J.; Meneghini, R.; Moore, R. K.

1981-01-01

To provide useful precipitation measurements from space, two requirements must be met: adequate spatial and temporal sampling of the storm and sufficient accuracy in the estimate of precipitation intensity. Although presently no single instrument or method completely satisfies both requirements, the visible/IR, microwave radiometer and radar methods can be used in a complementary manner. Visible/IR instruments provide good temporal sampling and rain area depiction, but recourse must be made to microwave measurements for quantitative rainfall estimates. The inadequacy of microwave radiometer measurements over land suggests, in turn, the use of radar. Several recently developed attenuating-wavelength radar methods are discussed in terms of their accuracy, dynamic range and system implementation. Traditionally, the requirements of high resolution and adequate dynamic range led to fairly costly and complex radar systems. Some simplications and cost reduction can be made; however, by using K-band wavelengths which have the advantages of greater sensitivity at the low rain rates and higher resolution capabilities. Several recently proposed methods of this kind are reviewed in terms of accuracy and system implementation. Finally, an adaptive-pointing multi-sensor instrument is described that would exploit certain advantages of the IR, radiometric and radar methods.

Estimation of equilibrium surface precipitation constants for trivalent metal sorption onto hydrous ferric oxide and calcite

NASA Astrophysics Data System (ADS)

Ragavan, Anpalaki J.; Adams, Dean V.

2009-06-01

Equilibrium constants for modeling surface precipitation of trivalent metal cations ( M) onto hydrous ferric oxide and calcite were estimated from linear correlations of standard state Gibbs free energies of formation, ( ΔGf,MvX(ss)0) of the surface precipitates. The surface precipitation reactions were derived from Farley et. al. [K.J. Farley, D.A. Dzombak, F.M.M. Morel, J. Colloid Interface Sci. 106 (1985) 226] surface precipitation model, which are based on surface complexation model coupled with solid solution representation for surface precipitation on the solid surface. The ΔGf,MvX(ss)0 values were correlated through the following linear free energy relations ΔGf,M(OH)3(ss)0-791.70r=0.1587ΔGn,M0-1273.07 and ΔGf,M2(CO3)3(ss)0-197.241r=0.278ΔGn,M0-1431.27 where 'ss' stands for the end-member solid component of surface precipitate, ΔGf,MvX(ss)0 is in kJ/mol, r is the Shannon-Prewitt radius of M in a given coordination state (nm), and ΔGn,M0 is the non-solvation contribution to the Gibbs free energy of formation of the aqueous M ion. Results indicate that the above surface precipitation correlations are useful tools where experimental data are not available.

Quantitative Compactness Estimates for Hamilton-Jacobi Equations

NASA Astrophysics Data System (ADS)

Ancona, Fabio; Cannarsa, Piermarco; Nguyen, Khai T.

2016-02-01

We study quantitative compactness estimates in {W^{1,1}_{loc}} for the map {S_t}, {t > 0} that is associated with the given initial data {u_0in Lip (R^N)} for the corresponding solution {S_t u_0} of a Hamilton-Jacobi equation u_t+Hbig(nabla_{x} ubig)=0, qquad t≥ 0,quad xinR^N, with a uniformly convex Hamiltonian {H=H(p)}. We provide upper and lower estimates of order {1/\\varepsilon^N} on the Kolmogorov {\\varepsilon}-entropy in {W^{1,1}} of the image through the map S t of sets of bounded, compactly supported initial data. Estimates of this type are inspired by a question posed by Lax (Course on Hyperbolic Systems of Conservation Laws. XXVII Scuola Estiva di Fisica Matematica, Ravello, 2002) within the context of conservation laws, and could provide a measure of the order of "resolution" of a numerical method implemented for this equation.

Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT

NASA Astrophysics Data System (ADS)

Bindschadler, Michael; Modgil, Dimple; Branch, Kelley R.; La Riviere, Patrick J.; Alessio, Adam M.

2014-04-01

Myocardial blood flow (MBF) can be estimated from dynamic contrast enhanced (DCE) cardiac CT acquisitions, leading to quantitative assessment of regional perfusion. The need for low radiation dose and the lack of consensus on MBF estimation methods motivates this study to refine the selection of acquisition protocols and models for CT-derived MBF. DCE cardiac CT acquisitions were simulated for a range of flow states (MBF = 0.5, 1, 2, 3 ml (min g)-1, cardiac output = 3, 5, 8 L min-1). Patient kinetics were generated by a mathematical model of iodine exchange incorporating numerous physiological features including heterogenenous microvascular flow, permeability and capillary contrast gradients. CT acquisitions were simulated for multiple realizations of realistic x-ray flux levels. CT acquisitions that reduce radiation exposure were implemented by varying both temporal sampling (1, 2, and 3 s sampling intervals) and tube currents (140, 70, and 25 mAs). For all acquisitions, we compared three quantitative MBF estimation methods (two-compartment model, an axially-distributed model, and the adiabatic approximation to the tissue homogeneous model) and a qualitative slope-based method. In total, over 11 000 time attenuation curves were used to evaluate MBF estimation in multiple patient and imaging scenarios. After iodine-based beam hardening correction, the slope method consistently underestimated flow by on average 47.5% and the quantitative models provided estimates with less than 6.5% average bias and increasing variance with increasing dose reductions. The three quantitative models performed equally well, offering estimates with essentially identical root mean squared error (RMSE) for matched acquisitions. MBF estimates using the qualitative slope method were inferior in terms of bias and RMSE compared to the quantitative methods. MBF estimate error was equal at matched dose reductions for all quantitative methods and range of techniques evaluated. This suggests that

The Effectiveness of Using Limited Gauge Measurements for Bias Adjustment of Satellite-Based Precipitation Estimation over Saudi Arabia

NASA Astrophysics Data System (ADS)

Alharbi, Raied; Hsu, Kuolin; Sorooshian, Soroosh; Braithwaite, Dan

2018-01-01

Precipitation is a key input variable for hydrological and climate studies. Rain gauges are capable of providing reliable precipitation measurements at point scale. However, the uncertainty of rain measurements increases when the rain gauge network is sparse. Satellite -based precipitation estimations appear to be an alternative source of precipitation measurements, but they are influenced by systematic bias. In this study, a method for removing the bias from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) over a region where the rain gauge is sparse is investigated. The method consists of monthly empirical quantile mapping, climate classification, and inverse-weighted distance method. Daily PERSIANN-CCS is selected to test the capability of the method for removing the bias over Saudi Arabia during the period of 2010 to 2016. The first six years (2010 - 2015) are calibrated years and 2016 is used for validation. The results show that the yearly correlation coefficient was enhanced by 12%, the yearly mean bias was reduced by 93% during validated year. Root mean square error was reduced by 73% during validated year. The correlation coefficient, the mean bias, and the root mean square error show that the proposed method removes the bias on PERSIANN-CCS effectively that the method can be applied to other regions where the rain gauge network is sparse.

Improved infrared precipitation estimation approaches based on k-means clustering: Application to north Algeria using MSG-SEVIRI satellite data

NASA Astrophysics Data System (ADS)

Mokdad, Fatiha; Haddad, Boualem

2017-06-01

In this paper, two new infrared precipitation estimation approaches based on the concept of k-means clustering are first proposed, named the NAW-Kmeans and the GPI-Kmeans methods. Then, they are adapted to the southern Mediterranean basin, where the subtropical climate prevails. The infrared data (10.8 μm channel) acquired by MSG-SEVIRI sensor in winter and spring 2012 are used. Tests are carried out in eight areas distributed over northern Algeria: Sebra, El Bordj, Chlef, Blida, Bordj Menael, Sidi Aich, Beni Ourthilane, and Beni Aziz. The validation is performed by a comparison of the estimated rainfalls to rain gauges observations collected by the National Office of Meteorology in Dar El Beida (Algeria). Despite the complexity of the subtropical climate, the obtained results indicate that the NAW-Kmeans and the GPI-Kmeans approaches gave satisfactory results for the considered rain rates. Also, the proposed schemes lead to improvement in precipitation estimation performance when compared to the original algorithms NAW (Nagri, Adler, and Wetzel) and GPI (GOES Precipitation Index).

Benefits of an Advanced Quantitative Precipitation Information System - San Francisco Bay Area Case Study

NASA Astrophysics Data System (ADS)

Cifelli, R.; Johnson, L. E.; White, A. B.

2014-12-01

Advancements in monitoring and prediction of precipitation and severe storms can provide significant benefits for water resource managers, allowing them to mitigate flood damage risks, capture additional water supplies and offset drought impacts, and enhance ecosystem services. A case study for the San Francisco Bay area provides the context for quantification of the benefits of an Advanced Quantitative Precipitation Information (AQPI) system. The AQPI builds off more than a decade of NOAA research and applications of advanced precipitation sensors, data assimilation, numerical models of storms and storm runoff, and systems integration for real-time operations. An AQPI would dovetail with the current National Weather Service forecast operations to provide higher resolution monitoring of rainfall events and longer lead time forecasts. A regional resource accounting approach has been developed to quantify the incremental benefits assignable to the AQPI system; these benefits total to $35 M/yr in the 9 county Bay region. Depending on the jurisdiction large benefits for flood damage avoidance may accrue for locations having dense development in flood plains. In other locations forecst=based reservoir operations can increase reservoir storage for water supplies. Ecosystem services benefits for fisheries may be obtained from increased reservoir storage and downstream releases. Benefits in the transporation sectors are associated with increased safety and avoided delays. Compared to AQPI system implementation and O&M costs over a 10 year operations period, a benefit - cost (B/C) ratio is computed which ranges between 2.8 to 4. It is important to acknowledge that many of the benefits are dependent on appropriate and adequate response by the hazards and water resources management agencies and citizens.

Impact of Precipitating Ice Hydrometeors on Longwave Radiative Effect Estimated by a Global Cloud-System Resolving Model

NASA Astrophysics Data System (ADS)

Chen, Ying-Wen; Seiki, Tatsuya; Kodama, Chihiro; Satoh, Masaki; Noda, Akira T.

2018-02-01

Satellite observation and general circulation model (GCM) studies suggest that precipitating ice makes nonnegligible contributions to the radiation balance of the Earth. However, in most GCMs, precipitating ice is diagnosed and its radiative effects are not taken into account. Here we examine the longwave radiative impact of precipitating ice using a global nonhydrostatic atmospheric model with a double-moment cloud microphysics scheme. An off-line radiation model is employed to determine cloud radiative effects according to the amount and altitude of each type of ice hydrometeor. Results show that the snow radiative effect reaches 2 W m-2 in the tropics, which is about half the value estimated by previous studies. This effect is strongly dependent on the vertical separation of ice categories and is partially generated by differences in terminal velocities, which are not represented in GCMs with diagnostic precipitating ice. Results from sensitivity experiments that artificially change the categories and altitudes of precipitating ice show that the simulated longwave heating profile and longwave radiation field are sensitive to the treatment of precipitating ice in models. This study emphasizes the importance of incorporating appropriate treatments for the radiative effects of precipitating ice in cloud and radiation schemes in GCMs in order to capture the cloud radiative effects of upper level clouds.

Quantitative estimation of time-variable earthquake hazard by using fuzzy set theory

NASA Astrophysics Data System (ADS)

Deyi, Feng; Ichikawa, M.

1989-11-01

In this paper, the various methods of fuzzy set theory, called fuzzy mathematics, have been applied to the quantitative estimation of the time-variable earthquake hazard. The results obtained consist of the following. (1) Quantitative estimation of the earthquake hazard on the basis of seismicity data. By using some methods of fuzzy mathematics, seismicity patterns before large earthquakes can be studied more clearly and more quantitatively, highly active periods in a given region and quiet periods of seismic activity before large earthquakes can be recognized, similarities in temporal variation of seismic activity and seismic gaps can be examined and, on the other hand, the time-variable earthquake hazard can be assessed directly on the basis of a series of statistical indices of seismicity. Two methods of fuzzy clustering analysis, the method of fuzzy similarity, and the direct method of fuzzy pattern recognition, have been studied is particular. One method of fuzzy clustering analysis is based on fuzzy netting, and another is based on the fuzzy equivalent relation. (2) Quantitative estimation of the earthquake hazard on the basis of observational data for different precursors. The direct method of fuzzy pattern recognition has been applied to research on earthquake precursors of different kinds. On the basis of the temporal and spatial characteristics of recognized precursors, earthquake hazards in different terms can be estimated. This paper mainly deals with medium-short-term precursors observed in Japan and China.

Evaluation and intercomparison of GPM-IMERG and TRMM 3B42 daily precipitation products over Greece

NASA Astrophysics Data System (ADS)

Kazamias, A. P.; Sapountzis, M.; Lagouvardos, K.

2017-09-01

Accurate precipitation data at high temporal and spatial resolutions are needed for numerous applications in hydrology, water resources management and flood risk management. Satellite-based precipitation estimations/products offer a potential alternative source of rainfall data for regions with sparse rain gauge network. The recently launched Global Precipitation Measurement (GPM) mission is the successor of Tropical Rainfall Measuring Mission (TRMM) providing global precipitation estimates at spatial resolution of 0.1 degree x 0.1 degree and half-hourly temporal resolution. This study aims at evaluating the accuracy of the Integrated Multi-satellite Retrievals for GPM (IMERG) near-real-time daily product (GPM-3IMERGDL) against rain gauge observations from a network of stations distributed across Greece for the year 2016. Moreover, the GPM-IMERG product is also compared with its predecessor, the Version-7 near-real-time (3B42RT) daily product of TRMM Multisatellite Precipitation Analysis (TMPA). Several statistical metrics are used to quantitatively evaluate the performance of the satellite-based precipitation estimates against rain gauge observations. In addition, categorical statistical indices are used to assess rain detection capabilities of the two satellite products. The GPM-IMERG daily product shows reasonable agreement (CC=0.60) against rain gauge observations, with the exception of coastal areas in which low correlations are achieved. The GPM-IMERG daily precipitation product tends to overestimate rainfall, especially in complex terrain areas with high annual precipitation. In particular, rainfall estimates in western Greece have a strong positive bias. On the other hand, the TRMM 3B42 product shows low correlation (CC=0.45) against rain gauge observations and slightly underestimates rainfall. This study is a first attempt to evaluate and compare the newly introduced GPM-IMERG and the TRMM 3B42 rainfall products at daily timescale over Greece.

The Global Precipitation Measurement Mission

NASA Astrophysics Data System (ADS)

Jackson, Gail

2014-05-01

The Global Precipitation Measurement (GPM) mission's Core satellite, scheduled for launch at the end of February 2014, is well designed estimate precipitation from 0.2 to 110 mm/hr and to detect falling snow. Knowing where and how much rain and snow falls globally is vital to understanding how weather and climate impact both our environment and Earth's water and energy cycles, including effects on agriculture, fresh water availability, and responses to natural disasters. The design of the GPM Core Observatory is an advancement of the Tropical Rainfall Measuring Mission (TRMM)'s highly successful rain-sensing package [3]. The cornerstone of the GPM mission is the deployment of a Core Observatory in a unique 65o non-Sun-synchronous orbit to serve as a physics observatory and a calibration reference to improve precipitation measurements by a constellation of 8 or more dedicated and operational, U.S. and international passive microwave sensors. The Core Observatory will carry a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a multi-channel (10-183 GHz) GPM Microwave Radiometer (GMI). The DPR will provide measurements of 3-D precipitation structures and microphysical properties, which are key to achieving a better understanding of precipitation processes and improving retrieval algorithms for passive microwave radiometers. The combined use of DPR and GMI measurements will place greater constraints on possible solutions to radiometer retrievals to improve the accuracy and consistency of precipitation retrievals from all constellation radiometers. Furthermore, since light rain and falling snow account for a significant fraction of precipitation occurrence in middle and high latitudes, the GPM instruments extend the capabilities of the TRMM sensors to detect falling snow, measure light rain, and provide, for the first time, quantitative estimates of microphysical properties of precipitation particles. The GPM Core Observatory was developed and tested at NASA

Retrospective Analog Year Analyses Using NASA Satellite Precipitation and Soil Moisture Data to Improve USDA's World Agricultural Supply and Demand Estimates

NASA Astrophysics Data System (ADS)

Teng, W. L.; Shannon, H.

2010-12-01

The USDA World Agricultural Outlook Board (WAOB) coordinates the development of the monthly World Agricultural Supply and Demand Estimates (WASDE) for the U.S. and major foreign producing countries. Given the significant effect of weather on crop progress, conditions, and production, WAOB prepares frequent agricultural weather assessments in the Global Agricultural Decision Support Environment (GLADSE). Because the timing of the precipitation is often as important as the amount, in their effects on crop production, WAOB frequently examines precipitation time series to estimate crop productivity. An effective method for such assessment is the use of analog year comparisons, where precipitation time series, based on surface weather stations, from several historical years are compared with the time series from the current year. Once analog years are identified, crop yields can be estimated for the current season based on observed yields from the analog years, because of the similarities in the precipitation patterns. In this study, NASA satellite precipitation and soil moisture time series are used to identify analog years. Given that soil moisture often has a more direct effect than does precipitation on crop water availability, the time series of soil moisture could be more effective than that of precipitation, in identifying those years with similar crop yields. Retrospective analyses of analogs will be conducted to determine any reduction in the level of uncertainty in identifying analog years, and any reduction in false negatives or false positives. The comparison of analog years could potentially be improved by quantifying the selection of analogs, instead of the current visual inspection method. Various approaches to quantifying are currently being evaluated. This study is part of a larger effort to improve WAOB estimates by integrating NASA remote sensing soil moisture observations and research results into GLADSE, including (1) the integration of the Land

Using damage data to estimate the risk from summer convective precipitation extremes

NASA Astrophysics Data System (ADS)

Schroeer, Katharina; Tye, Mari

2017-04-01

model to test whether the relationship between extreme rainfall events and damages is robust enough to estimate a potential underrepresentation of high intensity rainfall events in ungauged areas. Risk-relevant factors of socio-economic vulnerability, land cover, streamflow data, and weather type information are included to improve and sharpen the analysis. Within this study, we first aim to identify which rainfall events are most damaging and which factors affect the damages - seen as a proxy for the vulnerability - related to summer convective rainfall extremes in different catchment types. Secondly, we aim to detect potentially unreported damaging rainfall events and estimate the likelihood of such cases. We anticipate this damage perspective on summertime extreme convective precipitation to be beneficial for risk assessment, uncertainty management, and decision making with respect to weather and climate extremes on the regional-to-local level.

MSWEP V2 global 3-hourly 0.1° precipitation: methodology and quantitative appraisal

NASA Astrophysics Data System (ADS)

Beck, H.; Yang, L.; Pan, M.; Wood, E. F.; William, L.

2017-12-01

Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) V2, the first fully global gridded precipitation (P) dataset with a 0.1° spatial resolution. The dataset covers the period 1979-2016, has a 3-hourly temporal resolution, and was derived by optimally merging a wide range of data sources based on gauges (WorldClim, GHCN-D, GSOD, and others), satellites (CMORPH, GridSat, GSMaP, and TMPA 3B42RT), and reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR). MSWEP V2 implements some major improvements over V1, such as (i) the correction of distributional P biases using cumulative distribution function matching, (ii) increasing the spatial resolution from 0.25° to 0.1°, (iii) the inclusion of ocean areas, (iv) the addition of NCEP-CFSR P estimates, (v) the addition of thermal infrared-based P estimates for the pre-TRMM era, (vi) the addition of 0.1° daily interpolated gauge data, (vii) the use of a daily gauge correction scheme that accounts for regional differences in the 24-hour accumulation period of gauges, and (viii) extension of the data record to 2016. The gauge-based assessment of the reanalysis and satellite P datasets, necessary for establishing the merging weights, revealed that the reanalysis datasets strongly overestimate the P frequency for the entire globe, and that the satellite (resp. reanalysis) datasets consistently performed better at low (high) latitudes. Compared to other state-of-the-art P datasets, MSWEP V2 exhibits more plausible global patterns in mean annual P, percentiles, and annual number of dry days, and better resolves the small-scale variability over topographically complex terrain. Other P datasets appear to consistently underestimate P amounts over mountainous regions. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 959, 796, and 1026 mm/yr, respectively, in close agreement with the best previous published estimates.

Operational 0-3 h probabilistic quantitative precipitation forecasts: Recent performance and potential enhancements

NASA Astrophysics Data System (ADS)

Sokol, Z.; Kitzmiller, D.; Pešice, P.; Guan, S.

2009-05-01

The NOAA National Weather Service has maintained an automated, centralized 0-3 h prediction system for probabilistic quantitative precipitation forecasts since 2001. This advective-statistical system (ADSTAT) produces probabilities that rainfall will exceed multiple threshold values up to 50 mm at some location within a 40-km grid box. Operational characteristics and development methods for the system are described. Although development data were stratified by season and time of day, ADSTAT utilizes only a single set of nation-wide equations that relate predictor variables derived from radar reflectivity, lightning, satellite infrared temperatures, and numerical prediction model output to rainfall occurrence. A verification study documented herein showed that the operational ADSTAT reliably models regional variations in the relative frequency of heavy rain events. This was true even in the western United States, where no regional-scale, gridded hourly precipitation data were available during the development period in the 1990s. An effort was recently launched to improve the quality of ADSTAT forecasts by regionalizing the prediction equations and to adapt the model for application in the Czech Republic. We have experimented with incorporating various levels of regional specificity in the probability equations. The geographic localization study showed that in the warm season, regional climate differences and variations in the diurnal temperature cycle have a marked effect on the predictor-predictand relationships, and thus regionalization would lead to better statistical reliability in the forecasts.

Estimation of precipitable water over the Amazon Basin using GOES imagery

NASA Astrophysics Data System (ADS)

Callahan, John Andrew

The Amazon Rainforest is the largest continuous rainforest on Earth. It holds a rich abundance of life containing approximately one-half of all existing plant and animal species and 20% of the world's fresh water. Climatologically, the Amazon Rainforest is a massive storehouse of carbon dioxide and water vapor and hosts hydrologic and energy cycles that influence regional and global patterns. However, this region has gone through vast land cover changes during the past several decades. Lack of conventional, in situ data sources prohibits detailed measurements to assess the climatological impact these changes may cause. This thesis applies a satellite-based, thermal infrared remote sensing algorithm to determine precipitable water in the Amazon Basin to test its applicability in the region and to measure the diurnal changes in water vapor. Imagery from the GOES geostationary satellite and estimated atmospheric conditions and radiance values derived from the NCEP/NCAR Reanalysis project were used as inputs to the Physical Split Window (PSW) technique. Retrievals of precipitable water were made every 3 hours throughout each day from 12Z to 24Z for the months of June and October, 1988 and 1995. These months correspond to when the atmosphere is not dominated by clouds during the rainy (wet) season or smoke and haze during the burning (dry) season. Monthly, daily, and diurnal aggregates of precipitable water Fields were analyzed spatially through seven zones located uniformly throughout the region. Monthly average precipitable water values were found to be 20mm to 25mm in the southeast and 45mm to 50mm in the northwest zones. Central and northwest zones showed little variation throughout the day with most areas peaking between 15Z and 21Z, representing early to late afternoon local time. Comparisons were made to nearby, coincident radiosonde observations with r ranging from 0.7 to 0.9 and MAE from 6mm to 12 mm.

Stochastic error model corrections to improve the performance of bottom-up precipitation products for hydrologic applications

NASA Astrophysics Data System (ADS)

Maggioni, V.; Massari, C.; Ciabatta, L.; Brocca, L.

2016-12-01

Accurate quantitative precipitation estimation is of great importance for water resources management, agricultural planning, and forecasting and monitoring of natural hazards such as flash floods and landslides. In situ observations are limited around the Earth, especially in remote areas (e.g., complex terrain, dense vegetation), but currently available satellite precipitation products are able to provide global precipitation estimates with an accuracy that depends upon many factors (e.g., type of storms, temporal sampling, season, etc.). The recent SM2RAIN approach proposes to estimate rainfall by using satellite soil moisture observations. As opposed to traditional satellite precipitation methods, which sense cloud properties to retrieve instantaneous estimates, this new bottom-up approach makes use of two consecutive soil moisture measurements for obtaining an estimate of the fallen precipitation within the interval between two satellite overpasses. As a result, the nature of the measurement is different and complementary to the one of classical precipitation products and could provide a different valid perspective to substitute or improve current rainfall estimates. However, uncertainties in the SM2RAIN product are still not well known and could represent a limitation in utilizing this dataset for hydrological applications. Therefore, quantifying the uncertainty associated with SM2RAIN is necessary for enabling its use. The study is conducted over the Italian territory for a 5-yr period (2010-2014). A number of satellite precipitation error properties, typically used in error modeling, are investigated and include probability of detection, false alarm rates, missed events, spatial correlation of the error, and hit biases. After this preliminary uncertainty analysis, the potential of applying the stochastic rainfall error model SREM2D to correct SM2RAIN and to improve its performance in hydrologic applications is investigated. The use of SREM2D for

A probabilistic storm transposition approach for estimating exceedance probabilities of extreme precipitation depths

NASA Astrophysics Data System (ADS)

Foufoula-Georgiou, E.

1989-05-01

A storm transposition approach is investigated as a possible tool of assessing the frequency of extreme precipitation depths, that is, depths of return period much greater than 100 years. This paper focuses on estimation of the annual exceedance probability of extreme average precipitation depths over a catchment. The probabilistic storm transposition methodology is presented, and the several conceptual and methodological difficulties arising in this approach are identified. The method is implemented and is partially evaluated by means of a semihypothetical example involving extreme midwestern storms and two hypothetical catchments (of 100 and 1000 mi2 (˜260 and 2600 km2)) located in central Iowa. The results point out the need for further research to fully explore the potential of this approach as a tool for assessing the probabilities of rare storms, and eventually floods, a necessary element of risk-based analysis and design of large hydraulic structures.

The theory, direction, and magnitude of ecosystem fire probability as constrained by precipitation and temperature.

PubMed

Guyette, Richard; Stambaugh, Michael C; Dey, Daniel; Muzika, Rose Marie

2017-01-01

The effects of climate on wildland fire confronts society across a range of different ecosystems. Water and temperature affect the combustion dynamics, irrespective of whether those are associated with carbon fueled motors or ecosystems, but through different chemical, physical, and biological processes. We use an ecosystem combustion equation developed with the physical chemistry of atmospheric variables to estimate and simulate fire probability and mean fire interval (MFI). The calibration of ecosystem fire probability with basic combustion chemistry and physics offers a quantitative method to address wildland fire in addition to the well-studied forcing factors such as topography, ignition, and vegetation. We develop a graphic analysis tool for estimating climate forced fire probability with temperature and precipitation based on an empirical assessment of combustion theory and fire prediction in ecosystems. Climate-affected fire probability for any period, past or future, is estimated with given temperature and precipitation. A graphic analyses of wildland fire dynamics driven by climate supports a dialectic in hydrologic processes that affect ecosystem combustion: 1) the water needed by plants to produce carbon bonds (fuel) and 2) the inhibition of successful reactant collisions by water molecules (humidity and fuel moisture). These two postulates enable a classification scheme for ecosystems into three or more climate categories using their position relative to change points defined by precipitation in combustion dynamics equations. Three classifications of combustion dynamics in ecosystems fire probability include: 1) precipitation insensitive, 2) precipitation unstable, and 3) precipitation sensitive. All three classifications interact in different ways with variable levels of temperature.

The theory, direction, and magnitude of ecosystem fire probability as constrained by precipitation and temperature

PubMed Central

Guyette, Richard; Stambaugh, Michael C.; Dey, Daniel

2017-01-01

The effects of climate on wildland fire confronts society across a range of different ecosystems. Water and temperature affect the combustion dynamics, irrespective of whether those are associated with carbon fueled motors or ecosystems, but through different chemical, physical, and biological processes. We use an ecosystem combustion equation developed with the physical chemistry of atmospheric variables to estimate and simulate fire probability and mean fire interval (MFI). The calibration of ecosystem fire probability with basic combustion chemistry and physics offers a quantitative method to address wildland fire in addition to the well-studied forcing factors such as topography, ignition, and vegetation. We develop a graphic analysis tool for estimating climate forced fire probability with temperature and precipitation based on an empirical assessment of combustion theory and fire prediction in ecosystems. Climate-affected fire probability for any period, past or future, is estimated with given temperature and precipitation. A graphic analyses of wildland fire dynamics driven by climate supports a dialectic in hydrologic processes that affect ecosystem combustion: 1) the water needed by plants to produce carbon bonds (fuel) and 2) the inhibition of successful reactant collisions by water molecules (humidity and fuel moisture). These two postulates enable a classification scheme for ecosystems into three or more climate categories using their position relative to change points defined by precipitation in combustion dynamics equations. Three classifications of combustion dynamics in ecosystems fire probability include: 1) precipitation insensitive, 2) precipitation unstable, and 3) precipitation sensitive. All three classifications interact in different ways with variable levels of temperature. PMID:28704457

Assessment of Evolving TRMM-Based Real-Time Precipitation Estimation Methods and Their Impacts on Hydrologic Prediction in a High-Latitude Basin

NASA Technical Reports Server (NTRS)

Yong, Bin; Hong, Yang; Ren, Li-Liang; Gourley, Jonathan; Huffman, George J.; Chen, Xi; Wang, Wen; Khan, Sadiq I.

