Multisensor comparison of ice concentration estimates in the marginal ice zone

NASA Technical Reports Server (NTRS)

Burns, B. A.; Cavalieri, D. J.; Gloersen, P.; Keller, M. R.; Campbell, W. J.

1987-01-01

Aircraft remote sensing data collected during the 1984 summer Marginal Ice Zone Experiment in the Fram Strait are used to compare ice concentration estimates derived from synthetic aperture radar (SAR) imagery, passive microwave imagery at several frequencies, aerial photography, and spectral photometer data. The comparison is carried out not only to evaluate SAR performance against more established techniques but also to investigate how ice surface conditions, imaging geometry, and choice of algorithm parameters affect estimates made by each sensor.Active and passive microwave sensor estimates of ice concentration derived using similar algorithms show an rms difference of 13 percent. Agreement between each microwave sensor and near-simultaneous aerial photography is approximately the same (14 percent). The availability of high-resolution microwave imagery makes it possible to ascribe the discrepancies in the concentration estimates to variations in ice surface signatures in the scene.

Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application

NASA Technical Reports Server (NTRS)

Key, Jeff; Maslanik, James; Steffen, Konrad

1995-01-01

During the second phase project year we have made progress in the development and refinement of surface temperature retrieval algorithms and in product generation. More specifically, we have accomplished the following: (1) acquired a new advanced very high resolution radiometer (AVHRR) data set for the Beaufort Sea area spanning an entire year; (2) acquired additional along-track scanning radiometer(ATSR) data for the Arctic and Antarctic now totalling over eight months; (3) refined our AVHRR Arctic and Antarctic ice surface temperature (IST) retrieval algorithm, including work specific to Greenland; (4) developed ATSR retrieval algorithms for the Arctic and Antarctic, including work specific to Greenland; (5) developed cloud masking procedures for both AVHRR and ATSR; (6) generated a two-week bi-polar global area coverage (GAC) set of composite images from which IST is being estimated; (7) investigated the effects of clouds and the atmosphere on passive microwave 'surface' temperature retrieval algorithms; and (8) generated surface temperatures for the Beaufort Sea data set, both from AVHRR and special sensor microwave imager (SSM/I).

Improved Passive Microwave Algorithms for North America and Eurasia

NASA Technical Reports Server (NTRS)

Foster, James; Chang, Alfred; Hall, Dorothy

1997-01-01

Microwave algorithms simplify complex physical processes in order to estimate geophysical parameters such as snow cover and snow depth. The microwave radiances received at the satellite sensor and expressed as brightness temperatures are a composite of contributions from the Earth's surface, the Earth's atmosphere and from space. Owing to the coarse resolution inherent to passive microwave sensors, each pixel value represents a mixture of contributions from different surface types including deep snow, shallow snow, forests and open areas. Algorithms are generated in order to resolve these mixtures. The accuracy of the retrieved information is affected by uncertainties in the assumptions used in the radiative transfer equation (Steffen et al., 1992). One such uncertainty in the Chang et al., (1987) snow algorithm is that the snow grain radius is 0.3 mm for all layers of the snowpack and for all physiographic regions. However, this is not usually the case. The influence of larger grain sizes appears to be of more importance for deeper snowpacks in the interior of Eurasia. Based on this consideration and the effects of forests, a revised SMMR snow algorithm produces more realistic snow mass values. The purpose of this study is to present results of the revised algorithm (referred to for the remainder of this paper as the GSFC 94 snow algorithm) which incorporates differences in both fractional forest cover and snow grain size. Results from the GSFC 94 algorithm will be compared to the original Chang et al. (1987) algorithm and to climatological snow depth data as well.

Sea Ice Concentration Estimation Using Active and Passive Remote Sensing Data Fusion

NASA Astrophysics Data System (ADS)

Zhang, Y.; Li, F.; Zhang, S.; Zhu, T.

2017-12-01

In this abstract, a decision-level fusion method by utilizing SAR and passive microwave remote sensing data for sea ice concentration estimation is investigated. Sea ice concentration product from passive microwave concentration retrieval methods has large uncertainty within thin ice zone. Passive microwave data including SSM/I, AMSR-E, and AMSR-2 provide daily and long time series observations covering whole polar sea ice scene, and SAR images provide rich sea ice details with high spatial resolution including deformation and polarimetric features. In the proposed method, the merits from passive microwave data and SAR data are considered. Sea ice concentration products from ASI and sea ice category label derived from CRF framework in SAR imagery are calibrated under least distance protocol. For SAR imagery, incident angle and azimuth angle were used to correct backscattering values from slant range to ground range in order to improve geocoding accuracy. The posterior probability distribution between category label from SAR imagery and passive microwave sea ice concentration product is modeled and integrated under Bayesian network, where Gaussian statistical distribution from ASI sea ice concentration products serves as the prior term, which represented as an uncertainty of sea ice concentration. Empirical model based likelihood term is constructed under Bernoulli theory, which meets the non-negative and monotonically increasing conditions. In the posterior probability estimation procedure, final sea ice concentration is obtained using MAP criterion, which equals to minimize the cost function and it can be calculated with nonlinear iteration method. The proposed algorithm is tested on multiple satellite SAR data sets including GF-3, Sentinel-1A, RADARSAT-2 and Envisat ASAR. Results show that the proposed algorithm can improve the accuracy of ASI sea ice concentration products and reduce the uncertainty along the ice edge.

An equivalent method of mixed dielectric constant in passive microwave/millimeter radiometric measurement

NASA Astrophysics Data System (ADS)

Su, Jinlong; Tian, Yan; Hu, Fei; Gui, Liangqi; Cheng, Yayun; Peng, Xiaohui

2017-10-01

Dielectric constant is an important role to describe the properties of matter. This paper proposes This paper proposes the concept of mixed dielectric constant(MDC) in passive microwave radiometric measurement. In addition, a MDC inversion method is come up, Ratio of Angle-Polarization Difference(RAPD) is utilized in this method. The MDC of several materials are investigated using RAPD. Brightness temperatures(TBs) which calculated by MDC and original dielectric constant are compared. Random errors are added to the simulation to test the robustness of the algorithm. Keywords: Passive detection, microwave/millimeter, radiometric measurement, ratio of angle-polarization difference (RAPD), mixed dielectric constant (MDC), brightness temperatures, remote sensing, target recognition.

A 1DVAR-based snowfall rate retrieval algorithm for passive microwave radiometers

NASA Astrophysics Data System (ADS)

Meng, Huan; Dong, Jun; Ferraro, Ralph; Yan, Banghua; Zhao, Limin; Kongoli, Cezar; Wang, Nai-Yu; Zavodsky, Bradley

2017-06-01

Snowfall rate retrieval from spaceborne passive microwave (PMW) radiometers has gained momentum in recent years. PMW can be so utilized because of its ability to sense in-cloud precipitation. A physically based, overland snowfall rate (SFR) algorithm has been developed using measurements from the Advanced Microwave Sounding Unit-A/Microwave Humidity Sounder sensor pair and the Advanced Technology Microwave Sounder. Currently, these instruments are aboard five polar-orbiting satellites, namely, NOAA-18, NOAA-19, Metop-A, Metop-B, and Suomi-NPP. The SFR algorithm relies on a separate snowfall detection algorithm that is composed of a satellite-based statistical model and a set of numerical weather prediction model-based filters. There are four components in the SFR algorithm itself: cloud properties retrieval, computation of ice particle terminal velocity, ice water content adjustment, and the determination of snowfall rate. The retrieval of cloud properties is the foundation of the algorithm and is accomplished using a one-dimensional variational (1DVAR) model. An existing model is adopted to derive ice particle terminal velocity. Since no measurement of cloud ice distribution is available when SFR is retrieved in near real time, such distribution is implicitly assumed by deriving an empirical function that adjusts retrieved SFR toward radar snowfall estimates. Finally, SFR is determined numerically from a complex integral. The algorithm has been validated against both radar and ground observations of snowfall events from the contiguous United States with satisfactory results. Currently, the SFR product is operationally generated at the National Oceanic and Atmospheric Administration and can be obtained from that organization.

Classification methods for monitoring Arctic sea ice using OKEAN passive/active two-channel microwave data

USGS Publications Warehouse

Belchansky, Gennady I.; Douglas, David C.

2000-01-01

This paper presents methods for classifying Arctic sea ice using both passive and active (2-channel) microwave imagery acquired by the Russian OKEAN 01 polar-orbiting satellite series. Methods and results are compared to sea ice classifications derived from nearly coincident Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) image data of the Barents, Kara, and Laptev Seas. The Russian OKEAN 01 satellite data were collected over weekly intervals during October 1995 through December 1997. Methods are presented for calibrating, georeferencing and classifying the raw active radar and passive microwave OKEAN 01 data, and for correcting the OKEAN 01 microwave radiometer calibration wedge based on concurrent 37 GHz horizontal polarization SSM/I brightness temperature data. Sea ice type and ice concentration algorithms utilized OKEAN's two-channel radar and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter, together with a priori knowledge about the scattering parameters and natural emissivities of basic sea ice types. OKEAN 01 data and algorithms tended to classify lower concentrations of young or first-year sea ice when concentrations were less than 60%, and to produce higher concentrations of multi-year sea ice when concentrations were greater than 40%, when compared to estimates produced from SSM/I data. Overall, total sea ice concentration maps derived independently from OKEAN 01, SSM/I, and AVHRR satellite imagery were all highly correlated, with uniform biases, and mean differences in total ice concentration of less than four percent (sd<15%).