2013-01-01

The real-time availability of satellite-derived precipitation estimates provides hydrologists an opportunity to improve current hydrologic prediction capability for medium to large river basins. Due to the availability of new satellite data and upgrades to the precipitation algorithms, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis real-time estimates (TMPA-RT) have been undergoing several important revisions over the past ten years. In this study, the changes of the relative accuracy and hydrologic potential of TMPA-RT estimates over its three major evolving periods were evaluated and inter-compared at daily, monthly and seasonal scales in the high-latitude Laohahe basin in China. Assessment results show that the performance of TMPA-RT in terms of precipitation estimation and streamflow simulation was significantly improved after 3 February 2005. Overestimation during winter months was noteworthy and consistent, which is suggested to be a consequence from interference of snow cover to the passive microwave retrievals. Rainfall estimated by the new version 6 of TMPA-RT starting from 1 October 2008 to present has higher correlations with independent gauge observations and tends to perform better in detecting rain compared to the prior periods, although it suffers larger mean error and relative bias. After a simple bias correction, this latest dataset of TMPA-RT exhibited the best capability in capturing hydrologic response among the three tested periods. In summary, this study demonstrated that there is an increasing potential in the use of TMPA-RT in hydrologic streamflow simulations over its three algorithm upgrade periods, but still with significant challenges during the winter snowing events.

The impact of reflectivity correction and conversion methods to improve precipitation estimation by weather radar for an extreme low-land Mesoscale Convective System

NASA Astrophysics Data System (ADS)

Hazenberg, Pieter; Leijnse, Hidde; Uijlenhoet, Remko

2014-05-01

Between 25 and 27 August 2010 a long-duration mesoscale convective system was observed above the Netherlands. For most of the country this led to over 15 hours of near-continuous precipitation, which resulted in total event accumulations exceeding 150 mm in the eastern part of the Netherlands. Such accumulations belong to the largest sums ever recorded in this country and gave rise to local flooding. Measuring precipitation by weather radar within such mesoscale convective systems is known to be a challenge, since measurements are affected by multiple sources of error. For the current event the operational weather radar rainfall product only estimated about 30% of the actual amount of precipitation as measured by rain gauges. In the current presentation we will try to identify what gave rise to such large underestimations. In general weather radar measurement errors can be subdivided into two different groups: 1) errors affecting the volumetric reflectivity measurements taken, and 2) errors related to the conversion of reflectivity values in rainfall intensity and attenuation estimates. To correct for the first group of errors, the quality of the weather radar reflectivity data was improved by successively correcting for 1) clutter and anomalous propagation, 2) radar calibration, 3) wet radome attenuation, 4) signal attenuation and 5) the vertical profile of reflectivity. Such consistent corrections are generally not performed by operational meteorological services. Results show a large improvement in the quality of the precipitation data, however still only ~65% of the actual observed accumulations was estimated. To further improve the quality of the precipitation estimates, the second group of errors are corrected for by making use of disdrometer measurements taken in close vicinity of the radar. Based on these data the parameters of a normalized drop size distribution are estimated for the total event as well as for each precipitation type separately (convective

Satellite-Based Precipitation Datasets

NASA Astrophysics Data System (ADS)

Munchak, S. J.; Huffman, G. J.

2017-12-01

Of the possible sources of precipitation data, those based on satellites provide the greatest spatial coverage. There is a wide selection of datasets, algorithms, and versions from which to choose, which can be confusing to non-specialists wishing to use the data. The International Precipitation Working Group (IPWG) maintains tables of the major publicly available, long-term, quasi-global precipitation data sets (http://www.isac.cnr.it/ ipwg/data/datasets.html), and this talk briefly reviews the various categories. As examples, NASA provides two sets of quasi-global precipitation data sets: the older Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and current Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG). Both provide near-real-time and post-real-time products that are uniformly gridded in space and time. The TMPA products are 3-hourly 0.25°x0.25° on the latitude band 50°N-S for about 16 years, while the IMERG products are half-hourly 0.1°x0.1° on 60°N-S for over 3 years (with plans to go to 16+ years in Spring 2018). In addition to the precipitation estimates, each data set provides fields of other variables, such as the satellite sensor providing estimates and estimated random error. The discussion concludes with advice about determining suitability for use, the necessity of being clear about product names and versions, and the need for continued support for satellite- and surface-based observation.

Comparison of NEXRAD multisensor precipitation estimates to rain gage observations in and near DuPage County, Illinois, 2002–12

USGS Publications Warehouse

Spies, Ryan R.; Over, Thomas M.; Ortel, Terry W.

2018-05-21

In this report, precipitation data from 2002 to 2012 from the hourly gridded Next-Generation Radar (NEXRAD)-based Multisensor Precipitation Estimate (MPE) precipitation product are compared to precipitation data from two rain gage networks—an automated tipping bucket network of 25 rain gages operated by the U.S. Geological Survey (USGS) and 51 rain gages from the volunteer-operated Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network—in and near DuPage County, Illinois, at a daily time step to test for long-term differences in space, time, and distribution. The NEXRAD–MPE data that are used are from the fifty 2.5-mile grid cells overlying the rain gages from the other networks. Because of the challenges of measuring of frozen precipitation, the analysis period is separated between days with or without the chance of freezing conditions. The NEXRAD–MPE and tipping-bucket rain gage precipitation data are adjusted to account for undercatch by multiplying by a previously determined factor of 1.14. Under nonfreezing conditions, the three precipitation datasets are broadly similar in cumulative depth and distribution of daily values when the data are combined spatially across the networks. However, the NEXRAD–MPE data indicate a significant trend relative to both rain gage networks as a function of distance from the NEXRAD radar just south of the study area. During freezing conditions, of the USGS network rain gages only the heated gages were considered, and these gages indicate substantial mean undercatch of 50 and 61 percent compared to the NEXRAD–MPE and the CoCoRaHS gages, respectively. The heated USGS rain gages also indicate substantially lower quantile values during freezing conditions, except during the most extreme (highest) events. Because NEXRAD precipitation products are continually evolving, the report concludes with a discussion of recent changes in those products and their potential for improved precipitation estimation. An appendix

STAMMEX high resolution gridded daily precipitation dataset over Germany: a new potential for regional precipitation climate research

NASA Astrophysics Data System (ADS)

Zolina, Olga; Simmer, Clemens; Kapala, Alice; Mächel, Hermann; Gulev, Sergey; Groisman, Pavel

2014-05-01

We present new high resolution precipitation daily grids developed at Meteorological Institute, University of Bonn and German Weather Service (DWD) under the STAMMEX project (Spatial and Temporal Scales and Mechanisms of Extreme Precipitation Events over Central Europe). Daily precipitation grids have been developed from the daily-observing precipitation network of DWD, which runs one of the World's densest rain gauge networks comprising more than 7500 stations. Several quality-controlled daily gridded products with homogenized sampling were developed covering the periods 1931-onwards (with 0.5 degree resolution), 1951-onwards (0.25 degree and 0.5 degree), and 1971-2000 (0.1 degree). Different methods were tested to select the best gridding methodology that minimizes errors of integral grid estimates over hilly terrain. Besides daily precipitation values with uncertainty estimates (which include standard estimates of the kriging uncertainty as well as error estimates derived by a bootstrapping algorithm), the STAMMEX data sets include a variety of statistics that characterize temporal and spatial dynamics of the precipitation distribution (quantiles, extremes, wet/dry spells, etc.). Comparisons with existing continental-scale daily precipitation grids (e.g., CRU, ECA E-OBS, GCOS) which include considerably less observations compared to those used in STAMMEX, demonstrate the added value of high-resolution grids for extreme rainfall analyses. These data exhibit spatial variability pattern and trends in precipitation extremes, which are missed or incorrectly reproduced over Central Europe from coarser resolution grids based on sparser networks. The STAMMEX dataset can be used for high-quality climate diagnostics of precipitation variability, as a reference for reanalyses and remotely-sensed precipitation products (including the upcoming Global Precipitation Mission products), and for input into regional climate and operational weather forecast models. We will present

Quantitative genetic tools for insecticide resistance risk assessment: estimating the heritability of resistance

Treesearch

Michael J. Firko; Jane Leslie Hayes

1990-01-01

Quantitative genetic studies of resistance can provide estimates of genetic parameters not available with other types of genetic analyses. Three methods are discussed for estimating the amount of additive genetic variation in resistance to individual insecticides and subsequent estimation of heritability (h2) of resistance. Sibling analysis and...

Climatic Variability of Precipitation from the Seasonal Cycle to ENSO Using GPCP's Merged Data Product and SSM/I-Based Microwave Estimates

NASA Technical Reports Server (NTRS)

Curtis, Scott; Huffman, George; Nelkin, Eric

1999-01-01

Satellite estimates and gauge observations of precipitation are useful in understanding the water cycle, analyzing climatic variability, and validating climate models. The Global Precipitation Climatology Project (GPCP) released a community merged precipitation data set for the period July 1987 through the present, and has recently extended that data set back to 1986. One objective of this study is to use GPCP estimates to describe and quantify the seasonal variation of precipitation, with emphasis on the Asian summer monsoon. Another focus is the 1997-98 El Nino Southern Oscillation (ENSO) and associated extreme precipitation events. The summer monsoon tends to be drier than normal in El Nino ears. This was not observed for 1997 or 1998, while for 1997 the NCEP model produced the largest summer rain rates over India in years. This inconsistency will be examined. The average annual global precipitation rate is 2.7 mm day as estimated by GPCP, which is similar to values computed from long-term climatologies. From 30 deg N to 30 deg S the average precipitation rate is 2.7 mm day over land with a maximum in the annual cycle occurring in February-March, when the Amazon basin receives abundant rainfall. The average precipitation rate is 3.1 mm day over the tropical oceans, with a peak earlier in the season (November-December), corresponding with the transition from a strong Pacific Intertropical Convergence Zone (ITCZ) from June to November to a strong South Pacific Convergence Zone (SPCZ) from December to March. The seasonal evolution of C, C, the Asian summer monsoon stands out with rains in excess of 15 mm day off the coast of Burma in June. The GPROF pentad data also captures the onset of the tropical Pacific rainfall patterns associated with the 1997-98 ENSO. From February to October 1997 at least four rain-producing systems traveled from West to East in the equatorial corridor. A rapid transition from El Nino to La Nina conditions occurred in May-June 1998. GPCP

A multi-source precipitation approach to fill gaps over a radar precipitation field

NASA Astrophysics Data System (ADS)

Tesfagiorgis, K. B.; Mahani, S. E.; Khanbilvardi, R.

2012-12-01

Satellite Precipitation Estimates (SPEs) may be the only available source of information for operational hydrologic and flash flood prediction due to spatial limitations of radar and gauge products. The present work develops an approach to seamlessly blend satellite, radar, climatological and gauge precipitation products to fill gaps over ground-based radar precipitation fields. To mix different precipitation products, the bias of any of the products relative to each other should be removed. For bias correction, the study used an ensemble-based method which aims to estimate spatially varying multiplicative biases in SPEs using a radar rainfall product. Bias factors were calculated for a randomly selected sample of rainy pixels in the study area. Spatial fields of estimated bias were generated taking into account spatial variation and random errors in the sampled values. A weighted Successive Correction Method (SCM) is proposed to make the merging between error corrected satellite and radar rainfall estimates. In addition to SCM, we use a Bayesian spatial method for merging the gap free radar with rain gauges, climatological rainfall sources and SPEs. We demonstrate the method using SPE Hydro-Estimator (HE), radar- based Stage-II, a climatological product PRISM and rain gauge dataset for several rain events from 2006 to 2008 over three different geographical locations of the United States. Results show that: the SCM method in combination with the Bayesian spatial model produced a precipitation product in good agreement with independent measurements. The study implies that using the available radar pixels surrounding the gap area, rain gauge, PRISM and satellite products, a radar like product is achievable over radar gap areas that benefits the scientific community.

Scoping a field experiment: error diagnostics of TRMM precipitation radar estimates in complex terrain as a basis for IPHEx2014

NASA Astrophysics Data System (ADS)

Duan, Y.; Wilson, A. M.; Barros, A. P.

2014-10-01

A diagnostic analysis of the space-time structure of error in Quantitative Precipitation Estimates (QPE) from the Precipitation Radar (PR) on the Tropical Rainfall Measurement Mission (TRMM) satellite is presented here in preparation for the Integrated Precipitation and Hydrology Experiment (IPHEx) in 2014. IPHEx is the first NASA ground-validation field campaign after the launch of the Global Precipitation Measurement (GPM) satellite. In anticipation of GPM, a science-grade high-density raingauge network was deployed at mid to high elevations in the Southern Appalachian Mountains, USA since 2007. This network allows for direct comparison between ground-based measurements from raingauges and satellite-based QPE (specifically, PR 2A25 V7 using 5 years of data 2008-2013). Case studies were conducted to characterize the vertical profiles of reflectivity and rain rate retrievals associated with large discrepancies with respect to ground measurements. The spatial and temporal distribution of detection errors (false alarm, FA, and missed detection, MD) and magnitude errors (underestimation, UND, and overestimation, OVR) for stratiform and convective precipitation are examined in detail toward elucidating the physical basis of retrieval error. The diagnostic error analysis reveals that detection errors are linked to persistent stratiform light rainfall in the Southern Appalachians, which explains the high occurrence of FAs throughout the year, as well as the diurnal MD maximum at midday in the cold season (fall and winter), and especially in the inner region. Although UND dominates the magnitude error budget, underestimation of heavy rainfall conditions accounts for less than 20% of the total consistent with regional hydrometeorology. The 2A25 V7 product underestimates low level orographic enhancement of rainfall associated with fog, cap clouds and cloud to cloud feeder-seeder interactions over ridges, and overestimates light rainfall in the valleys by large amounts

Scoping a field experiment: error diagnostics of TRMM precipitation radar estimates in complex terrain as a basis for IPHEx2014

NASA Astrophysics Data System (ADS)

Duan, Y.; Wilson, A. M.; Barros, A. P.

2015-03-01

A diagnostic analysis of the space-time structure of error in quantitative precipitation estimates (QPEs) from the precipitation radar (PR) on the Tropical Rainfall Measurement Mission (TRMM) satellite is presented here in preparation for the Integrated Precipitation and Hydrology Experiment (IPHEx) in 2014. IPHEx is the first NASA ground-validation field campaign after the launch of the Global Precipitation Measurement (GPM) satellite. In anticipation of GPM, a science-grade high-density raingauge network was deployed at mid to high elevations in the southern Appalachian Mountains, USA, since 2007. This network allows for direct comparison between ground-based measurements from raingauges and satellite-based QPE (specifically, PR 2A25 Version 7 using 5 years of data 2008-2013). Case studies were conducted to characterize the vertical profiles of reflectivity and rain rate retrievals associated with large discrepancies with respect to ground measurements. The spatial and temporal distribution of detection errors (false alarm, FA; missed detection, MD) and magnitude errors (underestimation, UND; overestimation, OVR) for stratiform and convective precipitation are examined in detail toward elucidating the physical basis of retrieval error. The diagnostic error analysis reveals that detection errors are linked to persistent stratiform light rainfall in the southern Appalachians, which explains the high occurrence of FAs throughout the year, as well as the diurnal MD maximum at midday in the cold season (fall and winter) and especially in the inner region. Although UND dominates the error budget, underestimation of heavy rainfall conditions accounts for less than 20% of the total, consistent with regional hydrometeorology. The 2A25 V7 product underestimates low-level orographic enhancement of rainfall associated with fog, cap clouds and cloud to cloud feeder-seeder interactions over ridges, and overestimates light rainfall in the valleys by large amounts, though this

Light scattering application for quantitative estimation of apoptosis

NASA Astrophysics Data System (ADS)

Bilyy, Rostyslav O.; Stoika, Rostyslav S.; Getman, Vasyl B.; Bilyi, Olexander I.

2004-05-01

Estimation of cell proliferation and apoptosis are in focus of instrumental methods used in modern biomedical sciences. Present study concerns monitoring of functional state of cells, specifically the development of their programmed death or apoptosis. The available methods for such purpose are either very expensive, or require time-consuming operations. Their specificity and sensitivity are frequently not sufficient for making conclusions which could be used in diagnostics or treatment monitoring. We propose a novel method for apoptosis measurement based on quantitative determination of cellular functional state taking into account their physical characteristics. This method uses the patented device -- laser microparticle analyser PRM-6 -- for analyzing light scattering by the microparticles, including cells. The method gives an opportunity for quick, quantitative, simple (without complicated preliminary cell processing) and relatively cheap measurement of apoptosis in cellular population. The elaborated method was used for studying apoptosis expression in murine leukemia cells of L1210 line and human lymphoblastic leukemia cells of K562 line. The results obtained by the proposed method permitted measuring cell number in tested sample, detecting and quantitative characterization of functional state of cells, particularly measuring the ratio of the apoptotic cells in suspension.

Probabilistic estimates of drought impacts on agricultural production

NASA Astrophysics Data System (ADS)

Madadgar, Shahrbanou; AghaKouchak, Amir; Farahmand, Alireza; Davis, Steven J.

2017-08-01

Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980-2012, including the Millennium Drought in Australia (2001-2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25-45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops.

Verifying Diurnal Variations of Global Precipitation in Three New Global Reanalyses

NASA Astrophysics Data System (ADS)

Wu, S.; Xie, P.; Sun, F.; Joyce, R.

2013-12-01

Diurnal variations of global precipitation and their representation in three sets of new generation global reanalyses are examined using the reprocessed and bias corrected CMORPH satellite precipitation estimates. The CMORPH satellite precipitation estimates are produced on an 8km by 8km grid over the globe (60oS-60oN) and in a 30-min interval covering a 15-year period from 1998 to the present through combining information from IR and PMW observations from all available satellites. Bias correction is performed for the raw CMORPH precipitation estimates through calibration against an gauge-based analysis over land and against the pentad GPCP analysis over ocean. The reanalyses examined here include the NCEP CFS reanalysis (CFSR), NASA/GSFC MERRA, and ECMWF Interim. The bias-corrected CMORPH is integrated from its original resolution to the reanalyses grid systems to facilitate the verification. First, quantitative agreements between the reanalysis precipitation fields and the CMORPH satellite observation are examined over the global domain. Precipitation structures associated with the large-scale topography are well reproduced when compared against the observation. Evolution of precipitation patterns with the development of transient weather systems are captured by the CFSR and two other reanalyses. The reanalyses tend to generate precipitation fields with wider raining areas and reduced intensity for heavy rainfall cases compared the observations over both land and ocean. Seasonal migration of global precipitation depicted in the 15-year CMORPH satellite observations is very well captured by the three sets of new reanalyses, although magnitude of precipitation is larger, especially in the CFSR, compared to that in the observations. In general, the three sets of new reanalyses exhibit substantial improvements in their performance to represent global precipitation distributions and variations. In particular, the new reanalyses produced precipitation variations of

CO-PRECIPITATION IN QUANTITATIVE ANALYSIS. COMMUNICATION V. THE INFLUENCE EXERCISED BY COMPLEXION UPON THE PRECIPITATION OF ZIRCONIUM PHOSPHATE

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

Babko, A.K.; Shtokalo, M.I.

The influence exercised by ethylenediamino-tetraacetic acid upon some processes of precipitation was investigated. A sharp mopdification of the form of precipitate as well as a decrease of coprecipitation was ium and titanium by means of the phosphate ;method are given. (TCO-W.D.M.)

Analysis of clouds and precipitation during Baiu period over the East China Sea with cloud database CTOP and precipitation database GSMaP

NASA Astrophysics Data System (ADS)

Nishi, N.; Hamada, A.; Hirose, H.; Hotta, S.; Suzuki, J.

2016-12-01

We have made a quantitative research of the clouds and precipitation during Baiu: the rainy season within the East Asia, using recent satellite observation datasets. As the precipitation dataset, we utilized the Global Satellite Mapping of Precipitation (GSMaP), whose primary source is passive microwave observations. As the cloud dataset, we used our original database CTOP, in which the cloud top height and optical depth are estimated only with the infrared split-window channels of the geostationary satellites. Lookup tables are made by training the infrared observations with the direct cloud observation by CloudSat and CALIPSO. This technique was originally developed only for the tropics but we extended it to the mid-latitude by estimating temperature at the cloud top instead of the height. We analyzed the properties of northward shift of the Baiu precipitation zone over the East China Sea. Abrupt northward shift in mid-June has already been reported. We showed here that the abrupt shift is limited to the western half of the East China Sea. We also analyzed the zonal difference of the precipitation amount in the East China Sea. In the central latitudinal range (30-33N), the amount is larger in the eastern part of the sea. There is no significant zonal contrast in both the activity of the low pressure and the front, while the sea surface temperature in the eastern part is slightly larger than in the western part. The zonal gradient is much smaller than that in the southern region near the Kuroshio Current, but may possibly affect the zonal contrast of the precipitation. By using CTOP cloud top data, we also calculated the occurrence ratio of the cloud with various thresholds of the top height. The ratio of clouds with the tops higher than 12 km in the East China Sea is clearly lower than those over the Continental area and the main Japanese islands.

a 33GHZ and 95GHZ Cloud Profiling Radar System (cprs): Preliminary Estimates of Particle Size in Precipitation and Clouds.

NASA Astrophysics Data System (ADS)

Sekelsky, Stephen Michael

1995-11-01

The Microwave Remote Sensing Laboratory (MIRSL) st the University of Massachusetts has developed a unique single antenna, dual-frequency polarimetric Cloud Profiling Radar System (CPRS). This project was funded by the Department of Energy's Atmospheric Radiation Measurement (ARM) program, and was intended to help fill the void of ground-based remote sensors capable of characterizing cloud microphysical properties. CPRS is unique in that it can simultaneously measure the complex power backscattered from clouds at 33 GHz and 95 GHz through the same aperture. Both the 33 GHz and 95 GHz channels can transmit pulse-to-pulse selectable vertical or horizontal polarization, and simultaneously record both the copolarized and crosspolarized backscatter. CPRS Doppler, polarimetric and dual-wavelength reflectivity measurements combined with in situ cloud measurements should lead to the development of empirical models that can more accurately classify cloud-particle phase and habit, and make better quantitative estimates of particle size distribution parameters. This dissertation describes the CPRS hardware, and presents colocated 33 GHz and 95 GHz measurements that illustrate the use of dual-frequency measurements to estimate particle size when Mie scattering, is observed in backscatter from rain and ice-phase clouds. Polarimetric measurements are presented as a means of discriminating cloud phase (ice-water) and estimating crystal shape in cirrus clouds. Polarimetric and dual-wavelength observations of insects are also presented with a brief discussion of their impact on the interpretation of precipitation and liquid cloud measurements. In precipitation, Diermendjian's equations for Mie backscatter (1) and the Marshal-Palmer drop-size distribution are used to develop models relating differences in the reflectivity and mean velocity at 33 GHz and 95 GHz to the microphysical parameters of rain. These models are then used to estimate mean droplet size from CPRS measurements of

Deuterium excess in precipitation of Alpine regions - moisture recycling.

PubMed

Froehlich, Klaus; Kralik, Martin; Papesch, Wolfgang; Rank, Dieter; Scheifinger, Helfried; Stichler, Willibald

2008-03-01

The paper evaluates long-term seasonal variations of the deuterium excess (d-excess = delta(2)H - 8. delta(18)O) in precipitation of stations located north and south of the main ridge of the Austrian Alps. It demonstrates that sub-cloud evaporation during precipitation and continental moisture recycling are local, respectively, regional processes controlling these variations. In general, sub-cloud evaporation decreases and moisture recycling increases the d-excess. Therefore, evaluation of d-excess variations in terms of moisture recycling, the main aim of this paper, includes determination of the effect of sub-cloud evaporation. Since sub-cloud evaporation is governed by saturation deficit and distance between cloud base and the ground, its effect on the d-excess is expected to be lower at mountain than at lowland/valley stations. To determine quantitatively this difference, we examined long-term seasonal d-excess variations measured at three selected mountain and adjoining valley stations. The altitude differences between mountain and valley stations ranged from 470 to 1665 m. Adapting the 'falling water drop' model by Stewart [J. Geophys. Res., 80(9), 1133-1146 (1975).], we estimated that the long-term average of sub-cloud evaporation at the selected mountain stations (altitudes between about 1600 and 2250 m.a.s.l.) is less than 1 % of the precipitation and causes a decrease of the d-excess of less than 2 per thousand. For the selected valley stations, the corresponding evaporated fraction is at maximum 7 % and the difference in d-excess ranges up to 8 per thousand. The estimated d-excess differences have been used to correct the measured long-term d-excess values at the selected stations. Finally, the corresponding fraction of water vapour has been estimated that recycled by evaporation of surface water including soil water from the ground. For the two mountain stations Patscherkofel and Feuerkogel, which are located north of the main ridge of the Alps, the

Spatial distribution of precipitation extremes in Norway

NASA Astrophysics Data System (ADS)

Verpe Dyrrdal, Anita; Skaugen, Thomas; Lenkoski, Alex; Thorarinsdottir, Thordis; Stordal, Frode; Førland, Eirik J.

2015-04-01

Estimates of extreme precipitation, in terms of return levels, are crucial in planning and design of important infrastructure. Through two separate studies, we have examined the levels and spatial distribution of daily extreme precipitation over catchments in Norway, and hourly extreme precipitation in a point. The analyses were carried out through the development of two new methods for estimating extreme precipitation in Norway. For daily precipitation we fit the Generalized Extreme Value (GEV) distribution to areal time series from a gridded dataset, consisting of daily precipitation during the period 1957-today with a resolution of 1x1 km². This grid-based method is more objective and less manual and time-consuming compared to the existing method at MET Norway. In addition, estimates in ungauged catchments are easier to obtain, and the GEV approach includes a measure of uncertainty, which is a requirement in climate studies today. Further, we go into depth on the debated GEV shape parameter, which plays an important role for longer return periods. We show that it varies according to dominating precipitation types, having positive values in the southeast and negative values in the southwest. We also find indications that the degree of orographic enhancement might affect the shape parameter. For hourly precipitation, we estimate return levels on a 1x1 km² grid, by linking GEV distributions with latent Gaussian fields in a Bayesian hierarchical model (BHM). Generalized linear models on the GEV parameters, estimated from observations, are able to incorporate location-specific geographic and meteorological information and thereby accommodate these effects on extreme precipitation. Gaussian fields capture additional unexplained spatial heterogeneity and overcome the sparse grid on which observations are collected, while a Bayesian model averaging component directly assesses model uncertainty. We find that mean summer precipitation, mean summer temperature, latitude

Quantitative diagnosis of moisture sources and transport pathways for summer precipitation over the mid-lower Yangtze River Basin

NASA Astrophysics Data System (ADS)

Wang, Ning; Zeng, Xin-Min; Guo, Wei-Dong; Chen, Chaohui; You, Wei; Zheng, Yiqun; Zhu, Jian

2018-04-01

Using a moisture tracking model with 32-year reanalysis data and station precipitation observations, we diagnosed the sources of moisture for summer (June 1-August 31) precipitation in mid-lower reaches of the Yangtze River Basin (YRB). Results indicate the dominant role of oceanic evaporation compared to terrestrial evapotranspiration, and the previously overlooked southern Indian Ocean, as a source region, is found to contribute more moisture than the well-known Arabian Sea or Bay of Bengal. Terrestrial evapotranspiration appears to be important for summer precipitation, especially in early June when moisture contribution is more than 50%. The terrestrial contribution then decreases and is generally less than 40% after late June. The Indian Ocean is the most important oceanic source before mid-July, with its largest contribution during the period of heavy precipitation, while the Pacific Ocean becomes the more important oceanic source after mid-July. To quantitatively analyze paths of moisture transport to YRB, we proposed the Trajectory Frequency Method. The most intense branch of water vapor transport to YRB stretches from the Arabian Sea through the Bay of Bengal, the Indochina Peninsula, the South China Sea, and South China. The other main transport branches are westerly moisture fluxes to the south of the Tibetan Plateau, cross-equatorial flows north of Australia, and separate branches located in the north and equatorial Pacific Ocean. Significant intraseasonal variability for these branches is presented. Additionally, the importance of the South China Sea for moisture transport to YRB, especially from the sea areas, is emphasized.

Precipitation and Diabatic Heating Distributions from TRMM/GPM

NASA Astrophysics Data System (ADS)

Olson, W. S.; Grecu, M.; Wu, D.; Tao, W. K.; L'Ecuyer, T.; Jiang, X.

2016-12-01

The initial focus of our research effort was the development of a physically-based methodology for estimating 3D precipitation distributions from a combination of spaceborne radar and passive microwave radiometer observations. This estimation methodology was originally developed for applications to Global Precipitation Measurement (GPM) mission sensor data, but it has recently been adapted to Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and Microwave Imager observations. Precipitation distributions derived from the TRMM sensors are interpreted using cloud-system resolving model simulations to infer atmospheric latent+eddy heating (Q1-QR) distributions in the tropics and subtropics. Further, the estimates of Q1-QR are combined with estimates of radiative heating (QR), derived from TRMM Microwave Imager and Visible and Infrared Scanner data as well as environmental properties from NCEP reanalyses, to yield estimates of the large-scale total diabatic heating (Q1). A thirteen-year database of precipitation and diabatic heating is constructed using TRMM observations from 1998-2010 as part of NASA's Energy and Water cycle Study program. State-dependent errors in precipitation and heating products are evaluated by propagating the potential errors of a priori modeling assumptions through the estimation method framework. Knowledge of these errors is critical for determining the "closure" of global water and energy budgets. Applications of the precipitation/heating products to climate studies will be presented at the conference.

Application of Multilayer Feedforward Neural Networks to Precipitation Cell-Top Altitude Estimation

NASA Technical Reports Server (NTRS)

Spina, Michelle S.; Schwartz, Michael J.; Staelin, David H.; Gasiewski, Albin J.

1998-01-01

The use of passive 118-GHz O2 observations of rain cells for precipitation cell-top altitude estimation is demonstrated by using a multilayer feed forward neural network retrieval system. Rain cell observations at 118 GHz were compared with estimates of the cell-top altitude obtained by optical stereoscopy. The observations were made with 2 4 km horizontal spatial resolution by using the Millimeter-wave Temperature Sounder (MTS) scanning spectrometer aboard the NASA ER-2 research aircraft during the Genesis of Atlantic Lows Experiment (GALE) and the COoperative Huntsville Meteorological EXperiment (COHMEX) in 1986. The neural network estimator applied to MTS spectral differences between clouds, and nearby clear air yielded an rms discrepancy of 1.76 km for a combined cumulus, mature, and dissipating cell set and 1.44 km for the cumulus-only set. An improvement in rms discrepancy to 1.36 km was achieved by including additional MTS information on the absolute atmospheric temperature profile. An incremental method for training neural networks was developed that yielded robust results, despite the use of as few as 56 training spectra. Comparison of these results with a nonlinear statistical estimator shows that superior results can be obtained with a neural network retrieval system. Imagery of estimated cell-top altitudes was created from 118-GHz spectral imagery gathered from CAMEX, September through October 1993, and from cyclone Oliver, February 7, 1993.

Discussion of band selection and methodologies for the estimation of precipitable water vapour from AVIRIS data

NASA Technical Reports Server (NTRS)

Schanzer, Dena; Staenz, Karl

1992-01-01

An Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data set acquired over Canal Flats, B.C., on 14 Aug. 1990, was used for the purpose of developing methodologies for surface reflectance retrieval using the 5S atmospheric code. A scene of Rogers Dry Lake, California (23 Jul. 1990), acquired within three weeks of the Canal Flats scene, was used as a potential reference for radiometric calibration purposes and for comparison with other studies using primarily LOWTRAN7. Previous attempts at surface reflectance retrieval indicated that reflectance values in the gaseous absorption bands had the poorest accuracy. Modifications to 5S to use 1 nm step size, in order to make fuller use of the 20 cm(sup -1) resolution of the gaseous absorption data, resulted in some improvement in the accuracy of the retrieved surface reflectance. Estimates of precipitable water vapor using non-linear least squares regression and simple ratioing techniques such as the CIBR (Continuum Interpolated Band Ratio) technique or the narrow/wide technique, which relate ratios of combinations of bands to precipitable water vapor through calibration curves, were found to vary widely. The estimates depended on the bands used for the estimation; none provided entirely satisfactory surface reflectance curves.

Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign

DOE PAGES

Giangrande, Scott E.; Collis, Scott; Theisen, Adam K.; ...

2014-09-12

This study presents radar-based precipitation estimates collected during the two-month DOE ARM - NASA Midlatitude Continental Convective Clouds Experiment (MC3E). Emphasis is on the usefulness of radar observations from the C-band and X-band scanning ARM precipitation radars (CSAPR, XSAPR) for rainfall estimation products to distances within 100 km of the Oklahoma SGP facility. A dense collection of collocated ARM, NASA GPM and nearby surface Oklahoma Mesonet gauge records are consulted to evaluate potential ARM radar-based hourly rainfall products and campaign optimized methods over individual gauge and areal characterizations. Rainfall products are evaluated against the performance of the regional operational NWSmore » NEXRAD S-band radar polarimetric product. Results indicate that the ARM C-band system may achieve similar point and areal-gauge bias and root mean square (rms) error performance to the NEXRAD standard for the variety of MC3E deep convective events sampled when capitalizing on differential phase measurements. The best campaign rainfall performance was achieved when applying radar relations capitalizing on estimates of the specific attenuation from the CSAPR system. The ARM X-band systems only demonstrate solid capabilities as compared to NEXRAD standards for hourly point and areal rainfall accumulations under 10 mm. Here, all methods exhibit a factor of 1.5 to 2.5 reduction in rms errors for areal accumulations over a 15 km2 NASA dense network housing 16 sites having collocated bucket gauges, with the higher error reductions best associated with polarimetric methods.« less

Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign

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

Giangrande, Scott E.; Collis, Scott; Theisen, Adam K.

This study presents radar-based precipitation estimates collected during the two-month DOE ARM - NASA Midlatitude Continental Convective Clouds Experiment (MC3E). Emphasis is on the usefulness of radar observations from the C-band and X-band scanning ARM precipitation radars (CSAPR, XSAPR) for rainfall estimation products to distances within 100 km of the Oklahoma SGP facility. A dense collection of collocated ARM, NASA GPM and nearby surface Oklahoma Mesonet gauge records are consulted to evaluate potential ARM radar-based hourly rainfall products and campaign optimized methods over individual gauge and areal characterizations. Rainfall products are evaluated against the performance of the regional operational NWSmore » NEXRAD S-band radar polarimetric product. Results indicate that the ARM C-band system may achieve similar point and areal-gauge bias and root mean square (rms) error performance to the NEXRAD standard for the variety of MC3E deep convective events sampled when capitalizing on differential phase measurements. The best campaign rainfall performance was achieved when applying radar relations capitalizing on estimates of the specific attenuation from the CSAPR system. The ARM X-band systems only demonstrate solid capabilities as compared to NEXRAD standards for hourly point and areal rainfall accumulations under 10 mm. Here, all methods exhibit a factor of 1.5 to 2.5 reduction in rms errors for areal accumulations over a 15 km2 NASA dense network housing 16 sites having collocated bucket gauges, with the higher error reductions best associated with polarimetric methods.« less

Radar-based Quantitative Precipitation Forecasting using Spatial-scale Decomposition Method for Urban Flood Management

NASA Astrophysics Data System (ADS)

Yoon, S.; Lee, B.; Nakakita, E.; Lee, G.

2016-12-01

Recent climate changes and abnormal weather phenomena have resulted in increased occurrences of localized torrential rainfall. Urban areas in Korea have suffered from localized heavy rainfall, including the notable Seoul flood disaster in 2010 and 2011. The urban hydrological environment has changed in relation to precipitation, such as reduced concentration time, a decreased storage rate, and increased peak discharge. These changes have altered and accelerated the severity of damage to urban areas. In order to prevent such urban flash flood damages, we have to secure the lead time for evacuation through the improvement of radar-based quantitative precipitation forecasting (QPF). The purpose of this research is to improve the QPF products using spatial-scale decomposition method for considering the life time of storm and to assess the accuracy between traditional QPF method and proposed method in terms of urban flood management. The layout of this research is as below. First, this research applies the image filtering to separate the spatial-scale of rainfall field. Second, the separated small and large-scale rainfall fields are extrapolated by each different forecasting method. Third, forecasted rainfall fields are combined at each lead time. Finally, results of this method are evaluated and compared with the results of uniform advection model for urban flood modeling. It is expected that urban flood information using improved QPF will help to reduce casualties and property damage caused by urban flooding through this research.

RADIOMETRIC STUDY ON THE SUITABILITY OF CINCHONIN AS PRECIPITATION REAGENT FOR TUNGSTEN AND MOLYBDENUM (in German)

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

Merz, E.

1962-01-01

The precipitation of W and Mo with cinchonin was studied using P/sup 32/ , Mp/sup 99/, and W/sup 185/. The effects of the acid, acid concentration, solution volume, and precipitation time on the quantitative precipitation of the metals were determined. W was precipitated only from HCl and HNO/sub 3/, and no quantitative precipitation of Mo was obtained. No variation was found in the completeness of the precipitation using HCl concentrations of 1 to 6N, but precipitation is prevented or strongly inhibited in very strong solutions. The quantitative precipitation of W is very strongly dependent on the solution volume, but theremore » is no sharp time dependence. A study of the effectiveness of cinchonin and tannin as combined precipitation agents showed no advantage over the use of cinchonin alone, except in the removal of interferring elements in the precipitation. (J.S.R.)« less

Stroke onset time estimation from multispectral quantitative magnetic resonance imaging in a rat model of focal permanent cerebral ischemia.

PubMed

McGarry, Bryony L; Rogers, Harriet J; Knight, Michael J; Jokivarsi, Kimmo T; Sierra, Alejandra; Gröhn, Olli Hj; Kauppinen, Risto A

2016-08-01

Quantitative T2 relaxation magnetic resonance imaging allows estimation of stroke onset time. We aimed to examine the accuracy of quantitative T1 and quantitative T2 relaxation times alone and in combination to provide estimates of stroke onset time in a rat model of permanent focal cerebral ischemia and map the spatial distribution of elevated quantitative T1 and quantitative T2 to assess tissue status. Permanent middle cerebral artery occlusion was induced in Wistar rats. Animals were scanned at 9.4T for quantitative T1, quantitative T2, and Trace of Diffusion Tensor (Dav) up to 4 h post-middle cerebral artery occlusion. Time courses of differentials of quantitative T1 and quantitative T2 in ischemic and non-ischemic contralateral brain tissue (ΔT1, ΔT2) and volumes of tissue with elevated T1 and T2 relaxation times (f1, f2) were determined. TTC staining was used to highlight permanent ischemic damage. ΔT1, ΔT2, f1, f2, and the volume of tissue with both elevated quantitative T1 and quantitative T2 (V(Overlap)) increased with time post-middle cerebral artery occlusion allowing stroke onset time to be estimated. V(Overlap) provided the most accurate estimate with an uncertainty of ±25 min. At all times-points regions with elevated relaxation times were smaller than areas with Dav defined ischemia. Stroke onset time can be determined by quantitative T1 and quantitative T2 relaxation times and tissue volumes. Combining quantitative T1 and quantitative T2 provides the most accurate estimate and potentially identifies irreversibly damaged brain tissue. © 2016 World Stroke Organization.

Uncertainty Analysis of Radar and Gauge Rainfall Estimates in the Russian River Basin

NASA Astrophysics Data System (ADS)

Cifelli, R.; Chen, H.; Willie, D.; Reynolds, D.; Campbell, C.; Sukovich, E.

2013-12-01

Radar Quantitative Precipitation Estimation (QPE) has been a very important application of weather radar since it was introduced and made widely available after World War II. Although great progress has been made over the last two decades, it is still a challenging process especially in regions of complex terrain such as the western U.S. It is also extremely difficult to make direct use of radar precipitation data in quantitative hydrologic forecasting models. To improve the understanding of rainfall estimation and distributions in the NOAA Hydrometeorology Testbed in northern California (HMT-West), extensive evaluation of radar and gauge QPE products has been performed using a set of independent rain gauge data. This study focuses on the rainfall evaluation in the Russian River Basin. The statistical properties of the different gridded QPE products will be compared quantitatively. The main emphasis of this study will be on the analysis of uncertainties of the radar and gauge rainfall products that are subject to various sources of error. The spatial variation analysis of the radar estimates is performed by measuring the statistical distribution of the radar base data such as reflectivity and by the comparison with a rain gauge cluster. The application of mean field bias values to the radar rainfall data will also be described. The uncertainty analysis of the gauge rainfall will be focused on the comparison of traditional kriging and conditional bias penalized kriging (Seo 2012) methods. This comparison is performed with the retrospective Multisensor Precipitation Estimator (MPE) system installed at the NOAA Earth System Research Laboratory. The independent gauge set will again be used as the verification tool for the newly generated rainfall products.

Exploration of a Dynamic Merging Scheme for Precipitation Estimation over a Small Urban Catchment

NASA Astrophysics Data System (ADS)

Al-Azerji, Sherien; Rico-Ramirez, Miguel, ,, Dr.; Han, Dawei, ,, Prof.

2016-04-01

The accuracy of quantitative precipitation estimation is of significant importance for urban areas due to the potentially damaging consequences that can result from pluvial flooding. Improved accuracy could be accomplished by merging rain gauge measurements with weather radar data through different merging methods. Several factors may affect the accuracy of the merged data, and the gauge density used for merging is one of the most important. However, if there are no gauges inside the research area, then a gauge network outside the research area can be used for the merging. Generally speaking, the denser the rain gauge network is, the better the merging results that can be achieved. However, in practice, the rain gauge network around the research area is fixed, and the research question is about the optimal merging area. The hypothesis is that if the merging area is too small, there are fewer gauges for merging and thus the result would be poor. If the merging area is too large, gauges far away from the research area can be included in merging. However, due to their large distances, those gauges far away from the research area provide little relevant information to the study and may even introduce noise in merging. Therefore, an optimal merging area that produces the best merged rainfall estimation in the research area could exist. To test this hypothesis, the distance from the centre of the research area and the number of merging gauges around the research area were gradually increased and merging with a new domain of radar data was then performed. The performance of the new merging scheme was compared with a gridded interpolated rainfall from four experimental rain gauges installed inside the research area for validation. The result of this analysis shows that there is indeed an optimum distance from the centre of research area and consequently an optimum number of rain gauges that produce the best merged rainfall data inside the research area. This study is of

Where Does the Irrigation Water Go? An Estimate of the Contribution of Irrigation to Precipitation Using MERRA

NASA Technical Reports Server (NTRS)

Wei, Jiangfeng; Dirmeyer, Paul A.; Wisser, Dominik; Bosilovich, Michael G.; Mocko, David M.

2013-01-01

Irrigation is an important human activity that may impact local and regional climate, but current climate model simulations and data assimilation systems generally do not explicitly include it. The European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) shows more irrigation signal in surface evapotranspiration (ET) than the Modern-Era Retrospective Analysis for Research and Applications (MERRA) because ERA-Interim adjusts soil moisture according to the observed surface temperature and humidity while MERRA has no explicit consideration of irrigation at the surface. But, when compared with the results from a hydrological model with detailed considerations of agriculture, the ET from both reanalyses show large deficiencies in capturing the impact of irrigation. Here, a back-trajectory method is used to estimate the contribution of irrigation to precipitation over local and surrounding regions, using MERRA with observation-based corrections and added irrigation-caused ET increase from the hydrological model. Results show substantial contributions of irrigation to precipitation over heavily irrigated regions in Asia, but the precipitation increase is much less than the ET increase over most areas, indicating that irrigation could lead to water deficits over these regions. For the same increase in ET, precipitation increases are larger over wetter areas where convection is more easily triggered, but the percentage increase in precipitation is similar for different areas. There are substantial regional differences in the patterns of irrigation impact, but, for all the studied regions, the highest percentage contribution to precipitation is over local land.

Accurate and quantitative polarization-sensitive OCT by unbiased birefringence estimator with noise-stochastic correction

NASA Astrophysics Data System (ADS)

Kasaragod, Deepa; Sugiyama, Satoshi; Ikuno, Yasushi; Alonso-Caneiro, David; Yamanari, Masahiro; Fukuda, Shinichi; Oshika, Tetsuro; Hong, Young-Joo; Li, En; Makita, Shuichi; Miura, Masahiro; Yasuno, Yoshiaki

2016-03-01

Polarization sensitive optical coherence tomography (PS-OCT) is a functional extension of OCT that contrasts the polarization properties of tissues. It has been applied to ophthalmology, cardiology, etc. Proper quantitative imaging is required for a widespread clinical utility. However, the conventional method of averaging to improve the signal to noise ratio (SNR) and the contrast of the phase retardation (or birefringence) images introduce a noise bias offset from the true value. This bias reduces the effectiveness of birefringence contrast for a quantitative study. Although coherent averaging of Jones matrix tomography has been widely utilized and has improved the image quality, the fundamental limitation of nonlinear dependency of phase retardation and birefringence to the SNR was not overcome. So the birefringence obtained by PS-OCT was still not accurate for a quantitative imaging. The nonlinear effect of SNR to phase retardation and birefringence measurement was previously formulated in detail for a Jones matrix OCT (JM-OCT) [1]. Based on this, we had developed a maximum a-posteriori (MAP) estimator and quantitative birefringence imaging was demonstrated [2]. However, this first version of estimator had a theoretical shortcoming. It did not take into account the stochastic nature of SNR of OCT signal. In this paper, we present an improved version of the MAP estimator which takes into account the stochastic property of SNR. This estimator uses a probability distribution function (PDF) of true local retardation, which is proportional to birefringence, under a specific set of measurements of the birefringence and SNR. The PDF was pre-computed by a Monte-Carlo (MC) simulation based on the mathematical model of JM-OCT before the measurement. A comparison between this new MAP estimator, our previous MAP estimator [2], and the standard mean estimator is presented. The comparisons are performed both by numerical simulation and in vivo measurements of anterior and

Using Kriging with a heterogeneous measurement error to improve the accuracy of extreme precipitation return level estimation

NASA Astrophysics Data System (ADS)

Yin, Shui-qing; Wang, Zhonglei; Zhu, Zhengyuan; Zou, Xu-kai; Wang, Wen-ting

2018-07-01

Extreme precipitation can cause flooding and may result in great economic losses and deaths. The return level is a commonly used measure of extreme precipitation events and is required for hydrological engineer designs, including those of sewerage systems, dams, reservoirs and bridges. In this paper, we propose a two-step method to estimate the return level and its uncertainty for a study region. In the first step, we use the generalized extreme value distribution, the L-moment method and the stationary bootstrap to estimate the return level and its uncertainty at the site with observations. In the second step, a spatial model incorporating the heterogeneous measurement errors and covariates is trained to estimate return levels at sites with no observations and to improve the estimates at sites with limited information. The proposed method is applied to the daily rainfall data from 273 weather stations in the Haihe river basin of North China. We compare the proposed method with two alternatives: the first one is based on the ordinary Kriging method without measurement error, and the second one smooths the estimated location and scale parameters of the generalized extreme value distribution by the universal Kriging method. Results show that the proposed method outperforms its counterparts. We also propose a novel approach to assess the two-step method by comparing it with the at-site estimation method with a series of reduced length of observations. Estimates of the 2-, 5-, 10-, 20-, 50- and 100-year return level maps and the corresponding uncertainties are provided for the Haihe river basin, and a comparison with those released by the Hydrology Bureau of Ministry of Water Resources of China is made.

(In)Consistent estimates of changes in relative precipitation in an European domain over the last 350 years

NASA Astrophysics Data System (ADS)

Bothe, Oliver; Wagner, Sebastian; Zorita, Eduardo

2015-04-01

How did regional precipitation change in past centuries? We have potentially three sources of information to answer this question: There are, especially for Europe, a number of long records of local station precipitation; documentary records and natural archives of past environmental variability serve as proxy records for empirical reconstructions; in addition, simulations with coupled climate models or Earth System Models provide estimates on the spatial structure of precipitation variability. However, instrumental records rarely extend back to the 18th century, reconstructions include large uncertainties, and simulation skill is often still unsatisfactory for precipitation. Thus, we can only seek to answer to which extent the three sources provide a consistent picture of past regional precipitation changes. This presentation describes the (lack of) consistency in describing changes of the distributional properties of seasonal precipitation between the different data sources. We concentrate on England and Wales since there are two recent reconstructions and a long observation based record available for this domain. The season of interest is an extended spring (March, April, May, June, July, MAMJJ) over the past 350 years. The main simulated data stem from a regional simulation for the European domain with CCLM driven at its lateral boundaries with conditions provided by a MPI-ESM COSMOS simulation for the last millennium using a high-amplitude solar forcing. A number of simulations for the past 1000 years from the Paleoclimate Modelling Intercomparison Project Phase III provide additional information. We fit a Weibull distribution to the available data sets following the approach for calculating standardized precipitation indices. We do so over 51 year moving windows to assess the consistency of changes in the distributional properties. Changes in the percentiles for severe (and extreme) dry or wet conditions and in the Weibull standard deviations of precipitation

Global Precipitation Measurement: Methods, Datasets and Applications

NASA Technical Reports Server (NTRS)

Tapiador, Francisco; Turk, Francis J.; Petersen, Walt; Hou, Arthur Y.; Garcia-Ortega, Eduardo; Machado, Luiz, A. T.; Angelis, Carlos F.; Salio, Paola; Kidd, Chris; Huffman, George J.;

How Well the Early 2017 California Atmospheric River Precipitation Events Were Captured by Satellite Products and Ground-based Radars?

NASA Astrophysics Data System (ADS)

Wen, Y. B.; Behrangi, A.; Chen, H.; Lambrigtsen, B.

2017-12-01

In January and February of 2017, California experienced multiple heavy storms that caused serious destruction of facilities and economic loss, although it also helped to reduce water storage deficit due to prolonged drought in previous years. These extreme precipitation events were mainly associated with Atmospheric Rivers (ARs) and brought about 174 km3 of water to California according to ground observations. This paper evaluates the performance of six commonly used satellite-based precipitation products (IMERG, 3B42RT, PERSIANN, CCS, CMORPH, and GSMaP), as well as ground-based radar products (Radar-only and Radar-lgc) in capturing the ARs precipitation rate and distribution. It is found that precipitation maps from all products present heavy precipitation in January and February, with more consistent observations over ocean than land. Though large uncertainties exist in quantitative precipitation estimation (QPE) over land, the ensemble mean of different remote sensing precipitation products over California is consistent with gauge measurements. Among the six satellite-based products, IMERG correlates the best with gauge observations both in the detection and quantification of precipitation, but it is not the best product in terms of root mean square error (RMSE) or bias. Compared to satellite products, ground weather radar shows better precipitation detectability and estimation skill. However, neither radar nor satellite QPE products have good performances in quantifying the peak precipitation intensity during the extreme events, suggesting that further advancement in quantification of extremely intense precipitation associated with AR in the Western United States is needed.

Merging bottom-up and top-down precipitation products using a stochastic error model

NASA Astrophysics Data System (ADS)

Maggioni, Viviana; Massari, Christian; Brocca, Luca; Ciabatta, Luca

2017-04-01

Accurate quantitative precipitation estimation is of great importance for water resources management, agricultural planning, and forecasting and monitoring of natural hazards such as flash floods and landslides. In situ observations are limited around the Earth, especially in remote areas (e.g., complex terrain, dense vegetation), but currently available satellite precipitation products are able to provide global precipitation estimates with an accuracy that depends upon many factors (e.g., type of storms, temporal sampling, season etc…). Recently, Brocca et al. (2014) have proposed an alternative approach (i.e., SM2RAIN) that allows to estimate rainfall from space by using satellite soil moisture observations. In contrast with classical satellite precipitation products which sense the cloud properties to retrieve the instantaneous precipitation, this new bottom-up approach makes use of two consecutive soil moisture measurements for obtaining an estimate of the fallen precipitation within the interval between two satellite passes. As a result, the nature of the measurement is different and complementary to the one of classical precipitation products and could provide a different valid perspective to improve current satellite rainfall estimates via appropriate integration between the products (i.e., SM2RAIN plus a classical satellite rainfall product). However, whether SM2RAIN is able or not to improve the performance of any state-of-the-art satellite rainfall product is much dependent upon an adequate quantification and characterization of the relative errors of the products. In this study, the stochastic rainfall error model SREM2D (Hossain et al. 2006) is used for characterizing the retrieval error of both SM2RAIN and a state-of-the-art satellite precipitation product (i.e., 3B42RT). The error characterization serves for an optimal integration between SM2RAIN and 3B42RT for enhancing the capability of the resulting integrated product (i.e. SM2RAIN+3B42RT) in

Extreme Precipitation and High-Impact Landslides

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia; Adler, Robert; Huffman, George; Peters-Lidard, Christa

2012-01-01

It is well known that extreme or prolonged rainfall is the dominant trigger of landslides; however, there remain large uncertainties in characterizing the distribution of these hazards and meteorological triggers at the global scale. Researchers have evaluated the spatiotemporal distribution of extreme rainfall and landslides at local and regional scale primarily using in situ data, yet few studies have mapped rainfall-triggered landslide distribution globally due to the dearth of landslide data and consistent precipitation information. This research uses a newly developed Global Landslide Catalog (GLC) and a 13-year satellite-based precipitation record from Tropical Rainfall Measuring Mission (TRMM) data. For the first time, these two unique products provide the foundation to quantitatively evaluate the co-occurence of precipitation and rainfall-triggered landslides globally. The GLC, available from 2007 to the present, contains information on reported rainfall-triggered landslide events around the world using online media reports, disaster databases, etc. When evaluating this database, we observed that 2010 had a large number of high-impact landslide events relative to previous years. This study considers how variations in extreme and prolonged satellite-based rainfall are related to the distribution of landslides over the same time scales for three active landslide areas: Central America, the Himalayan Arc, and central-eastern China. Several test statistics confirm that TRMM rainfall generally scales with the observed increase in landslide reports and fatal events for 2010 and previous years over each region. These findings suggest that the co-occurrence of satellite precipitation and landslide reports may serve as a valuable indicator for characterizing the spatiotemporal distribution of landslide-prone areas in order to establish a global rainfall-triggered landslide climatology. This research also considers the sources for this extreme rainfall, citing

Estimating Watershed-Averaged Precipitation and Evapotranspiration Fluxes using Streamflow Measurements in a Semi-Arid, High Altitude Montane Catchment

NASA Astrophysics Data System (ADS)

Herrington, C.; Gonzalez-Pinzon, R.

2014-12-01

Streamflow through the Middle Rio Grande Valley is largely driven by snowmelt pulses and monsoonal precipitation events originating in the mountain highlands of New Mexico (NM) and Colorado. Water managers rely on results from storage/runoff models to distribute this resource statewide and to allocate compact deliveries to Texas under the Rio Grande Compact agreement. Prevalent drought conditions and the added uncertainty of climate change effects in the American southwest have led to a greater call for accuracy in storage model parameter inputs. While precipitation and evapotranspiration measurements are subject to scaling and representativeness errors, streamflow readings remain relatively dependable and allow watershed-average water budget estimates. Our study seeks to show that by "Doing Hydrology Backwards" we can effectively estimate watershed-average precipitation and evapotranspiration fluxes in semi-arid landscapes of NM using fluctuations in streamflow data alone. We tested this method in the Valles Caldera National Preserve (VCNP) in the Jemez Mountains of central NM. This method will be further verified by using existing weather stations and eddy-covariance towers within the VCNP to obtain measured values to compare against our model results. This study contributes to further validate this technique as being successful in humid and semi-arid catchments as the method has already been verified as effective in the former setting.

Spatial reconstruction of semi-quantitative precipitation fields over Africa during the nineteenth century from documentary evidence and gauge data

NASA Astrophysics Data System (ADS)

Nicholson, Sharon E.; Klotter, Douglas; Dezfuli, Amin K.

2012-07-01

The article presents a newly created precipitation data set for the African continent and describes the methodology used in its creation. It is based on a combination of proxy data and rain gauge records. The data set is semi-quantitative, with a "wetness" index of - 3 to + 3 to describe the quality of the rainy season. It covers the period AD 1801 to 1900 and includes data for 90 geographical regions of the continent. The results underscore a multi-decadal period of aridity early in the nineteenth century.

Advances in Satellite Microwave Precipitation Retrieval Algorithms Over Land

NASA Astrophysics Data System (ADS)

Wang, N. Y.; You, Y.; Ferraro, R. R.

2015-12-01

Precipitation plays a key role in the earth's climate system, particularly in the aspect of its water and energy balance. Satellite microwave (MW) observations of precipitation provide a viable mean to achieve global measurement of precipitation with sufficient sampling density and accuracy. However, accurate precipitation information over land from satellite MW is a challenging problem. The Goddard Profiling Algorithm (GPROF) algorithm for the Global Precipitation Measurement (GPM) is built around the Bayesian formulation (Evans et al., 1995; Kummerow et al., 1996). GPROF uses the likelihood function and the prior probability distribution function to calculate the expected value of precipitation rate, given the observed brightness temperatures. It is particularly convenient to draw samples from a prior PDF from a predefined database of observations or models. GPROF algorithm does not search all database entries but only the subset thought to correspond to the actual observation. The GPM GPROF V1 database focuses on stratification by surface emissivity class, land surface temperature and total precipitable water. However, there is much uncertainty as to what is the optimal information needed to subset the database for different conditions. To this end, we conduct a database stratification study of using National Mosaic and Multi-Sensor Quantitative Precipitation Estimation, Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Technology Microwave Sounder (ATMS) and reanalysis data from Modern-Era Retrospective Analysis for Research and Applications (MERRA). Our database study (You et al., 2015) shows that environmental factors such as surface elevation, relative humidity, and storm vertical structure and height, and ice thickness can help in stratifying a single large database to smaller and more homogeneous subsets, in which the surface condition and precipitation vertical profiles are similar. It is found that the probability of detection (POD) increases

The influence of annual precipitation, topography, and vegetative cover on soil moisture and summer drought in southern California.