Disaggregation Of Passive Microwave Soil Moisture For Use In Watershed Hydrology Applications

NASA Astrophysics Data System (ADS)

Fang, Bin

In recent years the passive microwave remote sensing has been providing soil moisture products using instruments on board satellite/airborne platforms. Spatial resolution has been restricted by the diameter of antenna which is inversely proportional to resolution. As a result, typical products have a spatial resolution of tens of kilometers, which is not compatible for some hydrological research applications. For this reason, the dissertation explores three disaggregation algorithms that estimate L-band passive microwave soil moisture at the subpixel level by using high spatial resolution remote sensing products from other optical and radar instruments were proposed and implemented in this investigation. The first technique utilized a thermal inertia theory to establish a relationship between daily temperature change and average soil moisture modulated by the vegetation condition was developed by using NLDAS, AVHRR, SPOT and MODIS data were applied to disaggregate the 25 km AMSR-E soil moisture to 1 km in Oklahoma. The second algorithm was built on semi empirical physical models (NP89 and LP92) derived from numerical experiments between soil evaporation efficiency and soil moisture over the surface skin sensing depth (a few millimeters) by using simulated soil temperature derived from MODIS and NLDAS as well as AMSR-E soil moisture at 25 km to disaggregate the coarse resolution soil moisture to 1 km in Oklahoma. The third algorithm modeled the relationship between the change in co-polarized radar backscatter and the remotely sensed microwave change in soil moisture retrievals and assumed that change in soil moisture was a function of only the canopy opacity. The change detection algorithm was implemented using aircraft based the remote sensing data from PALS and UAVSAR that were collected in SMPAVEX12 in southern Manitoba, Canada. The PALS L-band h-polarization radiometer soil moisture retrievals were disaggregated by combining them with the PALS and UAVSAR L-band hh-polarization radar spatial resolutions of 1500 m and 5 m/800 m, respectively. All three algorithms were validated using ground measurements from network in situ stations or handheld hydra probes. The validation results demonstrate the practicability on coarse resolution passive microwave soil moisture products.

Snowfall Rate Retrieval using NPP ATMS Passive Microwave Measurements

NASA Technical Reports Server (NTRS)

Meng, Huan; Ferraro, Ralph; Kongoli, Cezar; Wang, Nai-Yu; Dong, Jun; Zavodsky, Bradley; Yan, Banghua; Zhao, Limin

2014-01-01

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2014). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The ATMS SFR product is validated against radar and gauge snowfall data and shows that the ATMS algorithm outperforms the AMSU/MHS SFR.

An RFI Detection Algorithm for Microwave Radiometers Using Sparse Component Analysis

NASA Technical Reports Server (NTRS)

Mohammed-Tano, Priscilla N.; Korde-Patel, Asmita; Gholian, Armen; Piepmeier, Jeffrey R.; Schoenwald, Adam; Bradley, Damon

2017-01-01

Radio Frequency Interference (RFI) is a threat to passive microwave measurements and if undetected, can corrupt science retrievals. The sparse component analysis (SCA) for blind source separation has been investigated to detect RFI in microwave radiometer data. Various techniques using SCA have been simulated to determine detection performance with continuous wave (CW) RFI.

Passive Microwave Algorithms for Sea Ice Concentration: A Comparison of Two Techniques

NASA Technical Reports Server (NTRS)

Comiso, Josefino C.; Cavalieri, Donald J.; Parkinson, Claire L.; Gloersen, Per

1997-01-01

The most comprehensive large-scale characterization of the global sea ice cover so far has been provided by satellite passive microwave data. Accurate retrieval of ice concentrations from these data is important because of the sensitivity of surface flux(e.g. heat, salt, and water) calculations to small change in the amount of open water (leads and polynyas) within the polar ice packs. Two algorithms that have been used for deriving ice concentrations from multichannel data are compared. One is the NASA Team algorithm and the other is the Bootstrap algorithm, both of which were developed at NASA's Goddard Space Flight Center. The two algorithms use different channel combinations, reference brightness temperatures, weather filters, and techniques. Analyses are made to evaluate the sensitivity of algorithm results to variations of emissivity and temperature with space and time. To assess the difference in the performance of the two algorithms, analyses were performed with data from both hemispheres and for all seasons. The results show only small differences in the central Arctic in but larger disagreements in the seasonal regions and in summer. In some ares in the Antarctic, the Bootstrap technique show ice concentrations higher than those of the Team algorithm by as much as 25%; whereas, in other areas, it shows ice concentrations lower by as much as 30%. The The differences in the results are caused by temperature effects, emissivity effects, and tie point differences. The Team and the Bootstrap results were compared with available Landsat, advanced very high resolution radiometer (AVHRR) and synthetic aperture radar (SAR) data. AVHRR, Landsat, and SAR data sets all yield higher concentrations than the passive microwave algorithms. Inconsistencies among results suggest the need for further validation studies.

Arctic multiyear ice classification and summer ice cover using passive microwave satellite data

NASA Technical Reports Server (NTRS)

Comiso, J. C.

1990-01-01

Passive microwave data collected by Nimbus 7 were used to classify and monitor the Arctic multilayer sea ice cover. Sea ice concentration maps during several summer minima are analyzed to obtain estimates of ice floes that survived summer, and the results are compared with multiyear-ice concentrations derived from these data by using an algorithm that assumes a certain emissivity for multiyear ice. The multiyear ice cover inferred from the winter data was found to be about 25 to 40 percent less than the summer ice-cover minimum, indicating that the multiyear ice cover in winter is inadequately represented by the passive microwave winter data and that a significant fraction of the Arctic multiyear ice floes exhibits a first-year ice signature.

Assimilation of a knowledge base and physical models to reduce errors in passive-microwave classifications of sea ice

NASA Technical Reports Server (NTRS)

Maslanik, J. A.; Key, J.

1992-01-01

An expert system framework has been developed to classify sea ice types using satellite passive microwave data, an operational classification algorithm, spatial and temporal information, ice types estimated from a dynamic-thermodynamic model, output from a neural network that detects the onset of melt, and knowledge about season and region. The rule base imposes boundary conditions upon the ice classification, modifies parameters in the ice algorithm, determines a `confidence' measure for the classified data, and under certain conditions, replaces the algorithm output with model output. Results demonstrate the potential power of such a system for minimizing overall error in the classification and for providing non-expert data users with a means of assessing the usefulness of the classification results for their applications.

NASA sea ice and snow validation plan for the Defense Meteorological Satellite Program special sensor microwave/imager

NASA Technical Reports Server (NTRS)

Cavalieri, Donald J. (Editor); Swift, Calvin T. (Editor)

1987-01-01

This document addresses the task of developing and executing a plan for validating the algorithm used for initial processing of sea ice data from the Special Sensor Microwave/Imager (SSMI). The document outlines a plan for monitoring the performance of the SSMI, for validating the derived sea ice parameters, and for providing quality data products before distribution to the research community. Because of recent advances in the application of passive microwave remote sensing to snow cover on land, the validation of snow algorithms is also addressed.

Mapping the spatial distribution and time evolution of snow water equivalent with passive microwave measurements

USGS Publications Warehouse

Guo, J.; Tsang, L.; Josberger, E.G.; Wood, A.W.; Hwang, J.-N.; Lettenmaier, D.P.

2003-01-01

This paper presents an algorithm that estimates the spatial distribution and temporal evolution of snow water equivalent and snow depth based on passive remote sensing measurements. It combines the inversion of passive microwave remote sensing measurements via dense media radiative transfer modeling results with snow accumulation and melt model predictions to yield improved estimates of snow depth and snow water equivalent, at a pixel resolution of 5 arc-min. In the inversion, snow grain size evolution is constrained based on pattern matching by using the local snow temperature history. This algorithm is applied to produce spatial snow maps of Upper Rio Grande River basin in Colorado. The simulation results are compared with that of the snow accumulation and melt model and a linear regression method. The quantitative comparison with the ground truth measurements from four Snowpack Telemetry (SNOTEL) sites in the basin shows that this algorithm is able to improve the estimation of snow parameters.

Estimation of snow emissivity via assimilation of multi-frequency passive microwave data into an ensemble-based data assimilation system

NASA Astrophysics Data System (ADS)

Farhadi, L.; Bateni, S. M.; Auligne, T.; Navari, M.

2017-12-01

Snow emissivity is a key parameter for the estimation of snow surface temperature, which is needed as an initial value in climate models and determination of the outgoing long-wave radiation. Moreover, snow emissivity is required for retrieval of atmospheric parameters (e.g., temperature and humidity profiles) from satellite measurements and satellite data assimilations in numerical weather prediction systems. Microwave emission models and remote sensing data cannot accurately estimate snow emissivity due to limitations attributed to each of them. Existing microwave emission models introduce significant uncertainties in their snow emissivity estimates. This is mainly due to shortcomings of the dense media theory for snow medium at high frequencies, and erroneous forcing variables. The well-known limitations of passive microwave data such as coarse spatial resolution, saturation in deep snowpack, and signal loss in wet snow are the major drawbacks of passive microwave retrieval algorithms for estimation of snow emissivity. A full exploitation of the information contained in the remote sensing data can be achieved by merging them with snow emission models within a data assimilation framework. Such an optimal merging can overcome the specific limitations of models and remote sensing data. An Ensemble Batch Smoother (EnBS) data assimilation framework was developed in this study to combine the synthetically generated passive microwave brightness temperatures at 1.4-, 18.7-, 36.5-, and 89-GHz frequencies with the MEMLS microwave emission model to reduce the uncertainty of the snow emissivity estimates. We have used the EnBS algorithm in the context of observing system simulation experiment (or synthetic experiment) at the local scale observation site (LSOS) of the NASA CLPX field campaign. Our findings showed that the developed methodology significantly improves the estimates of the snow emissivity. The simultaneous assimilation of passive microwave brightness temperatures at all frequencies (i.e., 1.4-, 18.7-, 36.5-, and 89-GHz) reduce the root-mean-square-error (RMSE) of snow emissivity at 1.4-, 18.7-, 36.5-, and 89-GHz (H-pol.) by 80%, 42%, 52%, 40%, respectively compared to the corresponding snow emissivity estimates from the open-loop model.

Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons

NASA Astrophysics Data System (ADS)

Karthikeyan, L.; Pan, Ming; Wanders, Niko; Kumar, D. Nagesh; Wood, Eric F.

2017-11-01

Soil moisture is widely recognized as an important land surface variable that provides a deeper knowledge of land-atmosphere interactions and climate change. Space-borne passive and active microwave sensors have become valuable and essential sources of soil moisture observations at global scales. Over the past four decades, several active and passive microwave sensors have been deployed, along with the recent launch of two fully dedicated missions (SMOS and SMAP). Signifying the four decades of microwave remote sensing of soil moisture, this Part 2 of the two-part review series aims to present an overview of how our knowledge in this field has improved in terms of the design of sensors and their accuracy for retrieving soil moisture. The first part discusses the developments made in active and passive microwave soil moisture retrieval algorithms. We assess the evolution of the products of various sensors over the last four decades, in terms of daily coverage, temporal performance, and spatial performance, by comparing the products of eight passive sensors (SMMR, SSM/I, TMI, AMSR-E, WindSAT, AMSR2, SMOS and SMAP), two active sensors (ERS-Scatterometer, MetOp-ASCAT), and one active/passive merged soil moisture product (ESA-CCI combined product) with the International Soil Moisture Network (ISMN) in-situ stations and the Variable Infiltration Capacity (VIC) land surface model simulations over the Contiguous United States (CONUS). In the process, the regional impacts of vegetation conditions on the spatial and temporal performance of soil moisture products are investigated. We also carried out inter-satellite comparisons to study the roles of sensor design and algorithms on the retrieval accuracy. We find that substantial improvements have been made over recent years in this field in terms of daily coverage, retrieval accuracy, and temporal dynamics. We conclude that the microwave soil moisture products have significantly evolved in the last four decades and will continue to make key contributions to the progress of hydro-meteorological and climate sciences.

A multi-sensor data-driven methodology for all-sky passive microwave inundation retrieval

NASA Astrophysics Data System (ADS)

Takbiri, Zeinab; Ebtehaj, Ardeshir M.; Foufoula-Georgiou, Efi

2017-06-01

We present a multi-sensor Bayesian passive microwave retrieval algorithm for flood inundation mapping at high spatial and temporal resolutions. The algorithm takes advantage of observations from multiple sensors in optical, short-infrared, and microwave bands, thereby allowing for detection and mapping of the sub-pixel fraction of inundated areas under almost all-sky conditions. The method relies on a nearest-neighbor search and a modern sparsity-promoting inversion method that make use of an a priori dataset in the form of two joint dictionaries. These dictionaries contain almost overlapping observations by the Special Sensor Microwave Imager and Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) F17 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua and Terra satellites. Evaluation of the retrieval algorithm over the Mekong Delta shows that it is capable of capturing to a good degree the inundation diurnal variability due to localized convective precipitation. At longer timescales, the results demonstrate consistency with the ground-based water level observations, denoting that the method is properly capturing inundation seasonal patterns in response to regional monsoonal rain. The calculated Euclidean distance, rank-correlation, and also copula quantile analysis demonstrate a good agreement between the outputs of the algorithm and the observed water levels at monthly and daily timescales. The current inundation products are at a resolution of 12.5 km and taken twice per day, but a higher resolution (order of 5 km and every 3 h) can be achieved using the same algorithm with the dictionary populated by the Global Precipitation Mission (GPM) Microwave Imager (GMI) products.

Satellite Snow-Cover Mapping: A Brief Review

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.

1995-01-01

Satellite snow mapping has been accomplished since 1966, initially using data from the reflective part of the electromagnetic spectrum, and now also employing data from the microwave part of the spectrum. Visible and near-infrared sensors can provide excellent spatial resolution from space enabling detailed snow mapping. When digital elevation models are also used, snow mapping can provide realistic measurements of snow extent even in mountainous areas. Passive-microwave satellite data permit global snow cover to be mapped on a near-daily basis and estimates of snow depth to be made, but with relatively poor spatial resolution (approximately 25 km). Dense forest cover limits both techniques and optical remote sensing is limited further by cloudcover conditions. Satellite remote sensing of snow cover with imaging radars is still in the early stages of research, but shows promise at least for mapping wet or melting snow using C-band (5.3 GHz) synthetic aperture radar (SAR) data. Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data beginning with the launch of the first EOS platform in 1998. Digital maps will be produced that will provide daily, and maximum weekly global snow, sea ice and lake ice cover at 1-km spatial resolution. Statistics will be generated on the extent and persistence of snow or ice cover in each pixel for each weekly map, cloudcover permitting. It will also be possible to generate snow- and ice-cover maps using MODIS data at 250- and 500-m resolution, and to study and map snow and ice characteristics such as albedo. been under development. Passive-microwave data offer the potential for determining not only snow cover, but snow water equivalent, depth and wetness under all sky conditions. A number of algorithms have been developed to utilize passive-microwave brightness temperatures to provide information on snow cover and water equivalent. The variability of vegetative Algorithms are being developed to map global snow and ice cover using Earth Algorithms to map global snow cover using passive-microwave data have also cover and of snow grain size, globally, limits the utility of a single algorithm to map global snow cover.

Synergistic use of active and passive microwave in soil moisture estimation

NASA Technical Reports Server (NTRS)

O'Neill, P.; Chauhan, N.; Jackson, T.; Saatchi, S.

1992-01-01

Data gathered during the MACHYDRO experiment in central Pennsylvania in July 1990 have been utilized to study the synergistic use of active and passive microwave systems for estimating soil moisture. These data sets were obtained during an eleven-day period with NASA's Airborne Synthetic Aperture Radar (AIRSAR) and Push-Broom Microwave Radiometer (PBMR) over an instrumented watershed which included agricultural fields with a number of different crop covers. Simultaneous ground truth measurements were also made in order to characterize the state of vegetation and soil moisture under a variety of meteorological conditions. A combination algorithm is presented as applied to a representative corn field in the MACHYDRO watershed.

Assessment of the Relative Accuracy of Hemispheric-Scale Snow-Cover Maps

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Kelly, Richard E.; Riggs, George A.; Chang, Alfred T. C.; Foster, James L.; Houser, Paul (Technical Monitor)

2001-01-01

There are several hemispheric-scale satellite-derived snow-cover maps available, but none has been fully validated. For the period October 23 - December 25, 2000, we compare snow maps of North America derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the National Oceanic and Atmospheric Administration (NOAA) National Operational Hydrologic Remote Sensing Center (NOHRSC), which both rely on satellite data from the visible and near-infrared parts of the spectrum; we also compare MODIS and Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) passive-microwave snow maps. The maps derived from visible and near-infrared data are more accurate for mapping snow cover than are the passive-microwave-derived maps, however discrepancies exist as to the location and extent of the snow cover among those maps. The large (approx. 30 km) footprint of the SSM/I data and the difficulty in distinguishing wet and shallow snow from wet or snow-free ground, reveal differences up to 5.32 million sq km in the amount of snow mapped using MODIS versus SSM/I data. Algorithms that utilize both visible and passive-microwave data, which would take advantage of the all-weather mapping ability of the passive-microwave data, will be refined following the launch of the Advanced Microwave Scanning Radiometer (AMSR) in the fall of 2001.

Combining Passive Microwave Rain Rate Retrieval with Visible and Infrared Cloud Classification.

NASA Astrophysics Data System (ADS)

Miller, Shawn William

The relation between cloud type and rain rate has been investigated here from different approaches. Previous studies and intercomparisons have indicated that no single passive microwave rain rate algorithm is an optimal choice for all types of precipitating systems. Motivated by the upcoming Tropical Rainfall Measuring Mission (TRMM), an algorithm which combines visible and infrared cloud classification with passive microwave rain rate estimation was developed and analyzed in a preliminary manner using data from the Tropical Ocean Global Atmosphere-Coupled Ocean Atmosphere Response Experiment (TOGA-COARE). Overall correlation with radar rain rate measurements across five case studies showed substantial improvement in the combined algorithm approach when compared to the use of any single microwave algorithm. An automated neural network cloud classifier for use over both land and ocean was independently developed and tested on Advanced Very High Resolution Radiometer (AVHRR) data. The global classifier achieved strict accuracy for 82% of the test samples, while a more localized version achieved strict accuracy for 89% of its own test set. These numbers provide hope for the eventual development of a global automated cloud classifier for use throughout the tropics and the temperate zones. The localized classifier was used in conjunction with gridded 15-minute averaged radar rain rates at 8km resolution produced from the current operational network of National Weather Service (NWS) radars, to investigate the relation between cloud type and rain rate over three regions of the continental United States and adjacent waters. The results indicate a substantially lower amount of available moisture in the Front Range of the Rocky Mountains than in the Midwest or in the eastern Gulf of Mexico.

SMMR Simulator radiative transfer calibration model. 2: Algorithm development

NASA Technical Reports Server (NTRS)

Link, S.; Calhoon, C.; Krupp, B.

1980-01-01

Passive microwave measurements performed from Earth orbit can be used to provide global data on a wide range of geophysical and meteorological phenomena. A Scanning Multichannel Microwave Radiometer (SMMR) is being flown on the Nimbus-G satellite. The SMMR Simulator duplicates the frequency bands utilized in the spacecraft instruments through an amalgamate of radiometer systems. The algorithm developed utilizes data from the fall 1978 NASA CV-990 Nimbus-G underflight test series and subsequent laboratory testing.

Comparative analysis of multisensor satellite monitoring of Arctic sea-ice

USGS Publications Warehouse

Belchansky, G.I.; Mordvintsev, Ilia N.; Douglas, David C.

1999-01-01

This report represents comparative analysis of nearly coincident Russian OKEAN-01 polar orbiting satellite data, Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) imagery. OKEAN-01 ice concentration algorithms utilize active and passive microwave measurements and a linear mixture model for measured values of the brightness temperature and the radar backscatter. SSM/I and AVHRR ice concentrations were computed with NASA Team algorithm and visible and thermal-infrared wavelength AVHRR data, accordingly

Impact of Surface Roughness on AMSR-E Sea Ice Products

NASA Technical Reports Server (NTRS)

Stroeve, Julienne C.; Markus, Thorsten; Maslanik, James A.; Cavalieri, Donald J.; Gasiewski, Albin J.; Heinrichs, John F.; Holmgren, Jon; Perovich, Donald K.; Sturm, Matthew

2006-01-01

This paper examines the sensitivity of Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (Tbs) to surface roughness by a using radiative transfer model to simulate AMSR-E Tbs as a function of incidence angle at which the surface is viewed. The simulated Tbs are then used to examine the influence that surface roughness has on two operational sea ice algorithms, namely: 1) the National Aeronautics and Space Administration Team (NT) algorithm and 2) the enhanced NT algorithm, as well as the impact of roughness on the AMSR-E snow depth algorithm. Surface snow and ice data collected during the AMSR-Ice03 field campaign held in March 2003 near Barrow, AK, were used to force the radiative transfer model, and resultant modeled Tbs are compared with airborne passive microwave observations from the Polarimetric Scanning Radiometer. Results indicate that passive microwave Tbs are very sensitive even to small variations in incidence angle, which can cause either an over or underestimation of the true amount of sea ice in the pixel area viewed. For example, this paper showed that if the sea ice areas modeled in this paper mere assumed to be completely smooth, sea ice concentrations were underestimated by nearly 14% using the NT sea ice algorithm and by 7% using the enhanced NT algorithm. A comparison of polarization ratios (PRs) at 10.7,18.7, and 37 GHz indicates that each channel responds to different degrees of surface roughness and suggests that the PR at 10.7 GHz can be useful for identifying locations of heavily ridged or rubbled ice. Using the PR at 10.7 GHz to derive an "effective" viewing angle, which is used as a proxy for surface roughness, resulted in more accurate retrievals of sea ice concentration for both algorithms. The AMSR-E snow depth algorithm was found to be extremely sensitive to instrument calibration and sensor viewing angle, and it is concluded that more work is needed to investigate the sensitivity of the gradient ratio at 37 and 18.7 GHz to these factors to improve snow depth retrievals from spaceborne passive microwave sensors.