PubMed

Miller, P C; Poole, D K

1983-02-01

The influence of annual precipitation and vegetation cover on soil moisture and on the length of the summer drought was estimated quantitatively using 9 years of soil moisture data collected at Echo Valley in southern California. The measurements support the conclusions that in the semi-arid mediterranean climate a soil drought will occur regardless of vegetation cover and annual precipitation, but the length of the drought is greatly dependent on soil depth and rockiness. Evergreen species which can survive this drought tend to accentuate the drought, especially in deep soil levels, by developing a canopy with a large transpiring surface.

Characterization of the evolution of the volume fraction of precipitates in aged AlMgSiCu alloys using DSC technique

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

Esmaeili, Shahrzad; Lloyd, David J.

2005-11-15

Differential scanning calorimetry is used to quantify the evolution of the volume fraction of precipitates during age hardening in AlMgSiCu alloys. The calorimetry tests are run on alloy samples after aging for various times at 180 deg. C and the change in the collective heat effects from the major precipitation and dissolution processes in each run are used to determine the precipitation state of the samples. The method is implemented on alloys with various thermal histories prior to artificial aging, including commercial pre-aging histories. The estimated values for the relative volume fraction of precipitates are compared with the results frommore » a newly developed analytical method using isothermal calorimetry and a related quantitative transmission electron microscopy work. Excellent agreement is obtained between the results from various methods.« less

Precipitation Data Merging over Mountainous Areas Using Satellite Estimates and Sparse Gauge Observations (PDMMA-USESGO) for Hydrological Modeling — A Case Study over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Yang, Z.; Hsu, K. L.; Sorooshian, S.; Xu, X.

2017-12-01

Precipitation in mountain regions generally occurs with high-frequency-intensity, whereas it is not well-captured by sparsely distributed rain-gauges imposing a great challenge on water management. Satellite-based Precipitation Estimation (SPE) provides global high-resolution alternative data for hydro-climatic studies, but are subject to considerable biases. In this study, a model named PDMMA-USESGO for Precipitation Data Merging over Mountainous Areas Using Satellite Estimates and Sparse Gauge Observations is developed to support precipitation mapping and hydrological modeling in mountainous catchments. The PDMMA-USESGO framework includes two calculating steps—adjusting SPE biases and merging satellite-gauge estimates—using the quantile mapping approach, a two-dimensional Gaussian weighting scheme (considering elevation effect), and an inverse root mean square error weighting method. The model is applied and evaluated over the Tibetan Plateau (TP) with the PERSIANN-CCS precipitation retrievals (daily, 0.04°×0.04°) and sparse observations from 89 gauges, for the 11-yr period of 2003-2013. To assess the data merging effects on streamflow modeling, a hydrological evaluation is conducted over a watershed in southeast TP based on the Soil and Water Assessment Tool (SWAT). Evaluation results indicate effectiveness of the model in generating high-resolution-accuracy precipitation estimates over mountainous terrain, with the merged estimates (Mer-SG) presenting consistently improved correlation coefficients, root mean square errors and absolute mean biases from original satellite estimates (Ori-CCS). It is found the Mer-SG forced streamflow simulations exhibit great improvements from those simulations using Ori-CCS, with coefficient of determination (R2) and Nash-Sutcliffe efficiency reach to 0.8 and 0.65, respectively. The presented model and case study serve as valuable references for the hydro-climatic applications using remote sensing-gauge information in

Dual respiratory and cardiac motion estimation in PET imaging: Methods design and quantitative evaluation.

PubMed

Feng, Tao; Wang, Jizhe; Tsui, Benjamin M W

2018-04-01

The goal of this study was to develop and evaluate four post-reconstruction respiratory and cardiac (R&C) motion vector field (MVF) estimation methods for cardiac 4D PET data. In Method 1, the dual R&C motions were estimated directly from the dual R&C gated images. In Method 2, respiratory motion (RM) and cardiac motion (CM) were separately estimated from the respiratory gated only and cardiac gated only images. The effects of RM on CM estimation were modeled in Method 3 by applying an image-based RM correction on the cardiac gated images before CM estimation, the effects of CM on RM estimation were neglected. Method 4 iteratively models the mutual effects of RM and CM during dual R&C motion estimations. Realistic simulation data were generated for quantitative evaluation of four methods. Almost noise-free PET projection data were generated from the 4D XCAT phantom with realistic R&C MVF using Monte Carlo simulation. Poisson noise was added to the scaled projection data to generate additional datasets of two more different noise levels. All the projection data were reconstructed using a 4D image reconstruction method to obtain dual R&C gated images. The four dual R&C MVF estimation methods were applied to the dual R&C gated images and the accuracy of motion estimation was quantitatively evaluated using the root mean square error (RMSE) of the estimated MVFs. Results show that among the four estimation methods, Methods 2 performed the worst for noise-free case while Method 1 performed the worst for noisy cases in terms of quantitative accuracy of the estimated MVF. Methods 4 and 3 showed comparable results and achieved RMSE lower by up to 35% than that in Method 1 for noisy cases. In conclusion, we have developed and evaluated 4 different post-reconstruction R&C MVF estimation methods for use in 4D PET imaging. Comparison of the performance of four methods on simulated data indicates separate R&C estimation with modeling of RM before CM estimation (Method 3) to be

Estimating the Response of Mid-latitude Orographic Precipitation to Global Warming

NASA Astrophysics Data System (ADS)

Shi, Xiaoming

The possible change in orographic precipitation in response to global warming is a rising concern under climate change, which could potentially cause significant societal impact. A general circulation model was employed to simulate the climate on an aquaplanet which has idealized mountains at its mid-latitudes. It was found that orographic precipitation at northern mid-latitudes could increase by rates faster than the Clausius-Clapeyron scaling, ˜7%/K of surface warming, in doubling CO2 simulations, while at southern mid-latitudes orographic precipitation decreased. The frequency of extreme events increased at all latitudes of the idealized mountains. Through a simple diagnostic model it was revealed that the changes in the climatological means of orographic precipitation rates were mostly determined by the changes in three variables: the speed of the wind component perpendicular to a mountain, the vertical displacement of saturated parcels, and the moist adiabatic lapse rate of saturation specific humidity. The last variable had relatively uniform contribution to the total changes in orographic precipitation across different latitudes, about 4 -- 5%/K. But contributions from the changes in wind speed and saturated vertical displacement were found to have strong north-south asymmetry, which were linked to the poleward shift of storm tracks. The changes in wind speed had positive contributions in general, with larger contributions at higher mid-latitudes. While the changes in saturated vertical displacement had negative contributions at all latitudes, but larger negative contributions were located at lower mid-latitudes. Although the poleward shift of storm tracks greatly affects regional precipitation, following the poleward shift of storm tracks the cumulative distribution function (CDF) of precipitation at the latitudes of maximum precipitation in the control simulation is very similar to that in the warm climate simulation, except that precipitation intensity

Estimation of the isotopic composition and origins of winter precipitation over Japan using a regional isotope circulation model

NASA Astrophysics Data System (ADS)

Tanoue, M.; Ichiyanagi, K.; Yoshimura, K.; Shimada, J.; Hirabayashi, Y.

2017-12-01

Understanding the dynamics of the origins of precipitation (i.e., vapor source regions of evaporated moisture) is useful for long-term forecasting and calibration of water isotope thermometer. In the Asian monsoon region, vapor source regions are identified by the deuterium excess (d-excess; defined as δD - 8 • δ18O) of precipitation because its values mainly reflect humidity conditions during evaporation at the source regions. In Japan, previous studies assumed the Sea of Japan to be the dominant source of winter precipitation when the d-excess value in winter is >20‰ or higher than the average value in summer. Because this assumption is based on an interpretation that the high d-excess value is due to an interaction between the continental winter monsoon (WM) and warm sea surface at the Sea of Japan, it may not be appropriate for winter precipitation caused by extratropical cyclones (EC). Here, we utilized a regional isotope circulation model and then clarified local patterns of isotopic composition and the origins of precipitation in the WM and EC types over Japan. The results indicated that moisture originating from the Sea of Japan made the highest contribution to precipitation on the Sea of Japan side of Japan in the WM type, whereas the Pacific Ocean was the dominant source of precipitation over Japan in the EC type. Because d-excess values were higher in the WM than in the EC type, we can assume that the Sea of Japan was the dominant source of precipitation on the Sea of Japan side when the d-excess value was high. Because precipitation on the Pacific Ocean side and the Kyushu island of Japan was mainly caused by the EC type, we could not identify the dominant source of precipitation as the Sea of Japan from only the d-excess values in these regions. We also found that WM activity could be estimated from observed d-excess values due to a clear positive correlation between simulated d-excess values and the activity.

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

A NOVEL TECHNIQUE FOR QUANTITATIVE ESTIMATION OF UPTAKE OF DIESEL EXHAUST PARTICLES BY LUNG CELLS

EPA Science Inventory

While airborne particulates like diesel exhaust particulates (DEP) exert significant toxicological effects on lungs, quantitative estimation of accumulation of DEP inside lung cells has not been reported due to a lack of an accurate and quantitative technique for this purpose. I...

Models of Quantitative Estimations: Rule-Based and Exemplar-Based Processes Compared

ERIC Educational Resources Information Center

von Helversen, Bettina; Rieskamp, Jorg

2009-01-01

The cognitive processes underlying quantitative estimations vary. Past research has identified task-contingent changes between rule-based and exemplar-based processes (P. Juslin, L. Karlsson, & H. Olsson, 2008). B. von Helversen and J. Rieskamp (2008), however, proposed a simple rule-based model--the mapping model--that outperformed the…

Quantitative estimation of Nipah virus replication kinetics in vitro

PubMed Central

Chang, Li-Yen; Ali, AR Mohd; Hassan, Sharifah Syed; AbuBakar, Sazaly

2006-01-01

Background Nipah virus is a zoonotic virus isolated from an outbreak in Malaysia in 1998. The virus causes infections in humans, pigs, and several other domestic animals. It has also been isolated from fruit bats. The pathogenesis of Nipah virus infection is still not well described. In the present study, Nipah virus replication kinetics were estimated from infection of African green monkey kidney cells (Vero) using the one-step SYBR® Green I-based quantitative real-time reverse transcriptase-polymerase chain reaction (qRT-PCR) assay. Results The qRT-PCR had a dynamic range of at least seven orders of magnitude and can detect Nipah virus from as low as one PFU/μL. Following initiation of infection, it was estimated that Nipah virus RNA doubles at every ~40 minutes and attained peak intracellular virus RNA level of ~8.4 log PFU/μL at about 32 hours post-infection (PI). Significant extracellular Nipah virus RNA release occurred only after 8 hours PI and the level peaked at ~7.9 log PFU/μL at 64 hours PI. The estimated rate of Nipah virus RNA released into the cell culture medium was ~0.07 log PFU/μL per hour and less than 10% of the released Nipah virus RNA was infectious. Conclusion The SYBR® Green I-based qRT-PCR assay enabled quantitative assessment of Nipah virus RNA synthesis in Vero cells. A low rate of Nipah virus extracellular RNA release and low infectious virus yield together with extensive syncytial formation during the infection support a cell-to-cell spread mechanism for Nipah virus infection. PMID:16784519

First estimates of the contribution of CaCO3 precipitation to the release of CO2 to the atmosphere during young sea ice growth

NASA Astrophysics Data System (ADS)

Geilfus, N.-X.; Carnat, G.; Dieckmann, G. S.; Halden, N.; Nehrke, G.; Papakyriakou, T.; Tison, J.-L.; Delille, B.

2013-01-01

report measurements of pH, total alkalinity, air-ice CO2 fluxes (chamber method), and CaCO3 content of frost flowers (FF) and thin landfast sea ice. As the temperature decreases, concentration of solutes in the brine skim increases. Along this gradual concentration process, some salts reach their solubility threshold and start precipitating. The precipitation of ikaite (CaCO3.6H2O) was confirmed in the FF and throughout the ice by Raman spectroscopy and X-ray analysis. The amount of ikaite precipitated was estimated to be 25 µmol kg-1 melted FF, in the FF and is shown to decrease from 19 to 15 µmol kg-1 melted ice in the upper part and at the bottom of the ice, respectively. CO2 release due to precipitation of CaCO3 is estimated to be 50 µmol kg-1 melted samples. The dissolved inorganic carbon (DIC) normalized to a salinity of 10 exhibits significant depletion in the upper layer of the ice and in the FF. This DIC loss is estimated to be 2069 µmol kg-1 melted sample and corresponds to a CO2 release from the ice to the atmosphere ranging from 20 to 40 mmol m-2 d-1. This estimate is consistent with flux measurements of air-ice CO2 exchange. Our measurements confirm previous laboratory findings that growing young sea ice acts as a source of CO2 to the atmosphere. CaCO3 precipitation during early ice growth appears to promote the release of CO2 to the atmosphere; however, its contribution to the overall release by newly formed ice is most likely minor.

Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study.

PubMed

Walker, Martin; Basáñez, María-Gloria; Ouédraogo, André Lin; Hermsen, Cornelus; Bousema, Teun; Churcher, Thomas S

2015-01-16

Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen density in a variety of clinical and epidemiological contexts. These methods are often classified as semi-quantitative, yet estimates of reliability or sensitivity are seldom reported. Here, a statistical framework is developed for assessing the reliability (uncertainty) of pathogen densities estimated using QMMs and the associated diagnostic sensitivity. The method is illustrated with quantification of Plasmodium falciparum gametocytaemia by QT-NASBA. The reliability of pathogen (e.g. gametocyte) densities, and the accompanying diagnostic sensitivity, estimated by two contrasting statistical calibration techniques, are compared; a traditional method and a mixed model Bayesian approach. The latter accounts for statistical dependence of QMM assays run under identical laboratory protocols and permits structural modelling of experimental measurements, allowing precision to vary with pathogen density. Traditional calibration cannot account for inter-assay variability arising from imperfect QMMs and generates estimates of pathogen density that have poor reliability, are variable among assays and inaccurately reflect diagnostic sensitivity. The Bayesian mixed model approach assimilates information from replica QMM assays, improving reliability and inter-assay homogeneity, providing an accurate appraisal of quantitative and diagnostic performance. Bayesian mixed model statistical calibration supersedes traditional techniques in the context of QMM-derived estimates of pathogen density, offering the potential to improve substantially the depth and quality of clinical and epidemiological inference for a wide variety of pathogens.

Accounting for Effects of Orography in LDAS Precipitation Forcing Data

NASA Astrophysics Data System (ADS)

Schaake, J.; Higgins, W.; Cong, S.; Shi, W.; Duan, Q.; Yarosh, E.

2001-05-01

Precipitation analysis procedures that are widely used to make gridded precipitation estimates do not work well in mountainous areas because the gage density is too sparse relative to the spatacial frequency content of the actual precipitation field. Moreover, in the western U.S. most of the precipitation observations are low elevations and may not even detect occurrence of storms at high elevations. Although there are indeed significant limits to how accurately actual fields of orographic precipitation can be estimated from gage data alone, it is possible to make estimates for each time period that, over a period of time, have a climatology that should approximate the true climatology of the actual events. Analysis schemes that use the PRISM precipitation climatology to aid the precipitation analysis are being tested. The results of these tests will be presented.

Validation of HOAPS- and ERA-Interim precipitation estimates over the ocean

NASA Astrophysics Data System (ADS)

Bumke, Karl; Schröder, Marc; Fennig, Karsten

2014-05-01

Although precipitation is one of the key parameters of the global hydrological cycle there are still large gaps in the global observation networks, especially over the oceans. But the progress in satellite technology has provided the possibility to retrieve global data sets from space, including precipitation. Levizzani et al. (2007) showed that precipitation over the oceans can be derived with sufficient accuracy from passive microwave radiometry. Advances in analysis techniques have also improved our knowledge of the global precipitation. On the other hand, e.g. Andersson et al. (2011) or Pfeifroth et al. (2012) pointed out that even state-of-the-art satellite retrievals and reanalysis data sets still disagree on global or regional precipitation with respect to amounts, patterns, variability or temporal behavior compared to observations. That creates the need for a validation study over data sparse areas. Within this study, a validation of HOAPS-3.0 (Hamburg Ocean Atmosphere Parameters and fluxes from Satellite Data) based precipitation at pixel-level resolution and of ERA-Interim reanalysis data for 1995-1997 is performed mainly over the Atlantic Ocean using information from ship rain gauges and optical disdrometers mounted onboard of research vessels. The satellite and ERA-Interim data are compared to the in situ measurement by the nearest neighbor approach. Therefore, it must be ensured that both observations are related to each other, which can be determined by the decorrelation lengths in space and time. At least a number of 658 precipitation events are at our disposal including 127 snow events. The statistical analysis follows the recommendations given by the World Meteorological Organization (WMO) for dichotomous or binary forecasts (WWRP/WGNE: http://www.cawcr.gov.au/projects/verification/#Methods_for_dichotomous_forecasts). Based on contingency tables a number of statistical parameters like the accuracy, the bias, the false alarm rate, success ratio or

Precipitation of molybdenum(V) as the hydroxide and its separation from rhenium.

PubMed

Yatirajam, V; Ahuja, U; Kakkar, L R

1975-03-01

A study of the conditions for precipitation of molybdenum(V) hydroxide shows that for Mo concentration 1 mg ml about 97.5% of the Mo can be precipitated between pH 5 and 5.8. Lower concentrations of molybdenum(V) or molybdenum(VI) can be precipitated quantitatively by using 20 times the amount of zirconium as collector, at the same pH. On this basis, a simple method is given for quantitative separation of rhenium from large amounts of molybdenum and is attested by analysis of synthetic and molybdenite samples.

Epithelium percentage estimation facilitates epithelial quantitative protein measurement in tissue specimens.

PubMed

Chen, Jing; Toghi Eshghi, Shadi; Bova, George Steven; Li, Qing Kay; Li, Xingde; Zhang, Hui

2013-12-01

The rapid advancement of high-throughput tools for quantitative measurement of proteins has demonstrated the potential for the identification of proteins associated with cancer. However, the quantitative results on cancer tissue specimens are usually confounded by tissue heterogeneity, e.g. regions with cancer usually have significantly higher epithelium content yet lower stromal content. It is therefore necessary to develop a tool to facilitate the interpretation of the results of protein measurements in tissue specimens. Epithelial cell adhesion molecule (EpCAM) and cathepsin L (CTSL) are two epithelial proteins whose expressions in normal and tumorous prostate tissues were confirmed by measuring staining intensity with immunohistochemical staining (IHC). The expressions of these proteins were measured by ELISA in protein extracts from OCT embedded frozen prostate tissues. To eliminate the influence of tissue heterogeneity on epithelial protein quantification measured by ELISA, a color-based segmentation method was developed in-house for estimation of epithelium content using H&E histology slides from the same prostate tissues and the estimated epithelium percentage was used to normalize the ELISA results. The epithelium contents of the same slides were also estimated by a pathologist and used to normalize the ELISA results. The computer based results were compared with the pathologist's reading. We found that both EpCAM and CTSL levels, measured by ELISA assays itself, were greatly affected by epithelium content in the tissue specimens. Without adjusting for epithelium percentage, both EpCAM and CTSL levels appeared significantly higher in tumor tissues than normal tissues with a p value less than 0.001. However, after normalization by the epithelium percentage, ELISA measurements of both EpCAM and CTSL were in agreement with IHC staining results, showing a significant increase only in EpCAM with no difference in CTSL expression in cancer tissues. These results

Annual and average estimates of water-budget components based on hydrograph separation and PRISM precipitation for gaged basins in the Appalachian Plateaus Region, 1900-2011

USGS Publications Warehouse

Nelms, David L.; Messinger, Terence; McCoy, Kurt J.

2015-07-14

As part of the U.S. Geological Survey’s Groundwater Resources Program study of the Appalachian Plateaus aquifers, annual and average estimates of water-budget components based on hydrograph separation and precipitation data from parameter-elevation regressions on independent slopes model (PRISM) were determined at 849 continuous-record streamflow-gaging stations from Mississippi to New York and covered the period of 1900 to 2011. Only complete calendar years (January to December) of streamflow record at each gage were used to determine estimates of base flow, which is that part of streamflow attributed to groundwater discharge; such estimates can serve as a proxy for annual recharge. For each year, estimates of annual base flow, runoff, and base-flow index were determined using computer programs—PART, HYSEP, and BFI—that have automated the separation procedures. These streamflow-hydrograph analysis methods are provided with version 1.0 of the U.S. Geological Survey Groundwater Toolbox, which is a new program that provides graphing, mapping, and analysis capabilities in a Windows environment. Annual values of precipitation were estimated by calculating the average of cell values intercepted by basin boundaries where previously defined in the GAGES–II dataset. Estimates of annual evapotranspiration were then calculated from the difference between precipitation and streamflow.

Precipitation Recycling and the Vertical Distribution of Local and Remote Sources of Water for Precipitation

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Atlas, Robert (Technical Monitor)

2002-01-01

Precipitation recycling is defined as the amount of water that evaporates from a region that precipitates within the same region. This is also interpreted as the local source of water for precipitation. In this study, the local and remote sources of water for precipitation have been diagnosed through the use of passive constituent tracers that represent regional evaporative sources along with their transport and precipitation. We will discuss the differences between this method and the simpler bulk diagnostic approach to precipitation recycling. A summer seasonal simulation has been analyzed for the regional sources of the United States Great Plains precipitation. While the tropical Atlantic Ocean (including the Gulf of Mexico) and the local continental sources of precipitation are most dominant, the vertically integrated column of water contains substantial water content originating from the Northern Pacific Ocean, which is not precipitated. The vertical profiles of regional water sources indicate that local Great Plains source of water dominates the lower troposphere, predominantly in the PBL. However, the Pacific Ocean source is dominant over a large portion of the middle to upper troposphere. The influence of the tropical Atlantic Ocean is reasonably uniform throughout the column. While the results are not unexpected given the formulation of the model's convective parameterization, the analysis provides a quantitative assessment of the impact of local evaporation on the occurrence of convective precipitation in the GCM. Further, these results suggest that local source of water is not well mixed throughout the vertical column.

Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Liu, Qing; Bindlish, Rajat; Cosh, Michael H.; Crow, Wade T.; deJeu, Richard; DeLannoy, Gabrielle J. M.; Huffman, George J.; Jackson, Thomas J.

2011-01-01

The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill is measured against in situ observations in the continental United States at 44 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface moisture skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or soil moisture retrievals increases surface soil moisture skill levels by IDDeltaR=0.06-0.08, and root zone soil moisture skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface soil moisture skill levels by DeltaR=0.13, and root zone soil moisture skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite soil moisture retrievals contribute similar and largely independent amounts of information.

Physiological frailty index (PFI): quantitative in-life estimate of individual biological age in mice.

PubMed

Antoch, Marina P; Wrobel, Michelle; Kuropatwinski, Karen K; Gitlin, Ilya; Leonova, Katerina I; Toshkov, Ilia; Gleiberman, Anatoli S; Hutson, Alan D; Chernova, Olga B; Gudkov, Andrei V

2017-03-19

The development of healthspan-extending pharmaceuticals requires quantitative estimation of age-related progressive physiological decline. In humans, individual health status can be quantitatively assessed by means of a frailty index (FI), a parameter which reflects the scale of accumulation of age-related deficits. However, adaptation of this methodology to animal models is a challenging task since it includes multiple subjective parameters. Here we report a development of a quantitative non-invasive procedure to estimate biological age of an individual animal by creating physiological frailty index (PFI). We demonstrated the dynamics of PFI increase during chronological aging of male and female NIH Swiss mice. We also demonstrated acceleration of growth of PFI in animals placed on a high fat diet, reflecting aging acceleration by obesity and provide a tool for its quantitative assessment. Additionally, we showed that PFI could reveal anti-aging effect of mTOR inhibitor rapatar (bioavailable formulation of rapamycin) prior to registration of its effects on longevity. PFI revealed substantial sex-related differences in normal chronological aging and in the efficacy of detrimental (high fat diet) or beneficial (rapatar) aging modulatory factors. Together, these data introduce PFI as a reliable, non-invasive, quantitative tool suitable for testing potential anti-aging pharmaceuticals in pre-clinical studies.

Spatial interpolation schemes of daily precipitation for hydrologic modeling

USGS Publications Warehouse

Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

2012-01-01

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

A generalized groundwater fluctuation model based on precipitation for estimating water table levels of deep unconfined aquifers

NASA Astrophysics Data System (ADS)

Jeong, Jina; Park, Eungyu; Shik Han, Weon; Kim, Kue-Young; Suk, Heejun; Beom Jo, Si

2018-07-01

A generalized water table fluctuation model based on precipitation was developed using a statistical conceptualization of unsaturated infiltration fluxes. A gamma distribution function was adopted as a transfer function due to its versatility in representing recharge rates with temporally dispersed infiltration fluxes, and a Laplace transformation was used to obtain an analytical solution. To prove the general applicability of the model, convergences with previous water table fluctuation models were shown as special cases. For validation, a few hypothetical cases were developed, where the applicability of the model to a wide range of unsaturated zone conditions was confirmed. For further validation, the model was applied to water table level estimations of three monitoring wells with considerably thick unsaturated zones on Jeju Island. The results show that the developed model represented the pattern of hydrographs from the two monitoring wells fairly well. The lag times from precipitation to recharge estimated from the developed system transfer function were found to agree with those from a conventional cross-correlation analysis. The developed model has the potential to be adopted for the hydraulic characterization of both saturated and unsaturated zones by being calibrated to actual data when extraneous and exogenous causes of water table fluctuation are limited. In addition, as it provides reference estimates, the model can be adopted as a tool for surveilling groundwater resources under hydraulically stressed conditions.

The relationship between precipitation and insurance data for floods in a Mediterranean region (northeast Spain)

NASA Astrophysics Data System (ADS)

Cortès, Maria; Turco, Marco; Llasat-Botija, Montserrat; Llasat, Maria Carmen

2018-03-01

Floods in the Mediterranean region are often surface water floods, in which intense precipitation is usually the main driver. Determining the link between the causes and impacts of floods can make it easier to calculate the level of flood risk. However, up until now, the limitations in quantitative observations for flood-related damages have been a major obstacle when attempting to analyse flood risk in the Mediterranean. Flood-related insurance damage claims for the last 20 years could provide a proxy for flood impact, and this information is now available in the Mediterranean region of Catalonia, in northeast Spain. This means a comprehensive analysis of the links between flood drivers and impacts is now possible. The objective of this paper is to develop and evaluate a methodology to estimate flood damages from heavy precipitation in a Mediterranean region. Results show that our model is able to simulate the probability of a damaging event as a function of precipitation. The relationship between precipitation and damage provides insights into flood risk in the Mediterranean and is also promising for supporting flood management strategies.

Precipitation recycling in the Amazon basin

NASA Technical Reports Server (NTRS)

Eltahir, E. A. B.; Bras, R. L.

1994-01-01

Precipitation recycling is the contribution of evaporation within a region to precipitation in that same region. The recycling rate is a diagnostic measure of the potential for interactions between land surface hydrology and regional climate. In this paper we present a model for describing the seasonal and spatial variability of the recycling process. The precipitation recycling ratio, rho, is the basic variable in describing the recycling process. Rho is the fraction of precipitation at a certain location and time which is contributed by evaporation within the region under study. The recycling model is applied in studyiing the hydrologic cycle in the Amazon basin. It is estimated that about 25% of all the rain that falls in the Amazon basin is contributed by evaporation within the basin. This estimate is based on analysis of a data set supplied by the European Centre for Medium-range Weather Forecasts (ECMWF). The same analysis is repeated using a different data set from the Geophysical Fluid Dynamics Laboratory (GFDL). Based on this data set, the recycling ratio is estimated to be 35%. The seasonal variability of the recycling ratio is small compared with the yearly average. The new estimates of the recycling ratio are compared with results of previous studies, and the differences are explained.

Estimating spatially and temporally varying recharge and runoff from precipitation and urban irrigation in the Los Angeles Basin, California

USGS Publications Warehouse

Hevesi, Joseph A.; Johnson, Tyler D.