Estimating surface soil moisture from SMAP observations using a neural network technique

USDA-ARS?s Scientific Manuscript database

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to June 2016 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observ...

Comparison of Snow Mass Estimates from a Prototype Passive Microwave Snow Algorithm, a Revised Algorithm and a Snow Depth Climatology

NASA Technical Reports Server (NTRS)

Foster, J. L.; Chang, A. T. C.; Hall, D. K.

1997-01-01

While it is recognized that no single snow algorithm is capable of producing accurate global estimates of snow depth, for research purposes it is useful to test an algorithm's performance in different climatic areas in order to see how it responds to a variety of snow conditions. This study is one of the first to develop separate passive microwave snow algorithms for North America and Eurasia by including parameters that consider the effects of variations in forest cover and crystal size on microwave brightness temperature. A new algorithm (GSFC 1996) is compared to a prototype algorithm (Chang et al., 1987) and to a snow depth climatology (SDC), which for this study is considered to be a standard reference or baseline. It is shown that the GSFC 1996 algorithm compares much more favorably to the SDC than does the Chang et al. (1987) algorithm. For example, in North America in February there is a 15% difference between the GSFC 198-96 Algorithm and the SDC, but with the Chang et al. (1987) algorithm the difference is greater than 50%. In Eurasia, also in February, there is only a 1.3% difference between the GSFC 1996 algorithm and the SDC, whereas with the Chang et al. (1987) algorithm the difference is about 20%. As expected, differences tend to be less when the snow cover extent is greater, particularly for Eurasia. The GSFC 1996 algorithm performs better in North America in each month than dose the Chang et al. (1987) algorithm. This is also the case in Eurasia, except in April and May when the Chang et al.(1987) algorithms is in closer accord to the SDC than is GSFC 1996 algorithm.

Attenuation of soil microwave emissivity by corn and soybeans at 1.4 and 5 GHz

NASA Technical Reports Server (NTRS)

Jackson, Thomas J.; O'Neill, Peggy E.

1989-01-01

Theory and experiments have shown that passive microwave radiometers can be used to measure soil moisture. However, the presence of a vegetative cover alters the measurement that might be obtained under bare conditions. Deterministically accounting for the effect of vegetation and developing algorithms for extracting soil moisture from observations of a vegetable-soil complex present significant obstacles to the practical use of this approach. The presence of a vegetation canopy reduces the sensitivity of passive microwave instruments to soil moisture variations. The reduction in sensitivity, as compared to a bare-soil relationship, increases as microwave frequency increases, implying that the longest wavelength sensors should provide the most information. Sensitivity also decreases as the amount of vegetative wet biomass increases for a given type of vegetation.

Evaluation of the tau-omega model for passive microwave soil moisture retrieval using SMAPEx data sets

USDA-ARS?s Scientific Manuscript database

The parameters used for passive soil moisture retrieval algorithms reported in the literature encompass a wide range, leading to a large uncertainty in the applicability of those values. This paper presents an evaluation of the proposed parameterizations of the tau-omega model from 1) SMAP ATBD for ...

Snow depth and snow cover retrieval from FengYun3B microwave radiation imagery based on a snow passive microwave unmixing method in Northeast China

NASA Astrophysics Data System (ADS)

Gu, Lingjia; Ren, Ruizhi; Zhao, Kai; Li, Xiaofeng

2014-01-01

The precision of snow parameter retrieval is unsatisfactory for current practical demands. The primary reason is because of the problem of mixed pixels that are caused by low spatial resolution of satellite passive microwave data. A snow passive microwave unmixing method is proposed in this paper, based on land cover type data and the antenna gain function of passive microwaves. The land cover type of Northeast China is partitioned into grass, farmland, bare soil, forest, and water body types. The component brightness temperatures (CBT), namely unmixed data, with 1 km data resolution are obtained using the proposed unmixing method. The snow depth determined by the CBT and three snow depth retrieval algorithms are validated through field measurements taken in forest and farmland areas of Northeast China in January 2012 and 2013. The results show that the overall of the retrieval precision of the snow depth is improved by 17% in farmland areas and 10% in forest areas when using the CBT in comparison with the mixed pixels. The snow cover results based on the CBT are compared with existing MODIS snow cover products. The results demonstrate that more snow cover information can be obtained with up to 86% accuracy.

The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies

NASA Astrophysics Data System (ADS)

Sanò, P.; Panegrossi, G.; Casella, D.; Di Paola, F.; Milani, L.; Mugnai, A.; Petracca, M.; Dietrich, S.

2015-02-01

The purpose of this study is to describe a new algorithm based on a neural network approach (Passive microwave Neural network Precipitation Retrieval - PNPR) for precipitation rate estimation from AMSU/MHS observations, and to provide examples of its performance for specific case studies over the European/Mediterranean area. The algorithm optimally exploits the different characteristics of Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) channels, and their combinations, including the brightness temperature (TB) differences of the 183.31 channels, with the goal of having a single neural network for different types of background surfaces (vegetated land, snow-covered surface, coast and ocean). The training of the neural network is based on the use of a cloud-radiation database, built from cloud-resolving model simulations coupled to a radiative transfer model, representative of the European and Mediterranean Basin precipitation climatology. The algorithm provides also the phase of the precipitation and a pixel-based confidence index for the evaluation of the reliability of the retrieval. Applied to different weather conditions in Europe, the algorithm shows good performance both in the identification of precipitation areas and in the retrieval of precipitation, which is particularly valuable over the extremely variable environmental and meteorological conditions of the region. The PNPR is particularly efficient in (1) screening and retrieval of precipitation over different background surfaces; (2) identification and retrieval of heavy rain for convective events; and (3) identification of precipitation over a cold/iced background, with increased uncertainties affecting light precipitation. In this paper, examples of good agreement of precipitation pattern and intensity with ground-based data (radar and rain gauges) are provided for four different case studies. The algorithm has been developed in order to be easily tailored to new radiometers as they become available (such as the cross-track scanning Suomi National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS)), and it is suitable for operational use as it is computationally very efficient. PNPR has been recently extended for applications to the regions of Africa and the South Atlantic, and an extended validation over these regions (using 2 yr of data acquired by the Tropical Rainfall Measuring Mission precipitation radar for comparison) is the subject of a paper in preparation. The PNPR is currently used operationally within the EUMETSAT Hydrology Satellite Application Facility (H-SAF) to provide instantaneous precipitation from passive microwave cross-track scanning radiometers. It undergoes routinely thorough extensive validation over Europe carried out by the H-SAF Precipitation Products Validation Team.

Multiscale comparison of GPM radar and passive microwave precipitation fields over oceans and land: effective resolution and global/regional/local diagnostics for improving retrieval algorithms

NASA Astrophysics Data System (ADS)

Guilloteau, C.; Foufoula-Georgiou, E.; Kummerow, C.; Kirstetter, P. E.

2017-12-01

A multiscale approach is used to compare precipitation fields retrieved from GMI using the last version of the GPROF algorithm (GPROF-2017) to the DPR fields all over the globe. Using a wavelet-based spectral analysis, which renders the multi-scale decompositions of the original fields independent of each other spatially and across scales, we quantitatively assess the various scales of variability of the retrieved fields, and thus define the spatially-variable "effective resolution" (ER) of the retrievals. Globally, a strong agreement is found between passive microwave and radar patterns at scales coarser than 80km. Over oceans the patterns match down to the 20km scale. Over land, comparison statistics are spatially heterogeneous. In most areas a strong discrepancy is observed between passive microwave and radar patterns at scales finer than 40-80km. The comparison is also supported by ground-based observations over the continental US derived from the NOAA/NSSL MRMS suite of products. While larger discrepancies over land than over oceans are classically explained by land complex surface emissivity perturbing the passive microwave retrieval, other factors are investigated here, such as intricate differences in the storm structure over oceans and land. Differences in term of statistical properties (PDF of intensities and spatial organization) of precipitation fields over land and oceans are assessed from radar data, as well as differences in the relation between the 89GHz brightness temperature and precipitation. Moreover, the multiscale approach allows quantifying the part of discrepancies caused by miss-match of the location of intense cells and instrument-related geometric effects. The objective is to diagnose shortcomings of current retrieval algorithms such that targeted improvements can be made to achieve over land the same retrieval performance as over oceans.

Microwave Observations of Precipitation and the Atmosphere

NASA Technical Reports Server (NTRS)

Staelin, David H.; Rosenkranz, Philip W.

2004-01-01

This research effort had three elements devoted to improving satellite-derived passive microwave retrievals of precipitation rate: morphological rain-rate retrievals, warm rain retrievals, and extension of a study of geostationary satellite options. The morphological precipitation-rate retrieval method uses for the first time the morphological character of the observed storm microwave spectra. The basic concept involves: 1) retrieval of point rainfall rates using current algorithms, 2) using spatial feature vectors of the observations over segmented multi-pixel storms to estimate the integrated rainfall rate for that storm (cu m/s), and 3) normalization of the point rain-rate retrievals to ensure consistency with the storm-wide retrieval. This work is ongoing, but two key steps have been completed: development of a segmentation algorithm for defining spatial regions corresponding to single storms for purposes of estimation, and reduction of some of the data from NAST-M that will be used to support this research going forward. The warm rain retrieval method involved extension of Aquai/AIRS/AMSU/HSB algorithmic work on cloud water retrievals. The central concept involves the fact that passive microwave cloud water retrievals over approx. 0.4 mm are very likely associated with precipitation. Since glaciated precipitation is generally detected quite successfully using scattering signatures evident in the surface-blind 54- and 183-GHz bands, this new method complements the first by permitting precipitation retrievals of non-glaciated events. The method is most successful over ocean, but has detected non-glaciated convective cells over land, perhaps in their early formative stages. This work will require additional exploration and validation prior to publication. Passive microwave instrument configurations for use in geostationary orbit were studied. They employ parabolic reflectors between 2 and 4 meters in diameter, and frequencies up to approx.430 GHz; this corresponds to nadir spot diameters as small as 10 km.

A passive microwave snow depth algorithm with a proxy for snow metamorphism

USGS Publications Warehouse

Josberger, E.G.; Mognard, N.M.

2002-01-01

Passive microwave brightness temperatures of snowpacks depend not only on the snow depth, but also on the internal snowpack properties, particularly the grain size, which changes through the winter. Algorithms that assume a constant grain size can yield erroneous estimates of snow depth or water equivalent. For snowpacks that are subject to temperatures well below freezing, the bulk temperature gradient through the snowpack controls the metamorphosis of the snow grains. This study used National Weather Service (NWS) station measurements of snow depth and air temperature from the Northern US Great Plains to determine temporal and spatial variability of the snow depth and bulk snowpack temperature gradient. This region is well suited for this study because it consists primarily of open farmland or prairie, has little relief, is subject to very cold temperatures, and has more than 280 reporting stations. A geostatistical technique called Kriging was used to grid the randomly spaced snow depth measurements. The resulting snow depth maps were then compared with the passive microwave observations from the Special Sensor Microwave Imager (SSM/I). Two snow seasons were examined: 1988-89, a typical snow year, and 1996-97, a record year for snow that was responsible for extensive flooding in the Red River Basin. Inspection of the time series of snow depth and microwave spectral gradient (the difference between the 19 and 37 GHz bands) showed that while the snowpack was constant, the spectral gradient continued to increase. However, there was a strong correlation (0.6 < R2 < 0.9) between the spectral gradient and the cumulative bulk temperature gradient through the snowpack (TGI). Hence, TGI is an index of grain size metamorphism that has occurred within the snowpack. TGI time series from 21 representative sites across the region and the corresponding SSM/I observations were used to develop an algorithm for snow depth that requires daily air temperatures. Copyright ?? 2002 John Wiley & Sons, Ltd.

A data-driven and physics-based single-pass retrieval of active-passive microwave covariation and vegetation parameters for the SMAP mission

NASA Astrophysics Data System (ADS)

Entekhabi, D.; Jagdhuber, T.; Das, N. N.; Baur, M.; Link, M.; Piles, M.; Akbar, R.; Konings, A. G.; Mccoll, K. A.; Alemohammad, S. H.; Montzka, C.; Kunstmann, H.

2016-12-01

The active-passive soil moisture retrieval algorithm of NASA's SMAP mission depends on robust statistical estimation of active-passive covariation (β) and vegetation structure (Γ) parameters in order to provide reliable global measurements of soil moisture on an intermediate level (9km) compared to the native resolution of the radiometer (36km) and radar (3km) instruments. These parameters apply to the SMAP radiometer-radar combination over the period of record that was cut short with the end of the SMAP radar transmission. They also apply to the current SMAP radiometer and Sentinel 1A/B radar combination for high-resolution surface soil moisture mapping. However, the performance of the statistically-based approach is directly dependent on the selection of a representative time frame in which these parameters can be estimated assuming dynamic soil moisture and stationary soil roughness and vegetation cover. Here, we propose a novel, data-driven and physics-based single-pass retrieval of active-passive microwave covariation and vegetation parameters for the SMAP mission. The algorithm does not depend on time series analyses and can be applied using minimum one pair of an active-passive acquisition. The algorithm stems from the physical link between microwave emission and scattering via conservation of energy. The formulation of the emission radiative transfer is combined with the Distorted Born Approximation of radar scattering for vegetated land surfaces. The two formulations are simultaneously solved for the covariation and vegetation structure parameters. Preliminary results from SMAP active-passive observations (April 13th to July 7th 2015) compare well with the time-series statistical approach and confirms the capability of this method to estimate these parameters. Moreover, the method is not restricted to a given frequency (applies to both L-band and C-band combinations for the radar) or incidence angle (all angles and not just the fixed 40° incidence). Therefore, the approach is applicable to the combination of SMAP and Sentinel-1A/B data for active-passive and high-resolution soil moisture estimation.

Verification of a New NOAA/NSIDC Passive Microwave Sea-Ice Concentration Climate Record

NASA Technical Reports Server (NTRS)

Meier, Walter N.; Peng, Ge; Scott, Donna J.; Savoie, Matt H.

2014-01-01

A new satellite-based passive microwave sea-ice concentration product developed for the National Oceanic and Atmospheric Administration (NOAA)Climate Data Record (CDR) programme is evaluated via comparison with other passive microwave-derived estimates. The new product leverages two well-established concentration algorithms, known as the NASA Team and Bootstrap, both developed at and produced by the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC). The sea ice estimates compare well with similar GSFC products while also fulfilling all NOAA CDR initial operation capability (IOC) requirements, including (1) self describing file format, (2) ISO 19115-2 compliant collection-level metadata,(3) Climate and Forecast (CF) compliant file-level metadata, (4) grid-cell level metadata (data quality fields), (5) fully automated and reproducible processing and (6) open online access to full documentation with version control, including source code and an algorithm theoretical basic document. The primary limitations of the GSFC products are lack of metadata and use of untracked manual corrections to the output fields. Smaller differences occur from minor variations in processing methods by the National Snow and Ice Data Center (for the CDR fields) and NASA (for the GSFC fields). The CDR concentrations do have some differences from the constituent GSFC concentrations, but trends and variability are not substantially different.

Measuring the global distribution of intense convection over land with passive microwave radiometry

NASA Technical Reports Server (NTRS)

Spencer, R. W.; Santek, D. A.

1985-01-01

The global distribution of intense convective activity over land is shown to be measurable with satellite passive-microwave methods through a comparison of an empirical rain rate algorithm with a climatology of thunderstorm days for the months of June-August. With the 18 and 37 GHz channels of the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR), the strong volume scattering effects of precipitation can be measured. Even though a single frequency (37 GHz) is responsive to the scattering signature, two frequencies are needed to remove most of the effect that variations in thermometric temperatures and soil moisture have on the brightness temperatures. Because snow cover is also a volume scatterer of microwave energy at these microwavelengths, a discrimination procedure involving four of the SMMR channels is employed to separate the rain and snow classes, based upon their differences in average thermometric temperature.

The AMSR2 Satellite-based Microwave Snow Algorithm (SMSA) to estimate regional to global snow depth and snow water equivalent

NASA Astrophysics Data System (ADS)

Kelly, R. E. J.; Saberi, N.; Li, Q.

2017-12-01

With moderate to high spatial resolution (<1 km) regional to global snow water equivalent (SWE) observation approaches yet to be fully scoped and developed, the long-term satellite passive microwave record remains an important tool for cryosphere-climate diagnostics. A new satellite microwave remote sensing approach is described for estimating snow depth (SD) and snow water equivalent (SWE). The algorithm, called the Satellite-based Microwave Snow Algorithm (SMSA), uses Advanced Microwave Scanning Radiometer - 2 (AMSR2) observations aboard the Global Change Observation Mission - Water mission launched by the Japan Aerospace Exploration Agency in 2012. The approach is unique since it leverages observed brightness temperatures (Tb) with static ancillary data to parameterize a physically-based retrieval without requiring parameter constraints from in situ snow depth observations or historical snow depth climatology. After screening snow from non-snow surface targets (water bodies [including freeze/thaw state], rainfall, high altitude plateau regions [e.g. Tibetan plateau]), moderate and shallow snow depths are estimated by minimizing the difference between Dense Media Radiative Transfer model estimates (Tsang et al., 2000; Picard et al., 2011) and AMSR2 Tb observations to retrieve SWE and SD. Parameterization of the model combines a parsimonious snow grain size and density approach originally developed by Kelly et al. (2003). Evaluation of the SMSA performance is achieved using in situ snow depth data from a variety of standard and experiment data sources. Results presented from winter seasons 2012-13 to 2016-17 illustrate the improved performance of the new approach in comparison with the baseline AMSR2 algorithm estimates and approach the performance of the model assimilation-based approach of GlobSnow. Given the variation in estimation power of SWE by different land surface/climate models and selected satellite-derived passive microwave approaches, SMSA provides SWE estimates that are independent of real or near real-time in situ and model data.

Multifrequency passive microwave observations of soil moisture in an arid rangeland environment

NASA Technical Reports Server (NTRS)

Jackson, T. J.; Schmugge, T. J.; Parry, R.; Kustas, W. P.; Ritchie, J. C.; Shutko, A. M.; Khaldin, A.; Reutov, E.; Novichikhin, E.; Liberman, B.

1992-01-01

A cooperative experiment was conducted by teams from the U.S. and U.S.S.R. to evaluate passive microwave instruments and algorithms used to estimate surface soil moisture. Experiments were conducted as part of an interdisciplinary experiment in an arid rangeland watershed located in the southwest United States. Soviet microwave radiometers operating at wavelengths of 2.25, 21 and 27 cm were flown on a U.S. aircraft. Radio frequency interference limited usable data to the 2.25 and 21 cm systems. Data have been calibrated and compared to ground observations of soil moisture. These analyses showed that the 21 cm system could produce reliable and useful soil moisture information and that the 2.25 cm system was of no value for soil moisture estimation in this experiment.

A Microwave Technique for Mapping Ice Temperature in the Arctic Seasonal Sea Ice Zone

NASA Technical Reports Server (NTRS)

St.Germain, Karen M.; Cavalieri, Donald J.