2016-10-17

A daily precipitation-runoff model, referred to as the Los Angeles Basin watershed model (LABWM), was used to estimate recharge and runoff for a 5,047 square kilometer study area that included the greater Los Angeles area and all surface-water drainages potentially contributing recharge to a 1,450 square kilometer groundwater-study area underlying the greater Los Angeles area, referred to as the Los Angeles groundwater-study area. The recharge estimates for the Los Angeles groundwater-study area included spatially distributed recharge in response to the infiltration of precipitation, runoff, and urban irrigation, as well as mountain-front recharge from surface-water drainages bordering the groundwater-study area. The recharge and runoff estimates incorporated a new method for estimating urban irrigation, consisting of residential and commercial landscape watering, based on land use and the percentage of pervious land area.The LABWM used a 201.17-meter gridded discretization of the study area to represent spatially distributed climate and watershed characteristics affecting the surface and shallow sub-surface hydrology for the Los Angeles groundwater study area. Climate data from a local network of 201 monitoring sites and published maps of 30-year-average monthly precipitation and maximum and minimum air temperature were used to develop the climate inputs for the LABWM. Published maps of land use, land cover, soils, vegetation, and surficial geology were used to represent the physical characteristics of the LABWM area. The LABWM was calibrated to available streamflow records at six streamflow-gaging stations.Model results for a 100-year target-simulation period, from water years 1915 through 2014, were used to quantify and evaluate the spatial and temporal variability of water-budget components, including evapotranspiration (ET), recharge, and runoff. The largest outflow of water from the LABWM was ET; the 100-year average ET rate of 362 millimeters per year (mm

Spectral Feature Analysis for Quantitative Estimation of Cyanobacteria Chlorophyll-A

NASA Astrophysics Data System (ADS)

Lin, Yi; Ye, Zhanglin; Zhang, Yugan; Yu, Jie

2016-06-01

In recent years, lake eutrophication caused a large of Cyanobacteria bloom which not only brought serious ecological disaster but also restricted the sustainable development of regional economy in our country. Chlorophyll-a is a very important environmental factor to monitor water quality, especially for lake eutrophication. Remote sensed technique has been widely utilized in estimating the concentration of chlorophyll-a by different kind of vegetation indices and monitoring its distribution in lakes, rivers or along coastline. For each vegetation index, its quantitative estimation accuracy for different satellite data might change since there might be a discrepancy of spectral resolution and channel center between different satellites. The purpose this paper is to analyze the spectral feature of chlorophyll-a with hyperspectral data (totally 651 bands) and use the result to choose the optimal band combination for different satellites. The analysis method developed here in this study could be useful to recognize and monitor cyanobacteria bloom automatically and accrately. In our experiment, the reflectance (from 350nm to 1000nm) of wild cyanobacteria in different consistency (from 0 to 1362.11ug/L) and the corresponding chlorophyll-a concentration were measured simultaneously. Two kinds of hyperspectral vegetation indices were applied in this study: simple ratio (SR) and narrow band normalized difference vegetation index (NDVI), both of which consists of any two bands in the entire 651 narrow bands. Then multivariate statistical analysis was used to construct the linear, power and exponential models. After analyzing the correlation between chlorophyll-a and single band reflectance, SR, NDVI respetively, the optimal spectral index for quantitative estimation of cyanobacteria chlorophyll-a, as well corresponding central wavelength and band width were extracted. Results show that: Under the condition of water disturbance, SR and NDVI are both suitable for quantitative

Validation of High Resolution Orbital Precipitation Over Upper Mahanadi River Basin, India

NASA Astrophysics Data System (ADS)

Gautam, A. K.; Pandey, A.

2016-12-01

Precipitation is one of the most important component of hydrologic cycle and used for various applications i.e. hydrological modeling, structure design to water management policy. Satellite based precipitation, radar rainfall and rain-gauge networks are supporting to each other, in relation to their spatial coverage and ability of observing precipitation. In the absence of rainfall data, satellite precipitation products can be used in the developing countries and over complex terrain where precipitation observations are either sparse or not available. However, satellite precipitation estimates are affected by different errors (AghaKouchak, et al., 2012.). Therefore, ground validation of satellite precipitation estimates is essential. In this study, the upper Mahanadi River Basin (A Part of Central India), has been selected for evaluation of the TRMM multi-satellite precipitation analysis (TMPA) and IMERG (Integrated Multi-satellite Retrievals for GPM) satellite Based Precipitation Products for the period of April 2014 - December 2015. The TMPA (3B42V7) and IMERG (late run) precipitation estimates were evaluated using statistical, contingency table and volumetric method for available 112 rain gauge stations in the study area. Results indicated that, both IMERG and TMPA precipitation overestimated the daily precipitation. The results also revealed that IMERG precipitation estimates provide better accuracy than TMPA precipitation estimates for very light rain (0.1-2.5 mm day-1), light rain (2.5-7.5 mm day-1), moderate rain (7.5-35.5 mm day-1), heavy rain (35.5-64.5 mm day-1) and very heavy rain (>64.5 mm day-1). Although, the detection capability of daily TMPA precipitation performed better in heavy rain. The results showed a good correlation (as high as 0.84) and poor correlation (as low as 0.012) with GPM satellite data over the most parts of the study area. The analyses suggest that, there is a need for improvement in precipitation estimation algorithm and accuracy

Depth-area-duration characteristics of storm rainfall in Texas using Multi-Sensor Precipitation Estimates

NASA Astrophysics Data System (ADS)

McEnery, J. A.; Jitkajornwanich, K.

2012-12-01

This presentation will describe the methodology and overall system development by which a benchmark dataset of precipitation information has been used to characterize the depth-area-duration relations in heavy rain storms occurring over regions of Texas. Over the past two years project investigators along with the National Weather Service (NWS) West Gulf River Forecast Center (WGRFC) have developed and operated a gateway data system to ingest, store, and disseminate NWS multi-sensor precipitation estimates (MPE). As a pilot project of the Integrated Water Resources Science and Services (IWRSS) initiative, this testbed uses a Standard Query Language (SQL) server to maintain a full archive of current and historic MPE values within the WGRFC service area. These time series values are made available for public access as web services in the standard WaterML format. Having this volume of information maintained in a comprehensive database now allows the use of relational analysis capabilities within SQL to leverage these multi-sensor precipitation values and produce a valuable derivative product. The area of focus for this study is North Texas and will utilize values that originated from the West Gulf River Forecast Center (WGRFC); one of three River Forecast Centers currently represented in the holdings of this data system. Over the past two decades, NEXRAD radar has dramatically improved the ability to record rainfall. The resulting hourly MPE values, distributed over an approximate 4 km by 4 km grid, are considered by the NWS to be the "best estimate" of rainfall. The data server provides an accepted standard interface for internet access to the largest time-series dataset of NEXRAD based MPE values ever assembled. An automated script has been written to search and extract storms over the 18 year period of record from the contents of this massive historical precipitation database. Not only can it extract site-specific storms, but also duration-specific storms and

Continuous Estimates of Precipitable Water Vapor Within and Around Hurricane Systems

NASA Astrophysics Data System (ADS)

Braun, J. J.; Iwabuchi, T.; van Hove, T.

2008-12-01

This study investigates how estimates of precipitable water vapor (PW) from Global Positioning System (GPS) stations can be used to quantify how atmospheric moisture influences the intensity of tropical storms and hurricanes. The motivation for this study is based on the fact that hurricanes derive their strength through water vapor that is both evaporated from warm ocean surfaces and the existing moisture in the surrounding atmospheric environment. Observationally, there are relatively few instruments that can accurately measure water vapor in the presence of clouds and rain. Retrievals of PW using GPS stations may be the most reliable way to continuously monitor column integrated water vapor. Using storm information from the National Hurricane Center (www.nhc.noaa.gov), we have compared storm intensity to PW estimates for all tropical storms and hurricanes making landfall within 100-km of a GPS station between 2003 and 2008. We find that PW is inversely correlated (r**2 < -0.7) to the drop in surface pressure observed at that station. We have also begun to relate atmospheric PW at a station to the local sea surface temperature (SST). This comparison can be used to measure how strongly atmospheric water vapor and SST are coupled. It can also be used to measure how quickly the atmosphere responds to changes in SST. Finally we have compared the estimated PW to the Global Forecast System (GFS) analysis fields that are used to initialize numerical weather prediction models. This comparison indicates that the GFS analysis fields have significantly larger errors in atmospheric moisture in the Caribbean and Gulf of Mexico when compared to differences over the continental United States. These results illustrate that estimates of PW are an important data set for atmospheric scientists and forecasters attempting to improve the prediction of hurricane intensity.

Strengthening due to Cr-rich precipitates in Fe-Cr alloys: Effect of temperature and precipitate composition

NASA Astrophysics Data System (ADS)

Terentyev, D.; Hafez Haghighat, S. M.; Schäublin, R.

2010-03-01

Molecular dynamics (MD) simulations were carried out to study the interaction between nanometric Cr precipitates and a 1/2 ⟨111⟩{110} edge dislocation (ED) in pure Fe and Fe-9 at. % Cr (Fe-9Cr) random alloy. The aim of this work is to estimate the variation in the pinning strength of the Cr precipitate as a function of temperature, its chemical composition and the matrix composition in which the precipitate is embedded. The dislocation was observed to shear Cr precipitates rather than by-pass via the formation of the Orowan loop, even though a pronounced screw dipole was emerged in the reactions with the precipitates of size larger than 4.5 nm. The screw arms of the formed dipole were not observed to climb thus no point defects were left inside the sheared precipitates, irrespective of simulation temperature. Both Cr solution and Cr precipitates, embedded in the Fe-9Cr matrix, were seen to contribute to the flow stress. The decrease in the flow stress with temperature in the alloy containing Cr precipitates is, therefore, related to the simultaneous change in the matrix friction stress, precipitate resistance, and dislocation flexibility. Critical stress estimated from MD simulations was seen to have a strong dependence on the precipitate composition. If the latter decreases from 95% down to 80%, the corresponding critical stress decreases almost as twice. The results presented here suggest a significant contribution to the flow stress due to the α -α' separation, at least for EDs. The obtained data can be used to validate and to parameterize dislocation dynamics models, where the temperature dependence of the obstacle strength is an essential input data.

Nonlinear Acoustical Assessment of Precipitate Nucleation

NASA Technical Reports Server (NTRS)

Cantrell, John H.; Yost, William T.

2004-01-01

The purpose of the present work is to show that measurements of the acoustic nonlinearity parameter in heat treatable alloys as a function of heat treatment time can provide quantitative information about the kinetics of precipitate nucleation and growth in such alloys. Generally, information on the kinetics of phase transformations is obtained from time-sequenced electron microscopical examination and differential scanning microcalorimetry. The present nonlinear acoustical assessment of precipitation kinetics is based on the development of a multiparameter analytical model of the effects on the nonlinearity parameter of precipitate nucleation and growth in the alloy system. A nonlinear curve fit of the model equation to the experimental data is then used to extract the kinetic parameters related to the nucleation and growth of the targeted precipitate. The analytical model and curve fit is applied to the assessment of S' precipitation in aluminum alloy 2024 during artificial aging from the T4 to the T6 temper.

Frequency analysis and its spatiotemporal characteristics of precipitation extreme events in China during 1951-2010

NASA Astrophysics Data System (ADS)

Shao, Yuehong; Wu, Junmei; Ye, Jinyin; Liu, Yonghe

2015-08-01

This study investigates frequency analysis and its spatiotemporal characteristics of precipitation extremes based on annual maximum of daily precipitation (AMP) data of 753 observation stations in China during the period 1951-2010. Several statistical methods including L-moments, Mann-Kendall test (MK test), Student's t test ( t test) and analysis of variance ( F-test) are used to study different statistical properties related to frequency and spatiotemporal characteristics of precipitation extremes. The results indicate that the AMP series of most sites have no linear trends at 90 % confidence level, but there is a distinctive decrease trend in Beijing-Tianjin-Tangshan region. The analysis of abrupt changes shows that there are no significant changes in most sites, and no distinctive regional patterns within the mutation sites either. An important innovation different from the previous studies is the shift in the mean and the variance which are also studied in this paper in order to further analyze the changes of strong and weak precipitation extreme events. The shift analysis shows that we should pay more attention to the drought in North China and to the flood control and drought in South China, especially to those regions that have no clear trend and have a significant shift in the variance. More important, this study conducts the comprehensive analysis of a complete set of quantile estimates and its spatiotemporal characteristic in China. Spatial distribution of quantile estimation based on the AMP series demonstrated that the values gradually increased from the Northwest to the Southeast with the increment of duration and return period, while the increasing rate of estimation is smooth in the arid and semiarid region and is rapid in humid region. Frequency estimates of 50-year return period are in agreement with the maximum observations of AMP series in the most stations, which can provide more quantitative and scientific basis for decision making.

Effect of Nitrite/Nitrate concentrations on Corrosivity of Washed Precipitate

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

Congdon, J.W.

2001-03-28

Cyclic polarization scans were performed using A-537 carbon steel in simulated washed precipitate solutions of various nitrite and nitrate concentrations. The results of this study indicate that nitrate is an aggressive anion in washed precipitate. Furthermore, a quantitative linear log-log relationship between the minimum effective nitrite concentration and the nitrate concentration was established for washed precipitate with other ions at their average compositions.

A suite of global reconstructed precipitation products and their error estimate by multivariate regression using empirical orthogonal functions: 1850-present

NASA Astrophysics Data System (ADS)

Shen, S. S.

2014-12-01

This presentation describes a suite of global precipitation products reconstructed by a multivariate regression method using an empirical orthogonal function (EOF) expansion. The sampling errors of the reconstruction are estimated for each product datum entry. The maximum temporal coverage is 1850-present and the spatial coverage is quasi-global (75S, 75N). The temporal resolution ranges from 5-day, monthly, to seasonal and annual. The Global Precipitation Climatology Project (GPCP) precipitation data from 1979-2008 are used to calculate the EOFs. The Global Historical Climatology Network (GHCN) gridded data are used to calculate the regression coefficients for reconstructions. The sampling errors of the reconstruction are analyzed in detail for different EOF modes. Our reconstructed 1900-2011 time series of the global average annual precipitation shows a 0.024 (mm/day)/100a trend, which is very close to the trend derived from the mean of 25 models of the CMIP5 (Coupled Model Intercomparison Project Phase 5). Our reconstruction examples of 1983 El Niño precipitation and 1917 La Niña precipitation (Figure 1) demonstrate that the El Niño and La Niña precipitation patterns are well reflected in the first two EOFs. The validation of our reconstruction results with GPCP makes it possible to use the reconstruction as the benchmark data for climate models. This will help the climate modeling community to improve model precipitation mechanisms and reduce the systematic difference between observed global precipitation, which hovers at around 2.7 mm/day for reconstructions and GPCP, and model precipitations, which have a range of 2.6-3.3 mm/day for CMIP5. Our precipitation products are publically available online, including digital data, precipitation animations, computer codes, readme files, and the user manual. This work is a joint effort between San Diego State University (Sam Shen, Nancy Tafolla, Barbara Sperberg, and Melanie Thorn) and University of Maryland (Phil

How effective is the new generation of GPM satellite precipitation in characterizing the rainfall variability over Malaysia?

NASA Astrophysics Data System (ADS)

Mahmud, Mohd Rizaludin; Hashim, Mazlan; Reba, Mohd Nadzri Mohd

2017-08-01

We investigated the potential of the new generation of satellite precipitation product from the Global Precipitation Mission (GPM) to characterize the rainfall in Malaysia. Most satellite precipitation products have limited ability to precisely characterize the high dynamic rainfall variation that occurred at both time and scale in this humid tropical region due to the coarse grid size to meet the physical condition of the smaller land size, sub-continent and islands. Prior to the status quo, an improved satellite precipitation was required to accurately measure the rainfall and its distribution. Subsequently, the newly released of GPM precipitation product at half-hourly and 0.1° resolution served an opportunity to anticipate the aforementioned conflict. Nevertheless, related evidence was not found and therefore, this study made an initiative to fill the gap. A total of 843 rain gauges over east (Borneo) and west Malaysia (Peninsular) were used to evaluate the rainfall the GPM rainfall data. The assessment covered all critical rainy seasons which associated with Asian Monsoon including northeast (Nov. - Feb.), southwest (May - Aug.) and their subsequent inter-monsoon period (Mar. - Apr. & Sep. - Oct.). The ability of GPM to provide quantitative rainfall estimates and qualitative spatial rainfall patterns were analysed. Our results showed that the GPM had good capacity to depict the spatial rainfall patterns in less heterogeneous rainfall patterns (Spearman's correlation, 0.591 to 0.891) compared to the clustered one (r = 0.368 to 0.721). Rainfall intensity and spatial heterogeneity that is largely driven by seasonal monsoon has significant influence on GPM ability to resolve local rainfall patterns. In quantitative rainfall estimation, large errors can be primarily associated with the rainfall intensity increment. 77% of the error variation can be explained through rainfall intensity particularly the high intensity (> 35 mm d-1). A strong relationship between GPM

Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded precipitation products

NASA Astrophysics Data System (ADS)

Tang, Guoqiang; Behrangi, Ali; Long, Di; Li, Changming; Hong, Yang

2018-04-01

Rain gauge observations are commonly used to evaluate the quality of satellite precipitation products. However, the inherent difference between point-scale gauge measurements and areal satellite precipitation, i.e. a point of space in time accumulation v.s. a snapshot of time in space aggregation, has an important effect on the accuracy and precision of qualitative and quantitative evaluation results. This study aims to quantify the uncertainty caused by various combinations of spatiotemporal scales (0.1°-0.8° and 1-24 h) of gauge network designs in the densely gauged and relatively flat Ganjiang River basin, South China, in order to evaluate the state-of-the-art satellite precipitation, the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG). For comparison with the dense gauge network serving as "ground truth", 500 sparse gauge networks are generated through random combinations of gauge numbers at each set of spatiotemporal scales. Results show that all sparse gauge networks persistently underestimate the performance of IMERG according to most metrics. However, the probability of detection is overestimated because hit and miss events are more likely fewer than the reference numbers derived from dense gauge networks. A nonlinear error function of spatiotemporal scales and the number of gauges in each grid pixel is developed to estimate the errors of using gauges to evaluate satellite precipitation. Coefficients of determination of the fitting are above 0.9 for most metrics. The error function can also be used to estimate the required minimum number of gauges in each grid pixel to meet a predefined error level. This study suggests that the actual quality of satellite precipitation products could be better than conventionally evaluated or expected, and hopefully enables non-subject-matter-expert researchers to have better understanding of the explicit uncertainties when using point-scale gauge observations to evaluate areal products.

Evaluation of High-Resolution Precipitation Estimates from Satellites during July 2012 Beijing Flood Event Using Dense Rain Gauge Observations

PubMed Central

Chen, Sheng; Liu, Huijuan; You, Yalei; Mullens, Esther; Hu, Junjun; Yuan, Ye; Huang, Mengyu; He, Li; Luo, Yongming; Zeng, Xingji; Tang, Guoqiang; Hong, Yang

2014-01-01

Satellite-based precipitation estimates products, CMORPH and PERSIANN-CCS, were evaluated with a dense rain gauge network over Beijing and adjacent regions for an extremely heavy precipitation event on July 21 2012. CMORPH and PEERSIANN-CSS misplaced the region of greatest rainfall accumulation, and failed to capture the spatial pattern of precipitation, evidenced by a low spatial correlation coefficient (CC). CMORPH overestimated the daily accumulated rainfall by 22.84% while PERSIANN-CCS underestimated by 72.75%. In the rainfall center, both CMORPH and PERSIANN-CCS failed to capture the temporal variation of the rainfall, and underestimated rainfall amounts by 43.43% and 87.26%, respectively. Based on our results, caution should be exercised when using CMORPH and PERSIANN-CCS as input for monitoring and forecasting floods in Beijing urban areas, and the potential for landslides in the mountainous zones west and north of Beijing. PMID:24691358

Evaluation of high-resolution precipitation estimates from satellites during July 2012 Beijing flood event using dense rain gauge observations.

PubMed

Chen, Sheng; Liu, Huijuan; You, Yalei; Mullens, Esther; Hu, Junjun; Yuan, Ye; Huang, Mengyu; He, Li; Luo, Yongming; Zeng, Xingji; Tang, Guoqiang; Hong, Yang

2014-01-01

Satellite-based precipitation estimates products, CMORPH and PERSIANN-CCS, were evaluated with a dense rain gauge network over Beijing and adjacent regions for an extremely heavy precipitation event on July 21 2012. CMORPH and PEERSIANN-CSS misplaced the region of greatest rainfall accumulation, and failed to capture the spatial pattern of precipitation, evidenced by a low spatial correlation coefficient (CC). CMORPH overestimated the daily accumulated rainfall by 22.84% while PERSIANN-CCS underestimated by 72.75%. In the rainfall center, both CMORPH and PERSIANN-CCS failed to capture the temporal variation of the rainfall, and underestimated rainfall amounts by 43.43% and 87.26%, respectively. Based on our results, caution should be exercised when using CMORPH and PERSIANN-CCS as input for monitoring and forecasting floods in Beijing urban areas, and the potential for landslides in the mountainous zones west and north of Beijing.

Direct Estimation of Optical Parameters From Photoacoustic Time Series in Quantitative Photoacoustic Tomography.

PubMed

Pulkkinen, Aki; Cox, Ben T; Arridge, Simon R; Goh, Hwan; Kaipio, Jari P; Tarvainen, Tanja

2016-11-01

Estimation of optical absorption and scattering of a target is an inverse problem associated with quantitative photoacoustic tomography. Conventionally, the problem is expressed as two folded. First, images of initial pressure distribution created by absorption of a light pulse are formed based on acoustic boundary measurements. Then, the optical properties are determined based on these photoacoustic images. The optical stage of the inverse problem can thus suffer from, for example, artefacts caused by the acoustic stage. These could be caused by imperfections in the acoustic measurement setting, of which an example is a limited view acoustic measurement geometry. In this work, the forward model of quantitative photoacoustic tomography is treated as a coupled acoustic and optical model and the inverse problem is solved by using a Bayesian approach. Spatial distribution of the optical properties of the imaged target are estimated directly from the photoacoustic time series in varying acoustic detection and optical illumination configurations. It is numerically demonstrated, that estimation of optical properties of the imaged target is feasible in limited view acoustic detection setting.

A Data Centred Method to Estimate and Map Changes in the Full Distribution of Daily Precipitation and Its Exceedances

NASA Astrophysics Data System (ADS)

Chapman, S. C.; Stainforth, D. A.; Watkins, N. W.

2014-12-01

Estimates of how our climate is changing are needed locally in order to inform adaptation planning decisions. This requires quantifying the geographical patterns in changes at specific quantiles or thresholds in distributions of variables such as daily temperature or precipitation. We develop a method[1] for analysing local climatic timeseries to assess which quantiles of the local climatic distribution show the greatest and most robust changes, to specifically address the challenges presented by 'heavy tailed' distributed variables such as daily precipitation. We extract from the data quantities that characterize the changes in time of the likelihood of daily precipitation above a threshold and of the relative amount of precipitation in those extreme precipitation days. Our method is a simple mathematical deconstruction of how the difference between two observations from two different time periods can be assigned to the combination of natural statistical variability and/or the consequences of secular climate change. This deconstruction facilitates an assessment of how fast different quantiles of precipitation distributions are changing. This involves both determining which quantiles and geographical locations show the greatest change but also, those at which any change is highly uncertain. We demonstrate this approach using E-OBS gridded data[2] timeseries of local daily precipitation from specific locations across Europe over the last 60 years. We treat geographical location and precipitation as independent variables and thus obtain as outputs the pattern of change at a given threshold of precipitation and with geographical location. This is model- independent, thus providing data of direct value in model calibration and assessment. Our results identify regionally consistent patterns which, dependent on location, show systematic increase in precipitation on the wettest days, shifts in precipitation patterns to less moderate days and more heavy days, and drying

A Bayesian kriging approach for blending satellite and ground precipitation observations

USGS Publications Warehouse

Verdin, Andrew P.; Rajagopalan, Balaji; Kleiber, William; Funk, Christopher C.

2015-01-01

Drought and flood management practices require accurate estimates of precipitation. Gauge observations, however, are often sparse in regions with complicated terrain, clustered in valleys, and of poor quality. Consequently, the spatial extent of wet events is poorly represented. Satellite-derived precipitation data are an attractive alternative, though they tend to underestimate the magnitude of wet events due to their dependency on retrieval algorithms and the indirect relationship between satellite infrared observations and precipitation intensities. Here we offer a Bayesian kriging approach for blending precipitation gauge data and the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates for Central America, Colombia, and Venezuela. First, the gauge observations are modeled as a linear function of satellite-derived estimates and any number of other variables—for this research we include elevation. Prior distributions are defined for all model parameters and the posterior distributions are obtained simultaneously via Markov chain Monte Carlo sampling. The posterior distributions of these parameters are required for spatial estimation, and thus are obtained prior to implementing the spatial kriging model. This functional framework is applied to model parameters obtained by sampling from the posterior distributions, and the residuals of the linear model are subject to a spatial kriging model. Consequently, the posterior distributions and uncertainties of the blended precipitation estimates are obtained. We demonstrate this method by applying it to pentadal and monthly total precipitation fields during 2009. The model's performance and its inherent ability to capture wet events are investigated. We show that this blending method significantly improves upon the satellite-derived estimates and is also competitive in its ability to represent wet events. This procedure also provides a means to estimate a full conditional distribution

Skill in Precipitation Forecasting in the National Weather Service.

NASA Astrophysics Data System (ADS)

Charba, Jerome P.; Klein, William H.

1980-12-01

All known long-term records of forecasting performance for different types of precipitation forecasts in the National Weather Service were examined for relative skill and secular trends in skill. The largest upward trends were achieved by local probability of precipitation (PoP) forecasts for the periods 24-36 h and 36-48 h after 0000 and 1200 GMT. Over the last 13 years, the skill of these forecasts has improved at an average rate of 7.2% per 10-year interval. Over the same period, improvement has been smaller in local PoP skill in the 12-24 h range (2.0% per 10 years) and in the accuracy of "Yea/No" forecasts of measurable precipitation. The overall trend in accuracy of centralized quantitative precipitation forecasts of 0.5 in and 1.0 in has been slightly upward at the 0-24 h range and strongly upward at the 24-48 h range. Most of the improvement in these forecasts has been achieved from the early 1970s to the present. Strong upward accuracy trends in all types of precipitation forecasts within the past eight years are attributed primarily to improvements in numerical and statistical centralized guidance forecasts.The skill and accuracy of both measurable and quantitative precipitation forecasts is 35-55% greater during the cool season than during the warm season. Also, the secular rate of improvement of the cool season precipitation forecasts is 50-110% greater than that of the warm season. This seasonal difference in performance reflects the relative difficulty of forecasting predominantly stratiform precipitation of the cool season and convective precipitation of the warm season.

Mapping Precipitation in the Lower Mekong River Basin and the U.S. Affiliated Pacific Islands

NASA Astrophysics Data System (ADS)

Lakshmi, V.; Sutton, J. R. P.; Bolten, J. D.

2017-12-01

Mapping and quantifying precipitation across varying temporal and spatial scales is of utmost importance in understanding, monitoring, and predicting flooding and drought. While there exists many in-situ precipitation gages that can accurately estimate precipitation in a given location, there are still many areas that lack in-situ gages. Many of these locations do not have precipitation gages because they are rural and/or topographically complex. The purpose of our research was to compare different remotely sensed satellite precipitation estimates with in-situ estimates across topographically complex and rural terrain within the United States Affiliated Pacific Islands (USAPI) and the Lower Mekong River Basin (LMRB). We utilize the publicly available Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Climate Data Record (CDR) from NOAA and two remotely sensed precipitation products from NASA; the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM). These precipitation estimates were compared with each other and to the available in-situ precipitation estimates from station gages. We also utilize NASA Landsat data to determine the land cover types of these study areas. Using the precipitation estimates, topography, and the land cover of the study areas, we were able to show areas experiencing differing amounts of rainfall and their agreement with in-situ estimates. Additionally, we study the seasonal and spatial trends in precipitation. These analyses can be used to help understand areas that are experience frequent flood or drought.

Study of accuracy of precipitation measurements using simulation method

NASA Astrophysics Data System (ADS)

Nagy, Zoltán; Lajos, Tamás; Morvai, Krisztián

2013-04-01

Hungarian Meteorological Service1 Budapest University of Technology and Economics2 Precipitation is one of the the most important meteorological parameters describing the state of the climate and to get correct information from trends, accurate measurements of precipitation is very important. The problem is that the precipitation measurements are affected by systematic errors leading to an underestimation of actual precipitation which errors vary by type of precipitaion and gauge type. It is well known that the wind speed is the most important enviromental factor that contributes to the underestimation of actual precipitation, especially for solid precipitation. To study and correct the errors of precipitation measurements there are two basic possibilities: · Use of results and conclusion of International Precipitation Measurements Intercomparisons; · To build standard reference gauges (DFIR, pit gauge) and make own investigation; In 1999 at the HMS we tried to achieve own investigation and built standard reference gauges But the cost-benefit ratio in case of snow (use of DFIR) was very bad (we had several winters without significant amount of snow, while the state of DFIR was continously falling) Due to the problem mentioned above there was need for new approximation that was the modelling made by Budapest University of Technology and Economics, Department of Fluid Mechanics using the FLUENT 6.2 model. The ANSYS Fluent package is featured fluid dynamics solution for modelling flow and other related physical phenomena. It provides the tools needed to describe atmospheric processes, design and optimize new equipment. The CFD package includes solvers that accurately simulate behaviour of the broad range of flows that from single-phase to multi-phase. The questions we wanted to get answer to are as follows: · How do the different types of gauges deform the airflow around themselves? · Try to give quantitative estimation of wind induced error. · How does the use

Statistical evaluation of the performance of gridded monthly precipitation products from reanalysis data, satellite estimates, and merged analyses over China

NASA Astrophysics Data System (ADS)

Deng, Xueliang; Nie, Suping; Deng, Weitao; Cao, Weihua

2018-04-01

In this study, we compared the following four different gridded monthly precipitation products: the National Centers for Environmental Prediction version 2 (NCEP-2) reanalysis data, the satellite-based Climate Prediction Center Morphing technique (CMORPH) data, the merged satellite-gauge Global Precipitation Climatology Project (GPCP) data, and the merged satellite-gauge-model data from the Beijing Climate Center Merged Estimation of Precipitation (BMEP). We evaluated the performances of these products using monthly precipitation observations spanning the period of January 2003 to December 2013 from a dense, national, rain gauge network in China. Our assessment involved several statistical techniques, including spatial pattern, temporal variation, bias, root-mean-square error (RMSE), and correlation coefficient (CC) analysis. The results show that NCEP-2, GPCP, and BMEP generally overestimate monthly precipitation at the national scale and CMORPH underestimates it. However, all of the datasets successfully characterized the northwest to southeast increase in the monthly precipitation over China. Because they include precipitation gauge information from the Global Telecommunication System (GTS) network, GPCP and BMEP have much smaller biases, lower RMSEs, and higher CCs than NCEP-2 and CMORPH. When the seasonal and regional variations are considered, NCEP-2 has a larger error over southern China during the summer. CMORPH poorly reproduces the magnitude of the precipitation over southeastern China and the temporal correlation over western and northwestern China during all seasons. BMEP has a lower RMSE and higher CC than GPCP over eastern and southern China, where the station network is dense. In contrast, BMEP has a lower CC than GPCP over western and northwestern China, where the gauge network is relatively sparse.

A QUANTITATIVE APPROACH FOR ESTIMATING EXPOSURE TO PESTICIDES IN THE AGRICULTURAL HEALTH STUDY

EPA Science Inventory

We developed a quantitative method to estimate chemical-specific pesticide exposures in a large prospective cohort study of over 58,000 pesticide applicators in North Carolina and Iowa. An enrollment questionnaire was administered to applicators to collect basic time- and inten...

Characterization of flood and precipitation events in Southwestern Germany and stochastic simulation of extreme precipitation (Project FLORIS-SV)

NASA Astrophysics Data System (ADS)

Florian, Ehmele; Michael, Kunz

2016-04-01

Several major flood events occurred in Germany in the past 15-20 years especially in the eastern parts along the rivers Elbe and Danube. Examples include the major floods of 2002 and 2013 with an estimated loss of about 2 billion Euros each. The last major flood events in the State of Baden-Württemberg in southwest Germany occurred in the years 1978 and 1993/1994 along the rivers Rhine and Neckar with an estimated total loss of about 150 million Euros (converted) each. Flood hazard originates from a combination of different meteorological, hydrological and hydraulic processes. Currently there is no defined methodology available for evaluating and quantifying the flood hazard and related risk for larger areas or whole river catchments instead of single gauges. In order to estimate the probable maximum loss for higher return periods (e.g. 200 years, PML200), a stochastic model approach is designed since observational data are limited in time and space. In our approach, precipitation is linearly composed of three elements: background precipitation, orographically-induces precipitation, and a convectively-driven part. We use linear theory of orographic precipitation formation for the stochastic precipitation model (SPM), which is based on fundamental statistics of relevant atmospheric variables. For an adequate number of historic flood events, the corresponding atmospheric conditions and parameters are determined in order to calculate a probability density function (pdf) for each variable. This method involves all theoretically possible scenarios which may not have happened, yet. This work is part of the FLORIS-SV (FLOod RISk Sparkassen Versicherung) project and establishes the first step of a complete modelling chain of the flood risk. On the basis of the generated stochastic precipitation event set, hydrological and hydraulic simulations will be performed to estimate discharge and water level. The resulting stochastic flood event set will be used to quantify the

Quantitative body DW-MRI biomarkers uncertainty estimation using unscented wild-bootstrap.

PubMed

Freiman, M; Voss, S D; Mulkern, R V; Perez-Rossello, J M; Warfield, S K

2011-01-01

We present a new method for the uncertainty estimation of diffusion parameters for quantitative body DW-MRI assessment. Diffusion parameters uncertainty estimation from DW-MRI is necessary for clinical applications that use these parameters to assess pathology. However, uncertainty estimation using traditional techniques requires repeated acquisitions, which is undesirable in routine clinical use. Model-based bootstrap techniques, for example, assume an underlying linear model for residuals rescaling and cannot be utilized directly for body diffusion parameters uncertainty estimation due to the non-linearity of the body diffusion model. To offset this limitation, our method uses the Unscented transform to compute the residuals rescaling parameters from the non-linear body diffusion model, and then applies the wild-bootstrap method to infer the body diffusion parameters uncertainty. Validation through phantom and human subject experiments shows that our method identify the regions with higher uncertainty in body DWI-MRI model parameters correctly with realtive error of -36% in the uncertainty values.

1,500 year quantitative reconstruction of winter precipitation in the Pacific Northwest

PubMed Central

Steinman, Byron A.; Abbott, Mark B.; Mann, Michael E.; Stansell, Nathan D.; Finney, Bruce P.

2012-01-01

Multiple paleoclimate proxies are required for robust assessment of past hydroclimatic conditions. Currently, estimates of drought variability over the past several thousand years are based largely on tree-ring records. We produced a 1,500-y record of winter precipitation in the Pacific Northwest using a physical model-based analysis of lake sediment oxygen isotope data. Our results indicate that during the Medieval Climate Anomaly (MCA) (900–1300 AD) the Pacific Northwest experienced exceptional wetness in winter and that during the Little Ice Age (LIA) (1450–1850 AD) conditions were drier, contrasting with hydroclimatic anomalies in the desert Southwest and consistent with climate dynamics related to the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). These findings are somewhat discordant with drought records from tree rings, suggesting that differences in seasonal sensitivity between the two proxies allow a more compete understanding of the climate system and likely explain disparities in inferred climate trends over centennial timescales. PMID:22753510

Quantitative reconstruction of precipitation and runoff during MIS 5a, MIS 3a, and Holocene, arid China

NASA Astrophysics Data System (ADS)

Liu, Yuan; Li, Yu

2017-11-01

Marine oxygen isotope stage 5a (MIS 5a), MIS 3a, and Holocene were highlighted periods in paleoclimate studies. Many scientists have published a great number of studies in this regard, but they paid more attention to qualitative research, and there was often a lack of quantitative data. In this paper, based on chronological evidence from a paleolake in arid China, MIS 5a, MIS 3a, and Holocene lake area, the precipitation of the drainage area and the runoff of the inflowing rivers of the lake were reconstructed with ArcGIS spatial analysis software and the improved water and energy balance model which was calibrated by modern meteorological and hydrological data in the Shiyang River drainage basin. The results showed that the paleolake areas were 1824, 1124, and 628 km2 for MIS 5a, MIS 3a, and Holocene; meanwhile, the paleoprecipitation and runoff were 293.992-297.433, 271.105-274.294, and 249.431-252.373 mm and 29.103 × 108-29.496 × 108, 18.810 × 108-18.959 × 108, and 10.637 × 108-10.777 × 108 mm, respectively. The quantitative data can help us not only strengthen the understanding of paleoclimatic characteristics but also recognize the complexity and diversity of the climate system.

Investigation of mesoscale precipitation processes in the Carolinas using a radar-based climatology

NASA Astrophysics Data System (ADS)

Boyles, Ryan Patrick

The complex topography, shoreline, soils, and land use patterns makes the Carolinas a unique location to study mesoscale processes. Using gage-calibrated radar estimates and a series of numerical model simulations, warm season mesoscale precipitation patterns are analyzed over the Carolinas. Gage-calibrated radar precipitation estimates are compared with surface gage observations. Stage IV estimates generally compared better than Stage II estimates, but some Stage II and Stage IV estimates have gross errors during autumn, winter, and spring seasons. Analysis of days when sea breeze is observed suggests that sea breeze induced precipitation occurs on nearly 40% of days in June, July, and August, but only 18% in May and 6% of days in April. Precipitation on days with sea breeze convection can contribute to over 50% of seasonal precipitation. Rainfall associated with sea breeze is generally maximized along east-facing shores 10-20 km inland, and minimized along south-facing shores in North Carolina. The shape of the shoreline along Cape Fear is associated with a local precipitation maximum that may be caused by the convergence of two sea breeze fronts from the south and east shores. Differential heating associated with contrasting soils along the Carolina Sandhills is suggested as a mechanism for enhancement in local precipitation. A high-resolution summer precipitation climatology suggests that precipitation is enhanced along the Sandhills region in both wet and dry years. Analysis of four numerical simulations suggests that contrasts in soils over the Carolinas Sandhills dominates over vegetation contrasts to produce heat flux gradients and a convergence zone along the sand-to-clay transition. Orographically induced precipitation is consistently observed in the summer, and appears to be isolated along windward slopes at 20km--40km from the ridge line. Amounts over external ridges are generally 50-100% higher than amounts observed over the foothills. Precipitation

Modelling river discharge and precipitation from estuarine salinity in the northern Chesapeake Bay: Application to Holocene palaeoclimate

USGS Publications Warehouse

Saenger, C.; Cronin, T.; Thunell, R.; Vann, C.

2006-01-01

Long-term chronologies of precipitation can provide a baseline against which twentieth-century trends in rainfall can be evaluated in terms of natural variability and anthropogenic influence. However, there are relatively few methods to quantitatively reconstruct palaeoprecipitation and river discharge compared with proxies of other climatic factors, such as temperature. We developed autoregressive and least squares statistical models relating Chesapeake Bay salinity to river discharge and regional precipitation records. Salinity in northern and central parts of the modern Chesapeake Bay is influenced largely by seasonal, interannual and decadal variations in Susquehanna River discharge, which in turn are controlled by regional precipitation patterns. A power regressive discharge model and linear precipitation model exhibit well-defined decadal variations in peak discharge and precipitation. The utility of the models was tested by estimating Holocene palaeoprecipitation and Susquehanna River palaeodischarge, as indicated by isotopically derived palaeosalinity reconstructions from Chesapeake Bay sediment cores. Model results indicate that the early-mid Holocene (7055-5900 yr BP) was drier than the late Holocene (1500 yr BP - present), the 'Mediaeval Warm Period' (MWP) (1200-600 yr BP) was drier than the 'Little Ice Age' (LIA) (500-100 yr BP), and the twentieth century experienced extremes in precipitation possibly associated with changes in ocean-atmosphere teleconnections. ?? 2006 Edward Arnold (Publishers) Ltd.

Evaluation of quantitative precipitation forecasts by TIGGE ensembles for south China during the presummer rainy season

NASA Astrophysics Data System (ADS)

Huang, Ling; Luo, Yali

2017-08-01

Based on The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) data set, this study evaluates the ability of global ensemble prediction systems (EPSs) from the European Centre for Medium-Range Weather Forecasts (ECMWF), U.S. National Centers for Environmental Prediction, Japan Meteorological Agency (JMA), Korean Meteorological Administration, and China Meteorological Administration (CMA) to predict presummer rainy season (April-June) precipitation in south China. Evaluation of 5 day forecasts in three seasons (2013-2015) demonstrates the higher skill of probability matching forecasts compared to simple ensemble mean forecasts and shows that the deterministic forecast is a close second. The EPSs overestimate light-to-heavy rainfall (0.1 to 30 mm/12 h) and underestimate heavier rainfall (>30 mm/12 h), with JMA being the worst. By analyzing the synoptic situations predicted by the identified more skillful (ECMWF) and less skillful (JMA and CMA) EPSs and the ensemble sensitivity for four representative cases of torrential rainfall, the transport of warm-moist air into south China by the low-level southwesterly flow, upstream of the torrential rainfall regions, is found to be a key synoptic factor that controls the quantitative precipitation forecast. The results also suggest that prediction of locally produced torrential rainfall is more challenging than prediction of more extensively distributed torrential rainfall. A slight improvement in the performance is obtained by shortening the forecast lead time from 30-36 h to 18-24 h to 6-12 h for the cases with large-scale forcing, but not for the locally produced cases.

Quantifying Energetic Electron Precipitation And Its Effect on Atmospheric Chemistry

NASA Astrophysics Data System (ADS)

Huang, C. L.; Spence, H. E.; Smith, S. S.; Duderstadt, K. A.; Boyd, A. J.; Geoffrey, R.; Blake, J. B.; Fennell, J. F.; Claudepierre, S. G.; Turner, D. L.; Crew, A. B.; Klumpar, D. M.; Shumko, M.; Johnson, A.; Sample, J. G.

2017-12-01

In this study we quantify the total radiation belt electron loss through precipitation into the atmosphere, and simulate the electrons' contribution to changing the atmospheric composition. We use total radiation belt electron content (TRBEC) calculated from Van Allen Probes ECT/MagEIS data to estimate the precipitation during electron loss events. The new TRBEC index is a high-level quantity for monitoring the entire radiation belt and has the benefit of removing both internal transport and the adiabatic effect. To assess the electron precipitation rate, we select TRBEC loss events that show no outward transport in the phase space density data in order to exclude drift magnetopause loss. Then we use FIREBIRD data to estimate and constrain the precipitation loss when it samples near the loss cone. Finally, we estimate the impact of electron precipitation on the composition of the upper and middle atmosphere using global climate simulations.

New method to estimate paleoprecipitation using fossil amphibians and reptiles and the middle and late Miocene precipitation gradients in Europe

NASA Astrophysics Data System (ADS)

Böhme, M.; Ilg, A.; Ossig, A.; Küchenhoff, H.

2006-06-01

Existing methods for determining paleoprecipitation are subject to large errors (±350 400 mm or more using mammalian proxies), or are restricted to wet climate systems due to their strong facies dependence (paleobotanical proxies). Here we describe a new paleoprecipitation tool based on an indexing of ecophysiological groups within herpetological communities. In recent communities these indices show a highly significant correlation to annual precipitation (r2 = 0.88), and yield paleoprecipitation estimates with average errors of ±250 280 mm. The approach was validated by comparison with published paleoprecipitation estimates from other methods. The method expands the application of paleoprecipitation tools to dry climate systems and in this way contributes to the establishment of a more comprehensive paleoprecipitation database. This method is applied to two high-resolution time intervals from the European Neogene: the early middle Miocene (early Langhian) and the early late Miocene (early Tortonian). The results indicate that both periods show significant meridional precipitation gradients in Europe, these being stronger in the early Langhian (threefold decrease toward the south) than in the early Tortonian (twofold decrease toward the south). This pattern indicates a strengthening of climatic belts during the middle Miocene climatic optimum due to Southern Hemisphere cooling and an increased contribution of Arctic low-pressure cells to the precipitation from the late Miocene onward due to Northern Hemisphere cooling.

Proficiency testing as a basis for estimating uncertainty of measurement: application to forensic alcohol and toxicology quantitations.

PubMed

Wallace, Jack

2010-05-01

While forensic laboratories will soon be required to estimate uncertainties of measurement for those quantitations reported to the end users of the information, the procedures for estimating this have been little discussed in the forensic literature. This article illustrates how proficiency test results provide the basis for estimating uncertainties in three instances: (i) For breath alcohol analyzers the interlaboratory precision is taken as a direct measure of uncertainty. This approach applies when the number of proficiency tests is small. (ii) For blood alcohol, the uncertainty is calculated from the differences between the laboratory's proficiency testing results and the mean quantitations determined by the participants; this approach applies when the laboratory has participated in a large number of tests. (iii) For toxicology, either of these approaches is useful for estimating comparability between laboratories, but not for estimating absolute accuracy. It is seen that data from proficiency tests enable estimates of uncertainty that are empirical, simple, thorough, and applicable to a wide range of concentrations.

Aerosol loading impact on Asian monsoon precipitation patterns

NASA Astrophysics Data System (ADS)

Biondi, Riccardo; Cagnazzo, Chiara; Costabile, Francesca; Cairo, Francesco

2017-04-01

Solar light absorption by aerosols such as black carbon and dust assume a key role in driving the precipitation patterns in the Indian subcontinent. The aerosols stack up against the foothills of the Himalayas in the pre-monsoon season and several studies have already demonstrated that this can cause precipitation anomalies during summer. Despite its great significance in climate change studies, the link between absorbing aerosols loading and precipitation patterns remains highly uncertain. The main challenge for this kind of studies is to find consistent and reliable datasets. Several aerosol time series are available from satellite and ground based instruments and some precipitation datasets from satellite sensors, but they all have different time/spatial resolution and they use different assumptions for estimating the parameter of interest. We have used the aerosol estimations from the Ozone Monitoring Instrument (OMI), the Along-Track Scanning Radiometer (AATSR) and the MODerate resolution Imaging Spectroradiometer (MODIS) and validated them against the Aerosol Robotic Network (AERONET) measurements in the Indian area. The precipitation has been analyzed by using the Tropical Rainfall Measuring Mission (TRMM) estimations and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). From our results it is evident the discrepancy between the aerosol loading on the area of interest from the OMI, AATSR, and MODIS, but even between 3 different algorithms applied to the MODIS data. This uncertainty does not allow to clearly distinguishing high aerosol loading years from low aerosol loading years except in a couple of cases where all the estimations agree. Similar issues are also present in the precipitation estimations from TRMM and MERRA-2. However, all the aerosol datasets agree in defining couples of consecutive years with a large gradient of aerosol loading. Based on this assumption we have compared the precipitation anomalies and

The Day-1 GPM Combined Precipitation Algorithm: IMERG

NASA Astrophysics Data System (ADS)

Huffman, G. J.; Bolvin, D. T.; Braithwaite, D.; Hsu, K.; Joyce, R.; Kidd, C.; Sorooshian, S.; Xie, P.

2012-12-01

The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) algorithm will provide the at-launch combined-sensor precipitation dataset being produced by the U.S. GPM Science Team. IMERG is being developed as a unified U.S. algorithm that takes advantage of strengths in three current U.S. algorithms: - the TRMM Multi-satellite Precipitation Analysis (TMPA), which addresses inter-satellite calibration of precipitation estimates and monthly scale combination of satellite and gauge analyses; - the CPC Morphing algorithm with Kalman Filtering (KF-CMORPH), which provides quality-weighted time interpolation of precipitation patterns following storm motion; and - the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS), which provides a neural-network-based scheme for generating microwave-calibrated precipitation estimates from geosynchronous infrared brightness temperatures, and filters out some non-raining cold clouds. The goal is to provide a long-term, fine-scale record of global precipitation from the entire constellation of precipitation-relevant satellite sensors, with input from surface precipitation gauges. The record will begin January 1998 at the start of the Tropical Rainfall Measuring Mission (TRMM) and extend as GPM records additional data. Although homogeneity is considered desirable, the use of diverse and evolving data sources works against the strict long-term homogeneity that characterizes a Climate Data Record (CDR). This talk will briefly review the design requirements for IMERG, including multiple runs at different latencies (most likely around 4 hours, 12 hours, and 2 months after observation time), various intermediate data fields as part of the IMERG data file, and the plans to bring up IMERG with calibration by TRMM initially, transitioning to GPM when its individual-sensor precipitation algorithms are fully functional

Comparison of satellite precipitation products with Q3 over the CONUS

NASA Astrophysics Data System (ADS)

Wang, J.; Petersen, W. A.; Wolff, D. B.; Kirstetter, P. E.

2016-12-01

The Global Precipitation Measurement (GPM) is an international satellite mission that provides a new-generation of global precipitation observations. A wealth of precipitation products have been generated since the launch of the GPM Core Observatory in February of 2014. However, the accuracy of the satellite-based precipitation products is affected by discrete temporal sampling and remote spaceborne retrieval algorithms. The GPM Ground Validation (GV) program is currently underway to independently verify the satellite precipitation products, which can be carried out by comparing satellite products with ground measurements. This study compares four Day-1 GPM surface precipitation products derived from the GPM Microwave Imager (GMI), Ku-band Precipitation Radar (KU), Dual-Frequency Precipitation Radar (DPR) and DPR-GMI CoMBined (CMB) algorithms, as well as the near-real-time Integrated Multi-satellitE Retrievals for GPM (IMERG) Late Run product and precipitation retrievals from Microwave Humidity Sounders (MHS) flown on NOAA and METOPS satellites, with the NOAA Multi-Radar Multi-Sensor suite (MRMS; now called "Q3"). The comparisons are conducted over the conterminous United States (CONUS) at various spatial and temporal scales with respect to different precipitation intensities, and filtered with radar quality index (RQI) thresholds and precipitation types. Various versions of GPM products are evaluated against Q3. The latest Version-04A GPM products are in reasonably good overall agreement with Q3. Based on the mission-to-date (March 2014 - May 2016) data from all GPM overpasses, the biases relative to Q3 for GMI and DPR precipitation estimates at 0.5o resolution are negative, whereas the biases for CMB and KU precipitation estimates are positive. Based on all available data (March 2015 - April 2016 at this writing), the CONUS-averaged near-real-time IMERG Late Run hourly precipitation estimate is about 46% higher than Q3. Preliminary comparison of 1-year (2015) MHS

An Update on Oceanic Precipitation Rate and its Zonal Distribution in Light of Advanced Observations from Space

NASA Technical Reports Server (NTRS)

Behrangi, Ali; Stephens, Graeme; Adler, Robert F.; Huffman, George J.; Lambrigsten, Bjorn; Lebstock, Matthew

2014-01-01

This study contributes to the estimation of the global mean and zonal distribution of oceanic precipitation rate using complementary information from advanced precipitation measuring sensors and provides an independent reference to assess current precipitation products. Precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and CloudSat cloud profiling radar (CPR) were merged, as the two complementary sensors yield an unprecedented range of sensitivity to quantify rainfall from drizzle through the most intense rates. At higher latitudes, where TRMM PR does not exist, precipitation estimates from Aqua's Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) complemented CloudSat CPR to capture intense precipitation rates. The high sensitivity of CPR allows estimation of snow rate, an important type of precipitation at high latitudes, not directly observed in current merged precipitation products. Using the merged precipitation estimate from the CloudSat, TRMM, and Aqua platforms (this estimate is abbreviated to MCTA), the authors' estimate for 3-yr (2007-09) nearglobal (80degS-80degN) oceanic mean precipitation rate is approx. 2.94mm/day. This new estimate of mean global ocean precipitation is about 9% higher than that of the corresponding Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) value (2.68mm/day) and about 4% higher than that of the Global Precipitation Climatology Project (GPCP; 2.82mm/day). Furthermore, MCTA suggests distinct differences in the zonal distribution of precipitation rate from that depicted in GPCPand CMAP, especially in the Southern Hemisphere.

Characterization of precipitation features over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.

2013-12-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, surface observations, and models to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets of TMPA 3B42, CMORPH, and PERSIANN. The satellite based QPEs are compared over the concurrent period with the NCEP Stage IV product, which is a near real time product providing precipitation data at the hourly temporal scale gridded at a nominal 4-km spatial resolution. In addition, remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model), which provides gridded precipitation estimates that are used as a baseline for multi-sensor QPE products comparison. The comparisons are performed at the annual, seasonal, monthly, and daily scales with focus on selected river basins (Southeastern US, Pacific Northwest, Great Plains). While, unconditional annual rain rates present a satisfying agreement between all products, results suggest that satellite QPE datasets exhibit important biases in particular at higher rain rates (≥4 mm/day). Conversely, on seasonal scales differences between remotely sensed data and ground surface observations can be greater than 50% and up to 90% for low daily accumulation (≤1 mm/day) such as in the Western US (summer) and Central US (winter). The conditional analysis performed using different daily rainfall accumulation thresholds (from low rainfall intensity to intense precipitation) shows that while intense events measured at the ground are infrequent (around 2% for daily accumulation above 2 inches/day), remotely sensed products displayed differences from 20-50% and up to 90-100%. A discussion on the impact of differing spatial and temporal resolutions with respect to the datasets ability to capture extreme

Modelling probabilities of heavy precipitation by regional approaches

NASA Astrophysics Data System (ADS)

Gaal, L.; Kysely, J.

2009-09-01

Extreme precipitation events are associated with large negative consequences for human society, mainly as they may trigger floods and landslides. The recent series of flash floods in central Europe (affecting several isolated areas) on June 24-28, 2009, the worst one over several decades in the Czech Republic as to the number of persons killed and the extent of damage to buildings and infrastructure, is an example. Estimates of growth curves and design values (corresponding e.g. to 50-yr and 100-yr return periods) of precipitation amounts, together with their uncertainty, are important in hydrological modelling and other applications. The interest in high quantiles of precipitation distributions is also related to possible climate change effects, as climate model simulations tend to project increased severity of precipitation extremes in a warmer climate. The present study compares - in terms of Monte Carlo simulation experiments - several methods to modelling probabilities of precipitation extremes that make use of ‘regional approaches’: the estimation of distributions of extremes takes into account data in a ‘region’ (‘pooling group’), in which one may assume that the distributions at individual sites are identical apart from a site-specific scaling factor (the condition is referred to as ‘regional homogeneity’). In other words, all data in a region - often weighted in some way - are taken into account when estimating the probability distribution of extremes at a given site. The advantage is that sampling variations in the estimates of model parameters and high quantiles are to a large extent reduced compared to the single-site analysis. We focus on the ‘region-of-influence’ (ROI) method which is based on the identification of unique pooling groups (forming the database for the estimation) for each site under study. The similarity of sites is evaluated in terms of a set of site attributes related to the distributions of extremes. The issue of

Investigation of EMIC wave scattering as the cause for the BARREL 17 January 2013 relativistic electron precipitation event: A quantitative comparison of simulation with observations

DOE PAGES

Li, Zan; Millan, Robyn M.; Hudson, Mary K.; ...

2014-12-23

Electromagnetic ion cyclotron (EMIC) waves were observed at multiple observatory locations for several hours on 17 January 2013. During the wave activity period, a duskside relativistic electron precipitation (REP) event was observed by one of the Balloon Array for Radiation belt Relativistic Electron Losses (BARREL) balloons and was magnetically mapped close to Geostationary Operational Environmental Satellite (GOES) 13. We simulate the relativistic electron pitch angle diffusion caused by gyroresonant interactions with EMIC waves using wave and particle data measured by multiple instruments on board GOES 13 and the Van Allen Probes. We show that the count rate, the energy distribution,more » and the time variation of the simulated precipitation all agree very well with the balloon observations, suggesting that EMIC wave scattering was likely the cause for the precipitation event. The event reported here is the first balloon REP event with closely conjugate EMIC wave observations, and our study employs the most detailed quantitative analysis on the link of EMIC waves with observed REP to date.« less

Investigation of EMIC wave scattering as the cause for the BARREL 17 January 2013 relativistic electron precipitation event: A quantitative comparison of simulation with observations

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

Li, Zan; Millan, Robyn M.; Hudson, Mary K.

Electromagnetic ion cyclotron (EMIC) waves were observed at multiple observatory locations for several hours on 17 January 2013. During the wave activity period, a duskside relativistic electron precipitation (REP) event was observed by one of the Balloon Array for Radiation belt Relativistic Electron Losses (BARREL) balloons and was magnetically mapped close to Geostationary Operational Environmental Satellite (GOES) 13. We simulate the relativistic electron pitch angle diffusion caused by gyroresonant interactions with EMIC waves using wave and particle data measured by multiple instruments on board GOES 13 and the Van Allen Probes. We show that the count rate, the energy distribution,more » and the time variation of the simulated precipitation all agree very well with the balloon observations, suggesting that EMIC wave scattering was likely the cause for the precipitation event. The event reported here is the first balloon REP event with closely conjugate EMIC wave observations, and our study employs the most detailed quantitative analysis on the link of EMIC waves with observed REP to date.« less

Applications of Precipitation Feature Databases from GPM core and constellation Satellites

NASA Astrophysics Data System (ADS)

Liu, C.