1997-01-01

A technique for deriving ice temperature in the Arctic seasonal sea ice zone from passive microwave radiances has been developed. The algorithm operates on brightness temperatures derived from the Special Sensor Microwave/Imager (SSM/I) and uses ice concentration and type from a previously developed thin ice algorithm to estimate the surface emissivity. Comparisons of the microwave derived temperatures with estimates derived from infrared imagery of the Bering Strait yield a correlation coefficient of 0.93 and an RMS difference of 2.1 K when coastal and cloud contaminated pixels are removed. SSM/I temperatures were also compared with a time series of air temperature observations from Gambell on St. Lawrence Island and from Point Barrow, AK weather stations. These comparisons indicate that the relationship between the air temperature and the ice temperature depends on ice type.

HAMP - the microwave package on the High Altitude and LOng range research aircraft (HALO)

NASA Astrophysics Data System (ADS)

Mech, M.; Orlandi, E.; Crewell, S.; Ament, F.; Hirsch, L.; Hagen, M.; Peters, G.; Stevens, B.

2014-12-01

An advanced package of microwave remote sensing instrumentation has been developed for the operation on the new German High Altitude LOng range research aircraft (HALO). The HALO Microwave Package, HAMP, consists of two nadir-looking instruments: a cloud radar at 36 GHz and a suite of passive microwave radiometers with 26 frequencies in different bands between 22.24 and 183.31 ± 12.5 GHz. We present a description of HAMP's instrumentation together with an illustration of its potential. To demonstrate this potential, synthetic measurements for the implemented passive microwave frequencies and the cloud radar based on cloud-resolving and radiative transfer model calculations were performed. These illustrate the advantage of HAMP's chosen frequency coverage, which allows for improved detection of hydrometeors both via the emission and scattering of radiation. Regression algorithms compare HAMP retrieval with standard satellite instruments from polar orbiters and show its advantages particularly for the lower atmosphere with a root-mean-square error reduced by 5 and 15% for temperature and humidity, respectively. HAMP's main advantage is the high spatial resolution of about 1 km, which is illustrated by first measurements from test flights. Together these qualities make it an exciting tool for gaining a better understanding of cloud processes, testing retrieval algorithms, defining future satellite instrument specifications, and validating platforms after they have been placed in orbit.

HAMP - the microwave package on the High Altitude and LOng range research aircraft HALO

NASA Astrophysics Data System (ADS)

Mech, M.; Orlandi, E.; Crewell, S.; Ament, F.; Hirsch, L.; Hagen, M.; Peters, G.; Stevens, B.

2014-05-01

An advanced package of microwave remote sensing instrumentation has been developed for the operation on the new German High Altitude LOng range research aircraft (HALO). The HALO Microwave Package, HAMP, consists of two nadir looking instruments: a cloud radar at 36 GHz and a suite of passive microwave radiometers with 26 frequencies in different bands between 22.24 and 183.31 ± 12.5 GHz. We present a description of HAMP's instrumentation together with an illustration of its potential. To demonstrate this potential synthetic measurements for the implemented passive microwave frequencies and the cloud radar based on cloud resolving and radiative transfer model calculations were performed. These illustrate the advantage of HAMP's chosen frequency coverage, which allows for improved detection of hydrometeors both via the emission and scattering of radiation. Regression algorithms compare HAMP retrieval with standard satellite instruments from polar orbiters and show its advantages particularly for the lower atmosphere with a reduced root mean square error by 5 and 15% for temperature and humidity, respectively. HAMP's main advantage is the high spatial resolution of about 1 km which is illustrated by first measurements from test flights. Together these qualities make it an exciting tool for gaining better understanding of cloud processes, testing retrieval algorithms, defining future satellite instrument specifications, and validating platforms after they have been placed in orbit.

Spatial Variability of Barrow-Area Shore-Fast Sea Ice and Its Relationships to Passive Microwave Emissivity

NASA Technical Reports Server (NTRS)

Maslanik, J. A.; Rivas, M. Belmonte; Holmgren, J.; Gasiewski, A. J.; Heinrichs, J. F.; Stroeve, J. C.; Klein, M.; Markus, T.; Perovich, D. K.; Sonntag, J. G.;

New Products and Perspectives from the Global Precipitation Measurement (GPM) Mission

NASA Astrophysics Data System (ADS)

Kummerow, C. D.; Randel, D.; Petkovic, V.

2016-12-01

The Global Precipitation Measurement (GPM) mission was launched in February 2014 as a joint mission between JAXA from Japan and NASA from the United States. GPM carries a state of the art dual-frequency precipitation radar and a multi-channel passive microwave radiometer that acts not only to enhance the radar's retrieval capability, but also as a reference for a constellation of existing satellites carrying passive microwave sensors. In March of 2016, GPM released Version 4 of its precipitation products that consists of radar, radiometer, and combined radar/radiometer products. The radiometer algorithm in Version 4 is the first time a fully parametric algorithm has been implemented. This talk will focus on the consistency among the constellation radiometers, and what these inconsistencies can tell us about the fundamental uncertainties within the rainfall products. This analysis will be used to then drive a bigger picture of how GPM's latest results inform the Global Water and Energy budgets.

SMAP Soil Moisture Disaggregation using Land Surface Temperature and Vegetation Data

NASA Astrophysics Data System (ADS)

Fang, B.; Lakshmi, V.

2016-12-01

Soil moisture (SM) is a key parameter in agriculture, hydrology and ecology studies. The global SM retrievals have been providing by microwave remote sensing technology since late 1970s and many SM retrieval algorithms have been developed, calibrated and applied on satellite sensors such as AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System), AMSR-2 (Advanced Microwave Scanning Radiometer 2) and SMOS (Soil Moisture and Ocean Salinity). Particularly, SMAP (Soil Moisture Active/Passive) satellite, which was developed by NASA, was launched in January 2015. SMAP provides soil moisture products of 9 km and 36 km spatial resolutions which are not capable for research and applications of finer scale. Toward this issue, this study applied a SM disaggregation algorithm to disaggregate SMAP passive microwave soil moisture 36 km product. This algorithm was developed based on the thermal inertial relationship between daily surface temperature variation and daily average soil moisture which is modulated by vegetation condition, by using remote sensing retrievals from AVHRR (Advanced Very High Resolution Radiometer, MODIS (Moderate Resolution Imaging Spectroradiometer), SPOT (Satellite Pour l'Observation de la Terre), as well as Land Surface Model (LSM) output from NLDAS (North American Land Data Assimilation System). The disaggregation model was built at 1/8o spatial resolution on monthly basis and was implemented to calculate and disaggregate SMAP 36 km SM retrievals to 1 km resolution in Oklahoma. The SM disaggregation results were also validated using MESONET (Mesoscale Network) and MICRONET (Microscale Network) ground SM measurements.

Reprocessing the Historical Satellite Passive Microwave Record at Enhanced Spatial Resolutions using Image Reconstruction

NASA Astrophysics Data System (ADS)

Hardman, M.; Brodzik, M. J.; Long, D. G.; Paget, A. C.; Armstrong, R. L.

2015-12-01

Beginning in 1978, the satellite passive microwave data record has been a mainstay of remote sensing of the cryosphere, providing twice-daily, near-global spatial coverage for monitoring changes in hydrologic and cryospheric parameters that include precipitation, soil moisture, surface water, vegetation, snow water equivalent, sea ice concentration and sea ice motion. Currently available global gridded passive microwave data sets serve a diverse community of hundreds of data users, but do not meet many requirements of modern Earth System Data Records (ESDRs) or Climate Data Records (CDRs), most notably in the areas of intersensor calibration, quality-control, provenance and consistent processing methods. The original gridding techniques were relatively primitive and were produced on 25 km grids using the original EASE-Grid definition that is not easily accommodated in modern software packages. Further, since the first Level 3 data sets were produced, the Level 2 passive microwave data on which they were based have been reprocessed as Fundamental CDRs (FCDRs) with improved calibration and documentation. We are funded by NASA MEaSUREs to reprocess the historical gridded data sets as EASE-Grid 2.0 ESDRs, using the most mature available Level 2 satellite passive microwave (SMMR, SSM/I-SSMIS, AMSR-E) records from 1978 to the present. We have produced prototype data from SSM/I and AMSR-E for the year 2003, for review and feedback from our Early Adopter user community. The prototype data set includes conventional, low-resolution ("drop-in-the-bucket" 25 km) grids and enhanced-resolution grids derived from the two candidate image reconstruction techniques we are evaluating: 1) Backus-Gilbert (BG) interpolation and 2) a radiometer version of Scatterometer Image Reconstruction (SIR). We summarize our temporal subsetting technique, algorithm tuning parameters and computational costs, and include sample SSM/I images at enhanced resolutions of up to 3 km. We are actively working with our Early Adopters to finalize content and format of this new, consistently-processed high-quality satellite passive microwave ESDR.

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part II: Evaluation of Estimates Using Independent Data

NASA Technical Reports Server (NTRS)

Yang, Song; Olson, William S.; Wang, Jian-Jian; Bell, Thomas L.; Smith, Eric A.; Kummerow, Christian D.

2006-01-01

Rainfall rate estimates from spaceborne microwave radiometers are generally accepted as reliable by a majority of the atmospheric science community. One of the Tropical Rainfall Measuring Mission (TRMM) facility rain-rate algorithms is based upon passive microwave observations from the TRMM Microwave Imager (TMI). In Part I of this series, improvements of the TMI algorithm that are required to introduce latent heating as an additional algorithm product are described. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, 0.5 deg. -resolution estimates of surface rain rate over ocean from the improved TMI algorithm are well correlated with independent radar estimates (r approx. 0.88 over the Tropics), but bias reduction is the most significant improvement over earlier algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly 2.5 -resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data is limited, TMI-estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain-rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with (a) additional contextual information brought to the estimation problem and/or (b) physically consistent and representative databases supporting the algorithm. A model of the random error in instantaneous 0.5 deg. -resolution rain-rate estimates appears to be consistent with the levels of error determined from TMI comparisons with collocated radar. Error model modifications for nonraining situations will be required, however. Sampling error represents only a portion of the total error in monthly 2.5 -resolution TMI estimates; the remaining error is attributed to random and systematic algorithm errors arising from the physical inconsistency and/or nonrepresentativeness of cloud-resolving-model-simulated profiles that support the algorithm.

Bias correction for rainrate retrievals from satellite passive microwave sensors

NASA Technical Reports Server (NTRS)

Short, David A.

1990-01-01

Rainrates retrieved from past and present satellite-borne microwave sensors are affected by a fundamental remote sensing problem. Sensor fields-of-view are typically large enough to encompass substantial rainrate variability, whereas the retrieval algorithms, based on radiative transfer calculations, show a non-linear relationship between rainrate and microwave brightness temperature. Retrieved rainrates are systematically too low. A statistical model of the bias problem shows that bias correction factors depend on the probability distribution of instantaneous rainrate and on the average thickness of the rain layer.

C-band Joint Active/Passive Dual Polarization Sea Ice Detection

NASA Astrophysics Data System (ADS)

Keller, M. R.; Gifford, C. M.; Winstead, N. S.; Walton, W. C.; Dietz, J. E.

2017-12-01

A technique for synergistically-combining high-resolution SAR returns with like-frequency passive microwave emissions to detect thin (<30 cm) ice under the difficult conditions of late melt and freeze-up is presented. As the Arctic sea ice cover thins and shrinks, the algorithm offers an approach to adapting existing sensors monitoring thicker ice to provide continuing coverage. Lower resolution (10-26 km) ice detections with spaceborne radiometers and scatterometers are challenged by rapidly changing thin ice. Synthetic Aperture Radar (SAR) is high resolution (5-100m) but because of cross section ambiguities automated algorithms have had difficulty separating thin ice types from water. The radiometric emissivity of thin ice versus water at microwave frequencies is generally unambiguous in the early stages of ice growth. The method, developed using RADARSAT-2 and AMSR-E data, uses higher-ordered statistics. For the SAR, the COV (coefficient of variation, ratio of standard deviation to mean) has fewer ambiguities between ice and water than cross sections, but breaking waves still produce ice-like signatures for both polarizations. For the radiometer, the PRIC (polarization ratio ice concentration) identifies areas that are unambiguously water. Applying cumulative statistics to co-located COV levels adaptively determines an ice/water threshold. Outcomes from extensive testing with Sentinel and AMSR-2 data are shown in the results. The detection algorithm was applied to the freeze-up in the Beaufort, Chukchi, Barents, and East Siberian Seas in 2015 and 2016, spanning mid-September to early November of both years. At the end of the melt, 6 GHz PRIC values are 5-10% greater than those reported by radiometric algorithms at 19 and 37 GHz. During freeze-up, COV separates grease ice (<5 cm thick) from water. As the ice thickens, the COV is less reliable, but adding a mask based on either the PRIC or the cross-pol/co-pol SAR ratio corrects for COV deficiencies. In general, the dual-sensor detection algorithm reports 10-15% higher total ice concentrations than operational scatterometer or radiometer algorithms, mostly from ice edge and coastal areas. In conclusion, the algorithm presented combines high-resolution SAR returns with passive microwave emissions for automated ice detection at SAR resolutions.

Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application

NASA Technical Reports Server (NTRS)

Key, Jeff; Maslanik, James; Steffen, Konrad

1994-01-01

During the first half of our second project year we have accomplished the following: (1) acquired a new AVHRR data set for the Beaufort Sea area spanning an entire year; (2) acquired additional ATSR data for the Arctic and Antarctic now totaling over seven months; (3) refined our AVHRR Arctic and Antarctic ice surface temperature (IST) retrieval algorithm, including work specific to Greenland; (4) developed ATSR retrieval algorithms for the Arctic and Antarctic, including work specific to Greenland; (5) investigated the effects of clouds and the atmosphere on passive microwave 'surface' temperature retrieval algorithms; (6) generated surface temperatures for the Beaufort Sea data set, both from AVHRR and SSM/I; and (7) continued work on compositing GAC data for coverage of the entire Arctic and Antarctic. During the second half of the year we will continue along these same lines, and will undertake a detailed validation study of the AVHRR and ATSR retrievals using LEADEX and the Beaufort Sea year-long data. Cloud masking methods used for the AVHRR will be modified for use with the ATSR. Methods of blending in situ and satellite-derived surface temperature data sets will be investigated.

Mapping Daily and Maximum Flood Extents at 90-m Resolution During Hurricanes Harvey and Irma Using Passive Microwave Remote Sensing

NASA Astrophysics Data System (ADS)

Galantowicz, J. F.; Picton, J.; Root, B.

2017-12-01

Passive microwave remote sensing can provided a distinct perspective on flood events by virtue of wide sensor fields of view, frequent observations from multiple satellites, and sensitivity through clouds and vegetation. During Hurricanes Harvey and Irma, we used AMSR2 (Advanced Microwave Scanning Radiometer 2, JAXA) data to map flood extents starting from the first post-storm rain-free sensor passes. Our standard flood mapping algorithm (FloodScan) derives flooded fraction from 22-km microwave data (AMSR2 or NASA's GMI) in near real time and downscales it to 90-m resolution using a database built from topography, hydrology, and Global Surface Water Explorer data and normalized to microwave data footprint shapes. During Harvey and Irma we tested experimental versions of the algorithm designed to map the maximum post-storm flood extent rapidly and made a variety of map products available immediately for use in storm monitoring and response. The maps have several unique features including spanning the entire storm-affected area and providing multiple post-storm updates as flood water shifted and receded. From the daily maps we derived secondary products such as flood duration, maximum flood extent (Figure 1), and flood depth. In this presentation, we describe flood extent evolution, maximum extent, and local details as detected by the FloodScan algorithm in the wake of Harvey and Irma. We compare FloodScan results to other available flood mapping resources, note observed shortcomings, and describe improvements made in response. We also discuss how best-estimate maps could be updated in near real time by merging FloodScan products and data from other remote sensing systems and hydrological models.

Arctic Sea ice studies with passive microwave satellite observations

NASA Technical Reports Server (NTRS)

Cavalieri, D. J.

1988-01-01

The objectives of this research are: (1) to improve sea ice concentration determinations from passive microwave space observations; (2) to study the role of Arctic polynyas in the production of sea ice and the associated salinization of Arctic shelf water; and (3) to study large scale sea ice variability in the polar oceans. The strategy is to analyze existing data sets and data acquired from both the DMSP SSM/I and recently completed aircraft underflights. Special attention will be given the high resolution 85.5 GHz SSM/I channels for application to thin ice algorithms and processes studies. Analysis of aircraft and satellite data sets is expected to provide a basis for determining the potential of the SSM/I high frequency channels for improving sea ice algorithms and for investigating oceanic processes. Improved sea ice algorithms will aid the study of Arctic coastal polynyas which in turn will provide a better understanding of the role of these polynyas in maintaining the Arctic watermass structure. Analysis of satellite and archived meteorological data sets will provide improved estimates of annual, seasonal and shorter-term sea ice variability.

Wind Retrievals under Rain for Passive Satellite Microwave Radiometers and its Applications to Hurricane Tracking

NASA Technical Reports Server (NTRS)

Meissner, Thomas; Wentz, Frank J.

2008-01-01

We have developed an algorithm that retrieves wind speed under rain using C-hand and X-band channels of passive microwave satellite radiometers. The spectral difference of the brightness temperature signals due to wind or rain allows to find channel combinations that are sufficiently sensitive to wind speed but little or not sensitive to rain. We &ve trained a statistical algorithm that applies under hurricane conditions and is able to measure wind speeds in hurricanes to an estimated accuracy of about 2 m/s. We have also developed a global algorithm, that is less accurate but can be applied under all conditions. Its estimated accuracy is between 2 and 5 mls, depending on wind speed and rain rate. We also extend the wind speed region in our model for the wind induced sea surface emissivity from currently 20 m/s to 40 mls. The data indicate that the signal starts to saturate above 30 mls. Finally, we make an assessment of the performance of wind direction retrievals from polarimetric radiometers as function of wind speed and rain rate

Estimation of improved resolution soil moisture in vegetated areas using passive AMSR-E data

NASA Astrophysics Data System (ADS)

Moradizadeh, Mina; Saradjian, Mohammad R.

2018-03-01

Microwave remote sensing provides a unique capability for soil parameter retrievals. Therefore, various soil parameters estimation models have been developed using brightness temperature (BT) measured by passive microwave sensors. Due to the low resolution of satellite microwave radiometer data, the main goal of this study is to develop a downscaling approach to improve the spatial resolution of soil moisture estimates with the use of higher resolution visible/infrared sensor data. Accordingly, after the soil parameters have been obtained using Simultaneous Land Parameters Retrieval Model algorithm, the downscaling method has been applied to the soil moisture estimations that have been validated against in situ soil moisture data. Advance Microwave Scanning Radiometer-EOS BT data in Soil Moisture Experiment 2003 region in the south and north of Oklahoma have been used to this end. Results illustrated that the soil moisture variability is effectively captured at 5 km spatial scales without a significant degradation of the accuracy.

A passive microwave technique for estimating rainfall and vertical structure information from space. Part 1: Algorithm description

NASA Technical Reports Server (NTRS)

Kummerow, Christian; Giglio, Louis

1994-01-01

This paper describes a multichannel physical approach for retrieving rainfall and vertical structure information from satellite-based passive microwave observations. The algorithm makes use of statistical inversion techniques based upon theoretically calculated relations between rainfall rates and brightness temperatures. Potential errors introduced into the theoretical calculations by the unknown vertical distribution of hydrometeors are overcome by explicity accounting for diverse hydrometeor profiles. This is accomplished by allowing for a number of different vertical distributions in the theoretical brightness temperature calculations and requiring consistency between the observed and calculated brightness temperatures. This paper will focus primarily on the theoretical aspects of the retrieval algorithm, which includes a procedure used to account for inhomogeneities of the rainfall within the satellite field of view as well as a detailed description of the algorithm as it is applied over both ocean and land surfaces. The residual error between observed and calculated brightness temperatures is found to be an important quantity in assessing the uniqueness of the solution. It is further found that the residual error is a meaningful quantity that can be used to derive expected accuracies from this retrieval technique. Examples comparing the retrieved results as well as the detailed analysis of the algorithm performance under various circumstances are the subject of a companion paper.

The performance of the new enhanced-resolution satellite passive microwave dataset applied for snow water equivalent estimation

NASA Astrophysics Data System (ADS)

Pan, J.; Durand, M. T.; Jiang, L.; Liu, D.