2017-12-01

Using the observations from Global Precipitation Mission (GPM) core and constellation satellites, global precipitation was quantitatively described from the perspective of precipitation systems and their properties. This presentation will introduce the development of precipitation feature databases, and several scientific questions that have been tackled using this database, including the topics of global snow precipitation, extreme intensive convection, hail storms, extreme precipitation, and microphysical properties derived with dual frequency radars at the top of convective cores. As more and more observations of constellation satellites become available, it is anticipated that the precipitation feature approach will help to address a large variety of scientific questions in the future. For anyone who is interested, all the current precipitation feature databases are freely open to public at: http://atmos.tamucc.edu/trmm/.

Performance of high-resolution satellite precipitation products over China

NASA Astrophysics Data System (ADS)

Shen, Y.; Xiong, A.; Wang, Y.; Xie, P.; Precipitation Merge Team

2010-12-01

A gauge-based analysis of hourly precipitation is constructed on a 0.25°latitude/ longitude grid over China for a 3 year period from 2005 to 2007 by interpolating gauge reports from ~2000 stations (fig.1) collected and quality controlled by the National Meteorological Information Center of the China Meteorological Administration. Gauge-based precipitation analysis is applied to examine the performance of six high-resolution satellite precipitation estimates, including Joyce et al.’s (2004) Climate Prediction Center Morphing Technique (CMORPH) and the arithmetic mean of the microwave estimates used in CMORPH; Huffman et al.’s (2007) Tropical Rainfall Measuring Mission (TRMM) precipitation product 3B42 and its real-time version 3B42RT; Turk et al.’s (2004) Naval Research Laboratory blended product; and Hsu et al.’s (1997) Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Network (PERSIANN). Our results showed the following: (1) all six satellite products are capable of capturing the overall spatial distribution and temporal variations of precipitation reasonably well; (2) performance of the satellite products varies for different regions and different precipitation regimes, with better comparison statistics observed over wet regions and for warm seasons; (3) products based solely on satellite observations present regionally and seasonally varying biases, while the gauge-adjustment procedures applied in TRMM 3B42 remove the large-scale bias almost completely; (4) CMORPH exhibits the best performance in depicting the spatial pattern and temporal variations of precipitation; and (5) both the relative magnitude and the phase of the warm season precipitation over China are estimated quite well, but the early morning peak associated with the Mei-Yu rainfall over central eastern China is substantially under-estimated by all satellite products. The work reported in this paper is an integral part of our efforts to construct an analysis

Gauge Adjusted Global Satellite Mapping of Precipitation (GSMAP_GAUGE)

NASA Astrophysics Data System (ADS)

Mega, T.; Ushio, T.; Yoshida, S.; Kawasaki, Z.; Kubota, T.; Kachi, M.; Aonashi, K.; Shige, S.

2013-12-01

Precipitation is one of the most important parameters on the earth system, and the global distribution of precipitation and its change are essential data for modeling the water cycle, maintaining the ecosystem environment, agricultural production, improvements of the weather forecast precision, flood warning and so on. The GPM (Global Precipitation Measurement) project is led mainly by the United States and Japan, and is now being actively promoted in Europe, France, India, and China with international cooperation. In this project, the microwave radiometers observing microwave emission from rain will be placed on many low-orbit satellites, to reduce the interval to about 3 hours in observation time for each location on the earth. However, the problem of sampling error arises if the global precipitation estimates are less than three hours. Therefore, it is necessary to utilize a gap-filling technique to generate precipitation maps with high temporal resolution, which is quite important for operational uses such as flash flood warning systems. Global Satellite Mapping of Precipitation (GSMaP) project was established by the Japan Science and Technology Agency (JST) in 2002 to produce global precipitation products with high resolution and high precision from not only microwave radiometers but also geostationary infrared radiometers. Currently, the GSMaP_MVK product has been successfully producing fairly good pictures in near real time, and the products shows a comparable score compared with other high-resolution precipitation systems (Ushio et al. 2009 and Kubota et al. 2009). However some evaluations particularly of the operational applications show the tendency of underestimation compared to some ground based observations for the cases showing extremely high precipitation rates. This is partly because the spatial and temporal samplings of the satellite estimates are different from that of the ground based estimates. The microwave imager observes signals from

Pollen-based temperature and precipitation inferences for the montane forest of Mt. Kilimanjaro during the last Glacial and the Holocene

NASA Astrophysics Data System (ADS)

Schüler, L.; Hemp, A.; Behling, H.

2014-01-01

The relationship between modern pollen-rain taxa and measured climate variables was explored along the elevational gradient of the southern slope of Mt. Kilimanjaro, Tanzania. Pollen assemblages in 28 pollen traps positioned on 14 montane forest vegetation plots were identified and their relationship with climate variables was examined using multivariate statistical methods. Canonical correspondence analysis revealed that the mean annual temperature, mean annual precipitation and minimum temperature each account for significant fractions of the variation in pollen taxa. A training set of 107 modern pollen taxa was used to derive temperature and precipitation transfer functions based on pollen subsets using weighted-averaging-partial-least-squares (WA-PLS) techniques. The transfer functions were then applied to a fossil pollen record from the montane forest of Mt. Kilimanjaro and the climate parameter estimates for the Late Glacial and the Holocene on Mt. Kilimanjaro were inferred. Our results present the first quantitatively reconstructed temperature and precipitation estimates for Mt Kilimanjaro and give highly interesting insights into the past 45 000 yr of climate dynamics in tropical East Africa. The climate reconstructions are consistent with the interpretation of pollen data in terms of vegetation and climate history of afro-montane forest in East Africa. Minimum temperatures above the frostline as well as increased precipitation turn out to be crucial for the development and expansion of montane forest during the Holocene. In contrast, consistently low minimum temperatures as well as about 25% drier climate conditions prevailed during the pre LGM, which kept the montane vegetation composition in a stable state. In prospective studies, the quantitative climate reconstruction will be improved by additional modern pollen rain data, especially from lower elevations with submontane dry forests and colline savanna vegetation in order to extend the reference

Risk assessment of precipitation extremes in northern Xinjiang, China

NASA Astrophysics Data System (ADS)

Yang, Jun; Pei, Ying; Zhang, Yanwei; Ge, Quansheng

2018-05-01

This study was conducted using daily precipitation records gathered at 37 meteorological stations in northern Xinjiang, China, from 1961 to 2010. We used the extreme value theory model, generalized extreme value (GEV) and generalized Pareto distribution (GPD), statistical distribution function to fit outputs of precipitation extremes with different return periods to estimate risks of precipitation extremes and diagnose aridity-humidity environmental variation and corresponding spatial patterns in northern Xinjiang. Spatiotemporal patterns of daily maximum precipitation showed that aridity-humidity conditions of northern Xinjiang could be well represented by the return periods of the precipitation data. Indices of daily maximum precipitation were effective in the prediction of floods in the study area. By analyzing future projections of daily maximum precipitation (2, 5, 10, 30, 50, and 100 years), we conclude that the flood risk will gradually increase in northern Xinjiang. GEV extreme value modeling yielded the best results, proving to be extremely valuable. Through example analysis for extreme precipitation models, the GEV statistical model was superior in terms of favorable analog extreme precipitation. The GPD model calculation results reflect annual precipitation. For most of the estimated sites' 2 and 5-year T for precipitation levels, GPD results were slightly greater than GEV results. The study found that extreme precipitation reaching a certain limit value level will cause a flood disaster. Therefore, predicting future extreme precipitation may aid warnings of flood disaster. A suitable policy concerning effective water resource management is thus urgently required.

Bridging the Global Precipitation and Soil Moisture Active Passive Missions: Variability of Microwave Surface Emissivity from In situ and Remote Sensing Perspectives

NASA Astrophysics Data System (ADS)

Zheng, Y.; Kirstetter, P.; Hong, Y.; Turk, J.

2016-12-01

The overland precipitation retrievals from satellite passive microwave (PMW) sensors such as the Global Precipitation Mission (GPM) microwave imager (GMI) are impacted by the land surface emissivity. The estimation of PMW emissivity faces challenges because it is highly variable under the influence of surface properties such as soil moisture, surface roughness and vegetation. This study proposes an improved quantitative understanding of the relationship between the emissivity and surface parameters. Surface parameter information is obtained through (i) in-situ measurements from the International Soil Moisture Network and (ii) satellite measurements from the Soil Moisture Active and Passive mission (SMAP) which provides global scale soil moisture estimates. The variation of emissivity is quantified with soil moisture, surface temperature and vegetation at various frequencies/polarization and over different types of land surfaces to sheds light into the processes governing the emission of the land. This analysis is used to estimate the emissivity under rainy conditions. The framework built with in-situ measurements serves as a benchmark for satellite-based analyses, which paves a way toward global scale emissivity estimates using SMAP.

Systematic Anomalies in Rainfall Intensity Estimates Over the Continental U.S.

NASA Technical Reports Server (NTRS)

Amitai, Eyal; Petersen, Walter Arthur; Llort, Xavier; Vasiloff, Steve

2010-01-01

Rainfall intensities during extreme events over the continental U.S. are compared for several advanced radar products. These products include: 1) TRMM spaceborne radar (PR) near surface estimates; 2) NOAA Next-Generation Quantitative Precipitation Estimation (QPE) very high-resolution (1 km) radar-only national mosaics (Q2); 3) very high-resolution instantaneous gauge adjusted radar national mosaics, which we have developed by applying gauge correction on the Q2 instantaneous radar-only products; and 4) several independent C-band dual-polarimetric radar-estimated rainfall samples collected with the ARMOR radar in northern Alabama. Though accumulated rainfall amounts are often similar, we find the satellite and the ground radar rain rate pdfs to be quite different. PR pdfs are shifted towards lower rain rates, implying a much larger stratiform/convective rain ratio than do ground radar products. The shift becomes more evident during strong continental convective storms and much less during tropical storms. Resolving the continental/maritime regime behavior and other large discrepancies between the products presents an important challenge. A challenge to improve our understanding of the source of the discrepancies, to determine the uncertainties of the estimates, and to improve remote-sensing estimates of precipitation in general.

Development of a precipitation-area curve for warning criteria of short-duration flash flood

NASA Astrophysics Data System (ADS)

Bae, Deg-Hyo; Lee, Moon-Hwan; Moon, Sung-Keun

2018-01-01

This paper presents quantitative criteria for flash flood warning that can be used to rapidly assess flash flood occurrence based on only rainfall estimates. This study was conducted for 200 small mountainous sub-catchments of the Han River basin in South Korea because South Korea has recently suffered many flash flood events. The quantitative criteria are calculated based on flash flood guidance (FFG), which is defined as the depth of rainfall of a given duration required to cause frequent flooding (1-2-year return period) at the outlet of a small stream basin and is estimated using threshold runoff (TR) and antecedent soil moisture conditions in all sub-basins. The soil moisture conditions were estimated during the flooding season, i.e., July, August and September, over 7 years (2002-2009) using the Sejong University Rainfall Runoff (SURR) model. A ROC (receiver operating characteristic) analysis was used to obtain optimum rainfall values and a generalized precipitation-area (P-A) curve was developed for flash flood warning thresholds. The threshold function was derived as a P-A curve because the precipitation threshold with a short duration is more closely related to basin area than any other variables. For a brief description of the P-A curve, generalized thresholds for flash flood warnings can be suggested for rainfall rates of 42, 32 and 20 mm h-1 in sub-basins with areas of 22-40, 40-100 and > 100 km2, respectively. The proposed P-A curve was validated based on observed flash flood events in different sub-basins. Flash flood occurrences were captured for 9 out of 12 events. This result can be used instead of FFG to identify brief flash flood (less than 1 h), and it can provide warning information to decision-makers or citizens that is relatively simple, clear and immediate.

Evolution of Tropical and Extratropical Precipitation Anomalies During the 1997 to 1999 ENSO Cycle

NASA Technical Reports Server (NTRS)

Curtis, Scott; Adler, Robert; Huffman, George; Nelkin, Eric; Bolvin, David; Einaudi, Franco (Technical Monitor)

2000-01-01

The 1997-1999 ENSO period was very powerful, but also well observed. Multiple satellite rainfall estimates combined with gauge observations allow for a quantitative analysis of precipitation anomalies in the tropics and elsewhere accompanying the 1997-99 ENSO cycle. An examination of the evolution of the El Nino and accompanying precipitation anomalies revealed that a dry Maritime Continent preceded the formation of positive SST anomalies in the eastern Pacific Ocean. 30-60 day oscillations in the winter of 1996/97 may have contributed to this lag relationship. Furthermore, westerly wind burst events may have maintained the drought over the Maritime Continent. The warming of the equatorial Pacific was then followed by an increase in convection. A rapid transition from El Nino to La Nina occurred in May 1998, but as early as October-November 1997 precipitation indices captured substantial changes in Pacific rainfall anomalies. The global precipitation patterns for this event were in good agreement with the strong consistent ENSO-related precipitation signals identified in earlier studies. Differences included a shift in precipitation anomalies over Africa during the 1997-98 El Nino and unusually wet conditions over northeast Australia during the later stages of the El Nino. Also, the typically wet region in the north tropical Pacific was mostly dry during the 1998-99 La Nina. Reanalysis precipitation was compared to observations during this time period and substantial differences were noted. In particular, the model had a bias towards positive precipitation anomalies and the magnitudes of the anomalies in the equatorial Pacific were small compared to the observations. Also, the evolution of the precipitation field, including the drying of the Maritime Continent and eastward progression of rainfall in the equatorial Pacific was less pronounced for the model compared to the observations.

Assessing Hourly Precipitation Forecast Skill with the Fractions Skill Score

NASA Astrophysics Data System (ADS)

Zhao, Bin; Zhang, Bo

2018-02-01

Statistical methods for category (yes/no) forecasts, such as the Threat Score, are typically used in the verification of precipitation forecasts. However, these standard methods are affected by the so-called "double-penalty" problem caused by slight displacements in either space or time with respect to the observations. Spatial techniques have recently been developed to help solve this problem. The fractions skill score (FSS), a neighborhood spatial verification method, directly compares the fractional coverage of events in windows surrounding the observations and forecasts. We applied the FSS to hourly precipitation verification by taking hourly forecast products from the GRAPES (Global/Regional Assimilation Prediction System) regional model and quantitative precipitation estimation products from the National Meteorological Information Center of China during July and August 2016, and investigated the difference between these results and those obtained with the traditional category score. We found that the model spin-up period affected the assessment of stability. Systematic errors had an insignificant role in the fraction Brier score and could be ignored. The dispersion of observations followed a diurnal cycle and the standard deviation of the forecast had a similar pattern to the reference maximum of the fraction Brier score. The coefficient of the forecasts and the observations is similar to the FSS; that is, the FSS may be a useful index that can be used to indicate correlation. Compared with the traditional skill score, the FSS has obvious advantages in distinguishing differences in precipitation time series, especially in the assessment of heavy rainfall.

The concurrent multiplicative-additive approach for gauge-radar/satellite multisensor precipitation estimates

NASA Astrophysics Data System (ADS)

Garcia-Pintado, J.; Barberá, G. G.; Erena Arrabal, M.; Castillo, V. M.

2010-12-01

Objective analysis schemes (OAS), also called ``succesive correction methods'' or ``observation nudging'', have been proposed for multisensor precipitation estimation combining remote sensing data (meteorological radar or satellite) with data from ground-based raingauge networks. However, opposite to the more complex geostatistical approaches, the OAS techniques for this use are not optimized. On the other hand, geostatistical techniques ideally require, at the least, modelling the covariance from the rain gauge data at every time step evaluated, which commonly cannot be soundly done. Here, we propose a new procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) for operational rainfall estimation using rain gauges and meteorological radar, which does not require explicit modelling of spatial covariances. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on the OAS, whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The approach considers radar estimates as background a priori information (first guess), so that nudging to observations (gauges) may be relaxed smoothly to the first guess, and the relaxation shape is obtained from the sequential

Evaluation of flash-flood discharge forecasts in complex terrain using precipitation

USGS Publications Warehouse

Yates, D.; Warner, T.T.; Brandes, E.A.; Leavesley, G.H.; Sun, Jielun; Mueller, C.K.

2001-01-01

Operational prediction of flash floods produced by thunderstorm (convective) precipitation in mountainous areas requires accurate estimates or predictions of the precipitation distribution in space and time. The details of the spatial distribution are especially critical in complex terrain because the watersheds are generally small in size, and small position errors in the forecast or observed placement of the precipitation can distribute the rain over the wrong watershed. In addition to the need for good precipitation estimates and predictions, accurate flood prediction requires a surface-hydrologic model that is capable of predicting stream or river discharge based on the precipitation-rate input data. Different techniques for the estimation and prediction of convective precipitation will be applied to the Buffalo Creek, Colorado flash flood of July 1996, where over 75 mm of rain from a thunderstorm fell on the watershed in less than 1 h. The hydrologic impact of the precipitation was exacerbated by the fact that a significant fraction of the watershed experienced a wildfire approximately two months prior to the rain event. Precipitation estimates from the National Weather Service's operational Weather Surveillance Radar-Doppler 1988 and the National Center for Atmospheric Research S-band, research, dual-polarization radar, colocated to the east of Denver, are compared. In addition, very short range forecasts from a convection-resolving dynamic model, which is initialized variationally using the radar reflectivity and Doppler winds, are compared with forecasts from an automated-algorithmic forecast system that also employs the radar data. The radar estimates of rain rate, and the two forecasting systems that employ the radar data, have degraded accuracy by virtue of the fact that they are applied in complex terrain. Nevertheless, the radar data and forecasts from the dynamic model and the automated algorithm could be operationally useful for input to surface

The ACCE method: an approach for obtaining quantitative or qualitative estimates of residual confounding that includes unmeasured confounding

PubMed Central

Smith, Eric G.

2015-01-01

Background:  Nonrandomized studies typically cannot account for confounding from unmeasured factors.  Method:  A method is presented that exploits the recently-identified phenomenon of  “confounding amplification” to produce, in principle, a quantitative estimate of total residual confounding resulting from both measured and unmeasured factors.  Two nested propensity score models are constructed that differ only in the deliberate introduction of an additional variable(s) that substantially predicts treatment exposure.  Residual confounding is then estimated by dividing the change in treatment effect estimate between models by the degree of confounding amplification estimated to occur, adjusting for any association between the additional variable(s) and outcome. Results:  Several hypothetical examples are provided to illustrate how the method produces a quantitative estimate of residual confounding if the method’s requirements and assumptions are met.  Previously published data is used to illustrate that, whether or not the method routinely provides precise quantitative estimates of residual confounding, the method appears to produce a valuable qualitative estimate of the likely direction and general size of residual confounding. Limitations:  Uncertainties exist, including identifying the best approaches for: 1) predicting the amount of confounding amplification, 2) minimizing changes between the nested models unrelated to confounding amplification, 3) adjusting for the association of the introduced variable(s) with outcome, and 4) deriving confidence intervals for the method’s estimates (although bootstrapping is one plausible approach). Conclusions:  To this author’s knowledge, it has not been previously suggested that the phenomenon of confounding amplification, if such amplification is as predictable as suggested by a recent simulation, provides a logical basis for estimating total residual confounding. The method's basic approach is

[Quantitative estimation source of urban atmospheric CO2 by carbon isotope composition].

PubMed

Liu, Wei; Wei, Nan-Nan; Wang, Guang-Hua; Yao, Jian; Zeng, You-Shi; Fan, Xue-Bo; Geng, Yan-Hong; Li, Yan

2012-04-01

To effectively reduce urban carbon emissions and verify the effectiveness of currently project for urban carbon emission reduction, quantitative estimation sources of urban atmospheric CO2 correctly is necessary. Since little fractionation of carbon isotope exists in the transportation from pollution sources to the receptor, the carbon isotope composition can be used for source apportionment. In the present study, a method was established to quantitatively estimate the source of urban atmospheric CO2 by the carbon isotope composition. Both diurnal and height variations of concentrations of CO2 derived from biomass, vehicle exhaust and coal burning were further determined for atmospheric CO2 in Jiading district of Shanghai. Biomass-derived CO2 accounts for the largest portion of atmospheric CO2. The concentrations of CO2 derived from the coal burning are larger in the night-time (00:00, 04:00 and 20:00) than in the daytime (08:00, 12:00 and 16:00), and increase with the increase of height. Those derived from the vehicle exhaust decrease with the height increase. The diurnal and height variations of sources reflect the emission and transport characteristics of atmospheric CO2 in Jiading district of Shanghai.

Air pollution or global warming: Attribution of extreme precipitation changes in eastern China—Comments on "Trends of extreme precipitation in Eastern China and their possible causes"

NASA Astrophysics Data System (ADS)

Wang, Yuan

2015-10-01

The recent study "Trends of Extreme Precipitation in Eastern China and Their Possible Causes" attributed the observed decrease/increase of light/heavy precipitation in eastern China to global warming rather than the regional aerosol effects. However, there exist compelling evidence from previous long-term observations and numerical modeling studies, suggesting that anthropogenic pollution is closely linked to the recent changes in precipitation intensity because of considerably modulated cloud physical properties by aerosols in eastern China. Clearly, a quantitative assessment of the aerosol and greenhouse effects on the regional scale is required to identify the primary cause for the extreme precipitation changes.

Combination of methylated-DNA precipitation and methylation-sensitive restriction enzymes (COMPARE-MS) for the rapid, sensitive and quantitative detection of DNA methylation.

PubMed

Yegnasubramanian, Srinivasan; Lin, Xiaohui; Haffner, Michael C; DeMarzo, Angelo M; Nelson, William G

2006-02-09

Hypermethylation of CpG island (CGI) sequences is a nearly universal somatic genome alteration in cancer. Rapid and sensitive detection of DNA hypermethylation would aid in cancer diagnosis and risk stratification. We present a novel technique, called COMPARE-MS, that can rapidly and quantitatively detect CGI hypermethylation with high sensitivity and specificity in hundreds of samples simultaneously. To quantitate CGI hypermethylation, COMPARE-MS uses real-time PCR of DNA that was first digested by methylation-sensitive restriction enzymes and then precipitated by methyl-binding domain polypeptides immobilized on a magnetic solid matrix. We show that COMPARE-MS could detect five genome equivalents of methylated CGIs in a 1000- to 10,000-fold excess of unmethylated DNA. COMPARE-MS was used to rapidly quantitate hypermethylation at multiple CGIs in >155 prostate tissues, including benign and malignant prostate specimens, and prostate cell lines. This analysis showed that GSTP1, MDR1 and PTGS2 CGI hypermethylation as determined by COMPARE-MS could differentiate between malignant and benign prostate with sensitivities >95% and specificities approaching 100%. This novel technology could significantly improve our ability to detect CGI hypermethylation.

Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEx): For Measurement Sake Let it Snow

NASA Technical Reports Server (NTRS)

Skofronick-Jackson, Gail; Hudak, David; Petersen, Walter; Nesbitt, Stephen W.; Chandrasekar, V.; Durden, Stephen; Gleicher, Kirstin J.; Huang, Gwo-Jong; Joe, Paul; Kollias, Pavlos;

Orographic Impacts on Liquid and Ice-Phase Precipitation Processes during OLYMPEX

NASA Astrophysics Data System (ADS)

Petersen, W. A.; Hunzinger, A.; Gatlin, P. N.; Wolff, D. B.

2017-12-01

The Global Precipitation Measurement (GPM) mission Olympic Mountains Experiment (OLYMPEX) focused on physical validation of GPM products in cold-season, mid-latitude frontal precipitation occurring over the Olympic Mountains of Washington State. Herein, we use data collected by the NASA S-band polarimetric radar (NPOL) to quantify and examine ice (IWP), liquid (LWP) and total water paths (TWP) relative to surface precipitation rates and column hydrometeor types for several cases occurring in different synoptic and/or Froude number regimes. These quantities are compared to coincident precipitation properties measured or estimated by GPM's Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR). Because ice scattering is the dominant radiometric signature used by the GMI for estimating precipitation over land, and because the DPR is greatly affected by ground clutter in the lowest 1 - 2 km above ground, measurement limitations combined with orographic forcing may impact the degree to which DPR and/or GMI algorithms are able to adequately observe and estimate precipitation over and around orography.Preliminary case results suggest: 1) as expected, the Olympic Mountains force robust enhancements in the liquid and ice microphysical processes on windward slopes, especially in atmospheric river events; 2) localized orographic enhancements alter the balance of liquid and frozen precipitation contributions (IWP/TWP, LWP/TWP) to near surface rain rate, and for two cases examined thus far the balance seems to be sensitive to flow direction at specific intersections with the terrain orientation; and 3) GPM measurement limitations related to the depth of surface clutter impact for the DPR, and degree to which ice processes are coupled to the orographic rainfall process (DPR and GMI), especially along windward mountain slopes, may constrain the ability of retrieval algorithms to properly estimate near-surface precipitation quantities over complex terrain. Ongoing

Recharge estimation in semi-arid karst catchments: Central West Bank, Palestine

NASA Astrophysics Data System (ADS)

Jebreen, Hassan; Wohnlich, Stefan; Wisotzky, Frank; Banning, Andre; Niedermayr, Andrea; Ghanem, Marwan

2018-03-01

Knowledge of groundwater recharge constitutes a valuable tool for sustainable management in karst systems. In this respect, a quantitative evaluation of groundwater recharge can be considered a pre-requisite for the optimal operation of groundwater resources systems, particular for semi-arid areas. This paper demonstrates the processes affecting recharge in Palestine aquifers. The Central Western Catchment is one of the main water supply sources in the West Bank. Quantification of potential recharge rates are estimated using chloride mass balance (CMB) and empirical recharge equations over the catchment. The results showing the spatialized recharge rate, which ranges from 111-216 mm/year, representing 19-37% of the long-term mean annual rainfall. Using Water Balance models and climatological data (e. g. solar radiation, monthly temperature, average monthly relative humidity and precipitation), actual evapotranspiration (AET) is estimated. The mean annual actual evapotranspiration was about 66-70% of precipitation.

Impact of acid precipitation on recreation and tourism in Ontario: an overview

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

Not Available

The impacts of acid precipitation on fishing opportunities, waterfowl and moose hunting, water contact activities, and the perception of the environment in Ontario are analyzed. Economic effects and future research needs are also estimated and discussed. These questions have been examined by identifying the likely links between acidic precipitation and recreation and tourism, by developing estimates of the importance of aquatic-based recreation and tourism, by describing the current and estimated future effects of acid precipitation. 101 references, 9 figures, 19 tables.

Kriging analysis of mean annual precipitation, Powder River Basin, Montana and Wyoming

USGS Publications Warehouse

Karlinger, M.R.; Skrivan, James A.

1981-01-01

Kriging is a statistical estimation technique for regionalized variables which exhibit an autocorrelation structure. Such structure can be described by a semi-variogram of the observed data. The kriging estimate at any point is a weighted average of the data, where the weights are determined using the semi-variogram and an assumed drift, or lack of drift, in the data. Block, or areal, estimates can also be calculated. The kriging algorithm, based on unbiased and minimum-variance estimates, involves a linear system of equations to calculate the weights. Kriging variances can then be used to give confidence intervals of the resulting estimates. Mean annual precipitation in the Powder River basin, Montana and Wyoming, is an important variable when considering restoration of coal-strip-mining lands of the region. Two kriging analyses involving data at 60 stations were made--one assuming no drift in precipitation, and one a partial quadratic drift simulating orographic effects. Contour maps of estimates of mean annual precipitation were similar for both analyses, as were the corresponding contours of kriging variances. Block estimates of mean annual precipitation were made for two subbasins. Runoff estimates were 1-2 percent of the kriged block estimates. (USGS)

Broad-spectrum monitoring strategies for predicting occult precipitation contribution to water balance in a coastal watershed in California: Ground-truthing, areal monitoring and isotopic analysis of fog in the San Francisco Bay region

NASA Astrophysics Data System (ADS)

Koohafkan, M.; Thompson, S. E.; Leonardson, R.; Dufour, A.

2013-12-01

We showcase a fog monitoring study designed to quantitatively estimate the contribution of summer fog events to the water balance of a coastal watershed managed by the San Francisco Public Utilities Commission. Two decades of research now clearly show that fog and occult precipitation can be major contributors to the water balance of watersheds worldwide. Monitoring, understanding and predicting occult precipitation is therefore as hydrologically compelling as forecasting precipitation or evaporation, particularly in the face of climate variability. We combine ground-based monitoring and collection strategies with remote sensing technologies, time-lapse imagery, and isotope analysis to trace the ';signature' of fog in physical and ecological processes. Spatial coverage and duration of fog events in the watershed is monitored using time-lapse cameras and leaf wetness sensors strategically positioned to provide estimates of the fog bank extent and cloud base elevation, and this fine-scale data is used to estimate transpiration suppression by fog and is examined in the context of regional climate through the use of satellite imagery. Soil moisture sensors, throughfall collectors and advective fog collectors deployed throughout the watershed provide quantitative estimates of fog drip contribution to soil moisture and plants. Fog incidence records and streamflow monitoring provide daily estimates of fog contribution to streamflow. Isotope analysis of soil water, fog drip, stream water and vegetation samples are used to probe for evidence of direct root and leaf uptake of fog drip by plants. Using this diversity of fog monitoring methods, we develop an empirical framework for the inclusion of fog processes in water balance models.

A study on raindrop size distribution variability in before and after landfall precipitations of tropical cyclones observed over southern India

NASA Astrophysics Data System (ADS)

Janapati, Jayalakshmi; seela, Balaji Kumar; Reddy M., Venkatrami; Reddy K., Krishna; Lin, Pay-Liam; Rao T., Narayana; Liu, Chian-Yi

2017-06-01

Raindrop size distribution (RSD) characteristics in before landfall (BLF) and after landfall (ALF) of three tropical cyclones (JAL, THANE, and NILAM) induced precipitations are investigated by using a laser-based (PARticleSIze and VELocity - PARSIVEL) disdrometer at two different locations [Kadapa (14.47°N, 78.82°E) and Gadanki (13.5°N, 79.2°E)] in semi-arid region of southern India. In both BLF and ALF precipitations of these three cyclones, convective precipitations have higher mass weighted mean diameter (Dm) and lower normalized intercept parameter (log10Nw) values than stratiform precipitations. The radar reflectivity (Z) and rain rate (R) relations (Z=A*Rb) showed distinct variations in BLF and ALF precipitations of three cyclones. BLF precipitation of JAL cyclone has a higher Dm than ALF precipitation. Whereas, for THANE and NILAM cyclones ALF precipitations have higher Dm than BLF. The Dm values of three cyclones (both in BLF and ALF) are smaller than the Dm values of the other (Atlantic and Pacific) oceanic cyclones. Interaction of different regions (eyewall, inner rainbands, and outer rainbands) of cyclones with the environment and underlying surface led to RSD variations between BLF and ALF precipitations through different microphysical (collision-coalescence, breakup, evaporation, and riming) processes. The immediate significance of the present work is that (i) it contributes to our understanding of cyclone RSD in BLF and ALF precipitations, and (ii) it provides the useful information for quantitative estimation of rainfall from Doppler weather radar observations.

Variability of and Factors Controlling Precipitation Production in Shallow Cumulus - Results from the ARM Eastern North Atlantic Site

NASA Astrophysics Data System (ADS)

Luke, E. P.; Kollias, P.

2016-12-01

Shallow cumulus clouds are by far the most frequently observed cloud type over the Earth's oceans and frequently produce warm rain. However, quantitative rainfall estimates from these clouds are challenging to acquire from satellites due to their small horizontal scale. Here, two years of observations from the US Department of Energy Atmospheric Radiation Measurement Program (ARM) Eastern North Atlantic (ENA) site located on Graciosa Island in the Azores are used to characterize the frequency, intensity, and fractional coverage of shallow cumulus precipitation. The analyzed dataset is the most comprehensive of its type, considering both its temporal extent and the sophistication of the ground-based observations. The precipitation rate at the base of shallow cumulus is estimated using combined radar-lidar observations and the rain retrievals are compared to the rainfall measurements available at the ground by optical disdrometers. Using synergy between surfaced-based observations of aerosols and thermodynamic soundings, the vertical structure of the Marine Boundary Layer and the temporal variability of the cloud condensation nuclei (CCN) number concentration are determined. The observed variability in shallow cumulus precipitation is examined in relation to the variability of the large-scale environment as captured by the humidity profile, the magnitude of the low-level horizontal winds and aerosol loading.

Methodologies for Quantitative Systems Pharmacology (QSP) Models: Design and Estimation.

PubMed

Ribba, B; Grimm, H P; Agoram, B; Davies, M R; Gadkar, K; Niederer, S; van Riel, N; Timmis, J; van der Graaf, P H

2017-08-01

With the increased interest in the application of quantitative systems pharmacology (QSP) models within medicine research and development, there is an increasing need to formalize model development and verification aspects. In February 2016, a workshop was held at Roche Pharma Research and Early Development to focus discussions on two critical methodological aspects of QSP model development: optimal structural granularity and parameter estimation. We here report in a perspective article a summary of presentations and discussions. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

The Global Precipitation Measurement (GPM) Mission: Overview and U.S. Status

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Azarbarzin, Ardeshir A.; Kakar, Ramesh K.; Neeck, Steven

2011-01-01

The Global Precipitation Measurement (GPM) Mission is an international satellite mission specifically designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. The cornerstone of the GPM mission is the deployment of a Core Observatory in a 65 deg non-Sun-synchronous orbit to serve as a physics observatory and a transfer standard for inter-calibration of constellation radiometers. The GPM Core Observatory will carry a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a conical-scanning multi-channel (10-183 GHz) GPM Microwave Radiometer (GMI). The first space-borne dual-frequency radar will provide not only measurements of 3-D precipitation structures but also quantitative information on microphysical properties of precipitating particles needed for improving precipitation retrievals from passive microwave sensors. The combined use of DPR and GMI measurements will place greater constraints on radiometer retrievals to improve the accuracy and consistency of precipitation estimates from all constellation radiometers. The GPM constellation is envisioned to comprise five or more conical-scanning microwave radiometers and four or more cross-track microwave sounders on operational satellites. NASA and the Japan Aerospace Exploration Agency (JAXA) plan to launch the GPM Core in July 2013. NASA will provide a second radiometer to be flown on a partner-provided GPM Low-Inclination Observatory (L10) to improve near real-time monitoring of hurricanes and mid-latitude storms. NASA and the Brazilian Space Program (AEB/IPNE) are currently engaged in a one-year study on potential L10 partnership. JAXA will contribute to GPM data from the Global Change Observation Mission-Water (GCOM-W) satellite. Additional partnerships are under development to include microwave radiometers on the French-Indian Megha-Tropiques satellite and U.S. Defense Meteorological Satellite Program (DMSP) satellites, as well as cross

Precipitation observations for operational flood forecasting in Scotland: Data availability, limitations and the impact of observational uncertainty

NASA Astrophysics Data System (ADS)

Parry, Louise; Neely, Ryan, III; Bennett, Lindsay; Collier, Chris; Dufton, David

2017-04-01

The Scottish Environment Protection Agency (SEPA) has a statutory responsibility to provide flood warning across Scotland. It achieves this through an operational partnership with the UK Met Office wherein meteorological forecasts are applied to a national distributed hydrological model, Grid- to- Grid (G2G), and catchment specific lumped PDM models. Both of these model types rely on observed precipitation input for model development and calibration, and operationally for historical runs to generate initial conditions. Scotland has an average annual precipitation of 1430mm per annum (1971-2000), but the spatial variability in totals is high, predominantly in relation to the topography and prevailing winds, which poses different challenges to both radar and point measurement methods of observation. In addition, the high elevations mean that in winter a significant proportion of precipitation falls as snow. For the operational forecasting models, observed rainfall data is provided in Near Real Time (NRT) from SEPA's network of approximately 260 telemetered TBR gauges and 4 UK Met Office C-band radars. Both data sources have their strengths and weaknesses, particularly in relation to the orography and spatial representativeness, but estimates of rainfall from the two methods can vary greatly. Northern Scotland, particularly near Inverness, is a comparatively sparse part of the radar network. Rainfall totals and distribution in this area are determined by the Northern Western Highlands and Cairngorms mountain ranges, which also have a negative impact on radar observations. In recognition of this issue, the NCAS mobile X-band weather radar (MXWR) was deployed in this area between February and August 2016. This study presents a comparison of rainfall estimates for the Inverness and Moray Firth region generated from the operational radar network, the TBR network, and the MXWR. Quantitative precipitation estimates (QPEs) from both sources of radar data were compared to

Insights into mountain precipitation and snowpack from a basin-scale wireless-sensor network

NASA Astrophysics Data System (ADS)

Zhang, Z.; Glaser, S.; Bales, R.; Conklin, M.; Rice, R.; Marks, D.

2017-08-01

A spatially distributed wireless-sensor network, installed across the 2154 km2 portion of the 5311 km2 American River basin above 1500 m elevation, provided spatial measurements of temperature, relative humidity, and snow depth in the Sierra Nevada, California. The network consisted of 10 sensor clusters, each with 10 measurement nodes, distributed to capture the variability in topography and vegetation cover. The sensor network captured significant spatial heterogeneity in rain versus snow precipitation for water-year 2014, variability that was not apparent in the more limited operational data. Using daily dew-point temperature to track temporal elevational changes in the rain-snow transition, the amount of snow accumulation at each node was used to estimate the fraction of rain versus snow. This resulted in an underestimate of total precipitation below the 0°C dew-point elevation, which averaged 1730 m across 10 precipitation events, indicating that measuring snow does not capture total precipitation. We suggest blending lower elevation rain gauge data with higher-elevation sensor-node data for each event to estimate total precipitation. Blended estimates were on average 15-30% higher than using either set of measurements alone. Using data from the current operational snow-pillow sites gives even lower estimates of basin-wide precipitation. Given the increasing importance of liquid precipitation in a warming climate, a strategy that blends distributed measurements of both liquid and solid precipitation will provide more accurate basin-wide precipitation estimates, plus spatial and temporal patters of snow accumulation and melt in a basin.

Spatio-temporal interpolation of precipitation during monsoon periods in Pakistan

NASA Astrophysics Data System (ADS)

Hussain, Ijaz; Spöck, Gunter; Pilz, Jürgen; Yu, Hwa-Lung

2010-08-01

Spatio-temporal estimation of precipitation over a region is essential to the modeling of hydrologic processes for water resources management. The changes of magnitude and space-time heterogeneity of rainfall observations make space-time estimation of precipitation a challenging task. In this paper we propose a Box-Cox transformed hierarchical Bayesian multivariate spatio-temporal interpolation method for the skewed response variable. The proposed method is applied to estimate space-time monthly precipitation in the monsoon periods during 1974-2000, and 27-year monthly average precipitation data are obtained from 51 stations in Pakistan. The results of transformed hierarchical Bayesian multivariate spatio-temporal interpolation are compared to those of non-transformed hierarchical Bayesian interpolation by using cross-validation. The software developed by [11] is used for Bayesian non-stationary multivariate space-time interpolation. It is observed that the transformed hierarchical Bayesian method provides more accuracy than the non-transformed hierarchical Bayesian method.

Timber Mountain Precipitation Monitoring Station

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

Lyles, Brad; McCurdy, Greg; Chapman, Jenny

2012-01-01

A precipitation monitoring station was placed on the west flank of Timber Mountain during the year 2010. It is located in an isolated highland area near the western border of the Nevada National Security Site (NNSS), south of Pahute Mesa. The cost of the equipment, permitting, and installation was provided by the Environmental Monitoring Systems Initiative (EMSI) project. Data collection, analysis, and maintenance of the station during fiscal year 2011 was funded by the U.S. Department of Energy, National Nuclear Security Administration, Nevada Site Office Environmental Restoration, Soils Activity. The station is located near the western headwaters of Forty Milemore » Wash on the Nevada Test and Training Range (NTTR). Overland flows from precipitation events that occur in the Timber Mountain high elevation area cross several of the contaminated Soils project CAU (Corrective Action Unit) sites located in the Forty Mile Wash watershed. Rain-on-snow events in the early winter and spring around Timber Mountain have contributed to several significant flow events in Forty Mile Wash. The data from the new precipitation gauge at Timber Mountain will provide important information for determining runoff response to precipitation events in this area of the NNSS. Timber Mountain is also a groundwater recharge area, and estimation of recharge from precipitation was important for the EMSI project in determining groundwater flowpaths and designing effective groundwater monitoring for Yucca Mountain. Recharge estimation additionally provides benefit to the Underground Test Area Sub-project analysis of groundwater flow direction and velocity from nuclear test areas on Pahute Mesa. Additionally, this site provides data that has been used during wild fire events and provided a singular monitoring location of the extreme precipitation events during December 2010 (see data section for more details). This letter report provides a summary of the site location, equipment, and data

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

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.

Toward quantitative estimation of material properties with dynamic mode atomic force microscopy: a comparative study.

PubMed

Ghosal, Sayan; Gannepalli, Anil; Salapaka, Murti

2017-08-11

In this article, we explore methods that enable estimation of material properties with the dynamic mode atomic force microscopy suitable for soft matter investigation. The article presents the viewpoint of casting the system, comprising of a flexure probe interacting with the sample, as an equivalent cantilever system and compares a steady-state analysis based method with a recursive estimation technique for determining the parameters of the equivalent cantilever system in real time. The steady-state analysis of the equivalent cantilever model, which has been implicitly assumed in studies on material property determination, is validated analytically and experimentally. We show that the steady-state based technique yields results that quantitatively agree with the recursive method in the domain of its validity. The steady-state technique is considerably simpler to implement, however, slower compared to the recursive technique. The parameters of the equivalent system are utilized to interpret storage and dissipative properties of the sample. Finally, the article identifies key pitfalls that need to be avoided toward the quantitative estimation of material properties.

Winter precipitation particle size distribution measurement by Multi-Angle Snowflake Camera

NASA Astrophysics Data System (ADS)

Huang, Gwo-Jong; Kleinkort, Cameron; Bringi, V. N.; Notaroš, Branislav M.

2017-12-01

From the radar meteorology viewpoint, the most important properties for quantitative precipitation estimation of winter events are 3D shape, size, and mass of precipitation particles, as well as the particle size distribution (PSD). In order to measure these properties precisely, optical instruments may be the best choice. The Multi-Angle Snowflake Camera (MASC) is a relatively new instrument equipped with three high-resolution cameras to capture the winter precipitation particle images from three non-parallel angles, in addition to measuring the particle fall speed using two pairs of infrared motion sensors. However, the results from the MASC so far are usually presented as monthly or seasonally, and particle sizes are given as histograms, no previous studies have used the MASC for a single storm study, and no researchers use MASC to measure the PSD. We propose the methodology for obtaining the winter precipitation PSD measured by the MASC, and present and discuss the development, implementation, and application of the new technique for PSD computation based on MASC images. Overall, this is the first study of the MASC-based PSD. We present PSD MASC experiments and results for segments of two snow events to demonstrate the performance of our PSD algorithm. The results show that the self-consistency of the MASC measured single-camera PSDs is good. To cross-validate PSD measurements, we compare MASC mean PSD (averaged over three cameras) with the collocated 2D Video Disdrometer, and observe good agreements of the two sets of results.

Quantitative estimation of film forming polymer-plasticizer interactions by the Lorentz-Lorenz Law.

PubMed

Dredán, J; Zelkó, R; Dávid, A Z; Antal, I

2006-03-09

Molar refraction as well as refractive index has many uses. Beyond confirming the identity and purity of a compound, determination of molecular structure and molecular weight, molar refraction is also used in other estimation schemes, such as in critical properties, surface tension, solubility parameter, molecular polarizability, dipole moment, etc. In the present study molar refraction values of polymer dispersions were determined for the quantitative estimation of film forming polymer-plasticizer interactions. Information can be obtained concerning the extent of interaction between the polymer and the plasticizer from the calculation of molar refraction values of film forming polymer dispersions containing plasticizer.

Development of Innovative Technology to Expand Precipitation Observations in Satellite Precipitation Validation in Under-developed Data-sparse Regions

NASA Astrophysics Data System (ADS)

Kucera, P. A.; Steinson, M.

2016-12-01

Accurate and reliable real-time monitoring and dissemination of observations of precipitation and surface weather conditions in general is critical for a variety of research studies and applications. Surface precipitation observations provide important reference information for evaluating satellite (e.g., GPM) precipitation estimates. High quality surface observations of precipitation, temperature, moisture, and winds are important for applications such as agriculture, water resource monitoring, health, and hazardous weather early warning systems. In many regions of the World, surface weather station and precipitation gauge networks are sparsely located and/or of poor quality. Existing stations have often been sited incorrectly, not well-maintained, and have limited communications established at the site for real-time monitoring. The University Corporation for Atmospheric Research (UCAR)/National Center for Atmospheric Research (NCAR), with support from USAID, has started an initiative to develop and deploy low-cost weather instrumentation including tipping bucket and weighing-type precipitation gauges in sparsely observed regions of the world. The goal is to improve the number of observations (temporally and spatially) for the evaluation of satellite precipitation estimates in data-sparse regions and to improve the quality of applications for environmental monitoring and early warning alert systems on a regional to global scale. One important aspect of this initiative is to make the data open to the community. The weather station instrumentation have been developed using innovative new technologies such as 3D printers, Raspberry Pi computing systems, and wireless communications. An initial pilot project have been implemented in the country of Zambia. This effort could be expanded to other data sparse regions around the globe. The presentation will provide an overview and demonstration of 3D printed weather station development and initial evaluation of observed

Quantitative Pointwise Estimate of the Solution of the Linearized Boltzmann Equation

NASA Astrophysics Data System (ADS)

Lin, Yu-Chu; Wang, Haitao; Wu, Kung-Chien

2018-04-01

We study the quantitative pointwise behavior of the solutions of the linearized Boltzmann equation for hard potentials, Maxwellian molecules and soft potentials, with Grad's angular cutoff assumption. More precisely, for solutions inside the finite Mach number region (time like region), we obtain the pointwise fluid structure for hard potentials and Maxwellian molecules, and optimal time decay in the fluid part and sub-exponential time decay in the non-fluid part for soft potentials. For solutions outside the finite Mach number region (space like region), we obtain sub-exponential decay in the space variable. The singular wave estimate, regularization estimate and refined weighted energy estimate play important roles in this paper. Our results extend the classical results of Liu and Yu (Commun Pure Appl Math 57:1543-1608, 2004), (Bull Inst Math Acad Sin 1:1-78, 2006), (Bull Inst Math Acad Sin 6:151-243, 2011) and Lee et al. (Commun Math Phys 269:17-37, 2007) to hard and soft potentials by imposing suitable exponential velocity weight on the initial condition.

Quantitative Pointwise Estimate of the Solution of the Linearized Boltzmann Equation

NASA Astrophysics Data System (ADS)

Lin, Yu-Chu; Wang, Haitao; Wu, Kung-Chien

2018-06-01

We study the quantitative pointwise behavior of the solutions of the linearized Boltzmann equation for hard potentials, Maxwellian molecules and soft potentials, with Grad's angular cutoff assumption. More precisely, for solutions inside the finite Mach number region (time like region), we obtain the pointwise fluid structure for hard potentials and Maxwellian molecules, and optimal time decay in the fluid part and sub-exponential time decay in the non-fluid part for soft potentials. For solutions outside the finite Mach number region (space like region), we obtain sub-exponential decay in the space variable. The singular wave estimate, regularization estimate and refined weighted energy estimate play important roles in this paper. Our results extend the classical results of Liu and Yu (Commun Pure Appl Math 57:1543-1608, 2004), (Bull Inst Math Acad Sin 1:1-78, 2006), (Bull Inst Math Acad Sin 6:151-243, 2011) and Lee et al. (Commun Math Phys 269:17-37, 2007) to hard and soft potentials by imposing suitable exponential velocity weight on the initial condition.

Validation of Satellite-based Rainfall Estimates for Severe Storms (Hurricanes & Tornados)

NASA Astrophysics Data System (ADS)

Nourozi, N.; Mahani, S.; Khanbilvardi, R.

2005-12-01

Severe storms such as hurricanes and tornadoes cause devastating damages, almost every year, over a large section of the United States. More accurate forecasting intensity and track of a heavy storm can help to reduce if not to prevent its damages to lives, infrastructure, and economy. Estimating accurate high resolution quantitative precipitation (QPE) from a hurricane, required to improve the forecasting and warning capabilities, is still a challenging problem because of physical characteristics of the hurricane even when it is still over the ocean. Satellite imagery seems to be a valuable source of information for estimating and forecasting heavy precipitation and also flash floods, particularly for over the oceans where the traditional ground-based gauge and radar sources cannot provide any information. To improve the capability of a rainfall retrieval algorithm for estimating QPE of severe storms, its product is evaluated in this study. High (hourly 4km x 4km) resolutions satellite infrared-based rainfall products, from the NESDIS Hydro-Estimator (HE) and also PERSIANN (Precipitation Estimation from Remotely Sensed Information using an Artificial Neural Networks) algorithms, have been tested against NEXRAD stage-IV and rain gauge observations in this project. Three strong hurricanes: Charley (category 4), Jeanne (category 3), and Ivan (category 3) that caused devastating damages over Florida in the summer 2004, have been considered to be investigated. Preliminary results demonstrate that HE tends to underestimate rain rates when NEXRAD shows heavy storm (rain rates greater than 25 mm/hr) and to overestimate when NEXRAD gives low rainfall amounts, but PERSIANN tends to underestimate rain rates, in general.

Quantitative analysis of low-density SNP data for parentage assignment and estimation of family contributions to pooled samples.

PubMed

Henshall, John M; Dierens, Leanne; Sellars, Melony J

2014-09-02

While much attention has focused on the development of high-density single nucleotide polymorphism (SNP) assays, the costs of developing and running low-density assays have fallen dramatically. This makes it feasible to develop and apply SNP assays for agricultural species beyond the major livestock species. Although low-cost low-density assays may not have the accuracy of the high-density assays widely used in human and livestock species, we show that when combined with statistical analysis approaches that use quantitative instead of discrete genotypes, their utility may be improved. The data used in this study are from a 63-SNP marker Sequenom® iPLEX Platinum panel for the Black Tiger shrimp, for which high-density SNP assays are not currently available. For quantitative genotypes that could be estimated, in 5% of cases the most likely genotype for an individual at a SNP had a probability of less than 0.99. Matrix formulations of maximum likelihood equations for parentage assignment were developed for the quantitative genotypes and also for discrete genotypes perturbed by an assumed error term. Assignment rates that were based on maximum likelihood with quantitative genotypes were similar to those based on maximum likelihood with perturbed genotypes but, for more than 50% of cases, the two methods resulted in individuals being assigned to different families. Treating genotypes as quantitative values allows the same analysis framework to be used for pooled samples of DNA from multiple individuals. Resulting correlations between allele frequency estimates from pooled DNA and individual samples were consistently greater than 0.90, and as high as 0.97 for some pools. Estimates of family contributions to the pools based on quantitative genotypes in pooled DNA had a correlation of 0.85 with estimates of contributions from DNA-derived pedigree. Even with low numbers of SNPs of variable quality, parentage testing and family assignment from pooled samples are

First Evaluation of the Climatological Calibration Algorithm in the Real-time TMPA Precipitation Estimates over Two Basins at High and Low Latitudes

NASA Technical Reports Server (NTRS)

Yong, Bin; Ren, Liliang; Hong, Yang; Gourley, Jonathan; Tian, Yudong; Huffman, George J.; Chen, Xi; Wang, Weiguang; Wen, Yixin

2013-01-01

The TRMM Multi-satellite Precipitation Analysis (TMPA) system underwent a crucial upgrade in early 2009 to include a climatological calibration algorithm (CCA) to its realtime product 3B42RT, and this algorithm will continue to be applied in the future Global Precipitation Measurement era constellation precipitation products. In this study, efforts are focused on the comparison and validation of the Version 6 3B42RT estimates before and after the climatological calibration is applied. The evaluation is accomplished using independent rain gauge networks located within the high-latitude Laohahe basin and the low-latitude Mishui basin, both in China. The analyses indicate the CCA can effectively reduce the systematic errors over the low-latitude Mishui basin but misrepresent the intensity distribution pattern of medium-high rain rates. This behavior could adversely affect TMPA's hydrological applications, especially for extreme events (e.g., floods and landslides). Results also show that the CCA tends to perform slightly worse, in particular, during summer and winter, over the high-latitude Laohahe basin. This is possibly due to the simplified calibration-processing scheme in the CCA that directly applies the climatological calibrators developed within 40 degrees latitude to the latitude belts of 40 degrees N-50 degrees N. Caution should therefore be exercised when using the calibrated 3B42RT for heavy rainfall-related flood forecasting (or landslide warning) over high-latitude regions, as the employment of the smooth-fill scheme in the CCA bias correction could homogenize the varying rainstorm characteristics. Finally, this study highlights that accurate detection and estimation of snow at high latitudes is still a challenging task for the future development of satellite precipitation retrievals.

ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative-quantitative modeling.

PubMed

Streif, Stefan; Savchenko, Anton; Rumschinski, Philipp; Borchers, Steffen; Findeisen, Rolf

2012-05-01

Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if-then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLab(TM)-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity. ADMIT, tutorials and illustrative examples are available free of charge for non-commercial use at http://ifatwww.et.uni-magdeburg.de/syst/ADMIT/

Enhancing Global Land Surface Hydrology Estimates from the NASA MERRA Reanalysis Using Precipitation Observations and Model Parameter Adjustments

NASA Technical Reports Server (NTRS)

Reichle, Rolf; Koster, Randal; DeLannoy, Gabrielle; Forman, Barton; Liu, Qing; Mahanama, Sarith; Toure, Ally

2011-01-01

The Modern-Era Retrospective analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides. in addition to atmospheric fields. global estimates of soil moisture, latent heat flux. snow. and runoff for J 979-present. This study introduces a supplemental and improved set of land surface hydrological fields ('MERRA-Land') generated by replaying a revised version of the land component of the MERRA system. Specifically. the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameters in the rainfall interception model, changes that effectively correct for known limitations in the MERRA land surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ERA-Interim reanalysis. MERRA-Land and ERA-Interim root zone soil moisture skills (against in situ observations at 85 US stations) are comparable and significantly greater than that of MERRA. Throughout the northern hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 15 basins in the western US) of MERRA and MERRA-Land is typically higher than that of ERA-Interim. With a few exceptions. the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using '\\-tERRA output for land surface hydrological studies.

Probability of occurrence of monthly and seasonal winter precipitation over Northwest India based on antecedent-monthly precipitation

NASA Astrophysics Data System (ADS)

Nageswararao, M. M.; Mohanty, U. C.; Dimri, A. P.; Osuri, Krishna K.

2018-05-01

Winter (December, January, and February (DJF)) precipitation over northwest India (NWI) is mainly associated with the eastward moving mid-latitude synoptic systems, western disturbances (WDs), embedded within the subtropical westerly jet (SWJ), and is crucial for Rabi (DJF) crops. In this study, the role of winter precipitation at seasonal and monthly scale over NWI and its nine meteorological subdivisions has been analyzed. High-resolution (0.25° × 0.25°) gridded precipitation data set of India Meteorological Department (IMD) for the period of 1901-2013 is used. Results indicated that the seasonal precipitation over NWI is below (above) the long-term mean in most of the years, when precipitation in any of the month (December/January/February) is in deficit (excess). The contribution of December precipitation (15-20%) to the seasonal (DJF) precipitation is lesser than January (35-40%) and February (35-50%) over all the subdivisions. December (0.60), January (0.57), and February (0.69) precipitation is in-phase (correlation) with the corresponding winter season precipitation. However, January precipitation is not in-phase with the corresponding December (0.083) and February (-0.03) precipitation, while December is in-phase with the February (0.21). When monthly precipitation (December or January or December-January or February) at subdivision level over NWI is excess (deficit); then, the probability of occurrence of seasonal excess (deficit) precipitation is high (almost nil). When antecedent-monthly precipitation is a deficit or excess, the probability of monthly (January or February or January + February) precipitation to be a normal category is >60% over all the subdivisions. This study concludes that the December precipitation is a good indicator to estimate the performance of January, February, January-February, and the seasonal (DJF) precipitation.

Study on Cloud Water Resources and Precipitation Efficiency Characteristic over China

NASA Astrophysics Data System (ADS)

Zhou, Y., Sr.; Cai, M., Jr.

2017-12-01

The original concept and quantitative assessment method of cloud water resource and its related physical parameters are proposed based on the atmospheric water circulation and precipitation enhancement. A diagnosis method of the three-dimensional (3-D) cloud and cloud water field are proposed , based on cloud observation and atmospheric reanalysis data. Furthermore, using analysis data and precipitation products, Chinese cloud water resources in 2008-2010 are assessed preliminarily. The results show that: 1. Atmospheric water cycle and water balance plays an important part of the climate system. Water substance includes water vapor and hydrometeors, and the water cycle is the process of phase transition of water substances. Water vapor changes its phase into solid or liquid hydrometeors by lifting and condensation, and after that, the hydrometeors grow lager through cloud physical processes and then precipitate to ground, which is the mainly resource of available fresh water .Therefore, it's far from enough to only focus on the amount of water vapor, more attention should be transfered to the hydrometeors (cloud water resources) which is formed by the process of phase transition including lifting and condensation. The core task of rainfall enhancement is to develop the cloud water resources and raise the precipitation efficiency by proper technological measures. 2. Comparing with the water vapor, the hydrometeor content is much smaller. Besides, the horizontal delivery amount also shows two orders of magnitude lower than water vapor. But the update cycle is faster and the precipitation efficiency is higher. The amount of cloud water resources in the atmosphere is determined by the instantaneous quantity, the advection transport, condensation and precipitation from the water balance.The cloud water resources vary a lot in different regions. In southeast China, hydrometeor has the fastest renewal cycle and the highest precipitation efficiency. The total amount of

A comparison of monthly precipitation point estimates at 6 locations in Iran using integration of soft computing methods and GARCH time series model

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2017-11-01