2017-12-01

The newly-processed NASA MEaSures Calibrated Enhanced-Resolution Brightness Temperature (CETB) reconstructed using antenna measurement response function (MRF) is considered to have significantly improved fine-resolution measurements with better georegistration for time-series observations and equivalent field of view (FOV) for frequencies with the same monomial spatial resolution. We are looking forward to its potential for the global snow observing purposes, and therefore aim to test its performance for characterizing snow properties, especially the snow water equivalent (SWE) in large areas. In this research, two candidate SWE algorithms will be tested in China for the years between 2005 to 2010 using the reprocessed TB from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), with the results to be evaluated using the daily snow depth measurements at over 700 national synoptic stations. One of the algorithms is the SWE retrieval algorithm used for the FengYun (FY) - 3 Microwave Radiation Imager. This algorithm uses the multi-channel TB to calculate SWE for three major snow regions in China, with the coefficients adapted for different land cover types. The second algorithm is the newly-established Bayesian Algorithm for SWE Estimation with Passive Microwave measurements (BASE-PM). This algorithm uses the physically-based snow radiative transfer model to find the histogram of most-likely snow property that matches the multi-frequency TB from 10.65 to 90 GHz. It provides a rough estimation of snow depth and grain size at the same time and showed a 30 mm SWE RMS error using the ground radiometer measurements at Sodankyla. This study will be the first attempt to test it spatially for satellite. The use of this algorithm benefits from the high resolution and the spatial consistency between frequencies embedded in the new dataset. This research will answer three questions. First, to what extent can CETB increase the heterogeneity in the mapped SWE? Second, will the SWE estimation error statistics be improved using this high-resolution dataset? Third, how will the SWE retrieval accuracy be improved using CETB and the new SWE retrieval techniques?

Towards better understanding of high-mountain cryosphere changes using GPM data: A Joint Snowfall and Snow-cover Passive Microwave Retrieval Algorithm

NASA Astrophysics Data System (ADS)

Ebtehaj, A.; Foufoula-Georgiou, E.

2016-12-01

Scientific evidence suggests that the duration and frequency of snowfall and the extent of snow cover are rapidly declining under global warming. Both precipitation and snow cover scatter the upwelling surface microwave emission and decrease the observed high-frequency brightness temperatures. The mixture of these two scattering signals is amongst the largest sources of ambiguities and errors in passive microwave retrievals of both precipitation and snow-cover. The dual frequency radar and the high-frequency radiometer on board the GPM satellite provide a unique opportunity to improve passive retrievals of precipitation and snow-cover physical properties and fill the gaps in our understating of their variability in view of climate change. Recently, a new Bayesian rainfall retrieval algorithm (called ShARP) was developed using modern approximation methods and shown to yield improvements against other algorithms in retrieval of rainfall over radiometrically complex land surfaces. However, ShARP uses a large database of input rainfall and output brightness temperatures, which might be undersampled. Furthermore, it is not capable to discriminate between solid and liquid phase of precipitation and specifically discriminate the background snow-cover emission and its contamination effects on the retrievals. We address these problems by extending it to a new Bayesian land-atmosphere retrieval framework (ShARP-L) that allows joint retrievals of atmospheric constituents and land surface physical properties. Using modern sparse approximation techniques, the database is reduced to atomic microwave signatures in a family of compact class consistent dictionaries. These dictionaries can efficiently represent the entire database and allow us to discriminate between different land-atmosphere states. First the algorithm makes use of the dictionaries to detect the phase of the precipitation and type of the land-cover and then it estimates the physical properties of precipitation and snow cover using an extended version of the Dantzig Selector, which is robust to non-Gaussian and correlated geophysical noise. Promising results are presented in retrievals of snowfall and snow-cover over coastal orographic features of North America's Coast Range and South America's Andes.

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.

A MODIS-based begetation index climatology

USDA-ARS?s Scientific Manuscript database

Passive microwave soil moisture algorithms must account for vegetation attenuation of the signal in the retrieval process. One approach to accounting for vegetation is to use vegetation indices such as the Normalized Difference Vegetation Index (NDVI) to estimate the vegetation optical depth. The pa...

Ramifications of a potential gap in passive microwave data for the long-term sea ice climate record

NASA Astrophysics Data System (ADS)

Meier, W.; Stewart, J. S.

2017-12-01

The time series of sea ice concentration and extent from passive microwave sensors is one of the longest satellite-derived climate records and the significant decline in Arctic sea ice extent is one of the most iconic indicators of climate change. However, this continuous and consistent record is under threat due to the looming gap in passive microwave sensor coverage. The record started in late 1978 with the launch of the Scanning Multichannel Microwave Radiometer (SMMR) and has continued with a series of Special Sensor Microwave Imager (SSMI) and Special Sensor Microwave Imager and Sounder (SSMIS) instruments on U.S. Defense Meteorological Satellite Program (DMSP) satellites. The data from the different sensors are intercalibrated at the algorithm level by adjusting algorithm coefficients so that the output sea ice data is as consistent as possible between the older and the newer sensor. A key aspect in constructing the time series is to have at least two sensors operating simultaneously so that data from the older and newer sensor can be obtained from the same locations. However, with recent losses of the DMSP F19 and F20, the remaining SSMIS sensors are all well beyond their planned mission lifetime. This means that risk of failure is not small and is increasing with each day of operation. The newest passive microwave sensor, the JAXA Advanced Microwave Scanning Radiometer-2 (AMSR2), is a potential contributor to the time series (though it too is now beyond it's planned 5-year mission lifetime). However, AMSR2's larger antenna and higher spatial resolution presents a challenge in integrating its data with the rest of the sea ice record because the ice edge is quite sensitive to the sensor resolution, which substantially affects the total sea ice extent and area estimates. This will need to be adjusted for if AMSR2 is used to continue the time series. Here we will discuss efforts at NSIDC to integrate AMSR2 estimates into the sea ice climate record if needed. We will also discuss potential contingency plans, such as using operational sea ice charts, to fill any gaps. This would allow the record to continue, but the consistency of the time series will be degraded because the ice charts use human analysis and differing sources, amounts and quality of input data, which makes them sub-optimal for long-term climate records.

Comparison of Passive Microwave-Derived Early Melt Onset Records on Arctic Sea Ice

NASA Technical Reports Server (NTRS)

Bliss, Angela C.; Miller, Jeffrey A.; Meier, Walter N.

2017-01-01

Two long records of melt onset (MO) on Arctic sea ice from passive microwave brightness temperatures (Tbs) obtained by a series of satellite-borne instruments are compared. The Passive Microwave (PMW) method and Advanced Horizontal Range Algorithm (AHRA) detect the increase in emissivity that occurs when liquid water develops around snow grains at the onset of early melting on sea ice. The timing of MO on Arctic sea ice influences the amount of solar radiation absorbed by the ice-ocean system throughout the melt season by reducing surface albedos in the early spring. This work presents a thorough comparison of these two methods for the time series of MO dates from 1979through 2012. The methods are first compared using the published data as a baseline comparison of the publically available data products. A second comparison is performed on adjusted MO dates we produced to remove known differences in inter-sensor calibration of Tbs and masking techniques used to develop the original MO date products. These adjustments result in a more consistent set of input Tbs for the algorithms. Tests of significance indicate that the trends in the time series of annual mean MO dates for the PMW and AHRA are statistically different for the majority of the Arctic Ocean including the Laptev, E. Siberian, Chukchi, Beaufort, and central Arctic regions with mean differences as large as 38.3 days in the Barents Sea. Trend agreement improves for our more consistent MO dates for nearly all regions. Mean differences remain large, primarily due to differing sensitivity of in-algorithm thresholds and larger uncertainties in thin-ice regions.

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part 1; Improved Method and Uncertainties

NASA Technical Reports Server (NTRS)

Olson, William S.; Kummerow, Christian D.; Yang, Song; Petty, Grant W.; Tao, Wei-Kuo; Bell, Thomas L.; Braun, Scott A.; Wang, Yansen; Lang, Stephen E.; Johnson, Daniel E.;

An Updated TRMM Composite Climatology of Tropical Rainfall and Its Validation

NASA Technical Reports Server (NTRS)

Wang, Jian-Jian; Adler, Robert F.; Huffman, George; Bolvin, David

2013-01-01

An updated 15-yr Tropical Rainfall Measuring Mission (TRMM) composite climatology (TCC) is presented and evaluated. This climatology is based on a combination of individual rainfall estimates made with data from the primaryTRMMinstruments: theTRMM Microwave Imager (TMI) and the precipitation radar (PR). This combination climatology of passive microwave retrievals, radar-based retrievals, and an algorithm using both instruments simultaneously provides a consensus TRMM-based estimate of mean precipitation. The dispersion of the three estimates, as indicated by the standard deviation sigma among the estimates, is presented as a measure of confidence in the final estimate and as an estimate of the uncertainty thereof. The procedures utilized by the compositing technique, including adjustments and quality-control measures, are described. The results give a mean value of the TCC of 4.3mm day(exp -1) for the deep tropical ocean beltbetween 10 deg N and 10 deg S, with lower values outside that band. In general, the TCC values confirm ocean estimates from the Global Precipitation Climatology Project (GPCP) analysis, which is based on passive microwave results adjusted for sampling by infrared-based estimates. The pattern of uncertainty estimates shown by sigma is seen to be useful to indicate variations in confidence. Examples include differences between the eastern and western portions of the Pacific Ocean and high values in coastal and mountainous areas. Comparison of the TCC values (and the input products) to gauge analyses over land indicates the value of the radar-based estimates (small biases) and the limitations of the passive microwave algorithm (relatively large biases). Comparison with surface gauge information from western Pacific Ocean atolls shows a negative bias (16%) for all the TRMM products, although the representativeness of the atoll gauges of open-ocean rainfall is still in question.

Bias adjustment of infrared-based rainfall estimation using Passive Microwave satellite rainfall data

NASA Astrophysics Data System (ADS)

Karbalaee, Negar; Hsu, Kuolin; Sorooshian, Soroosh; Braithwaite, Dan

2017-04-01

This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). The PERSIANN-CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN-CCS is one of the algorithms used in the Integrated Multisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN-CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO-based PERSIANN-CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO-based PERSIANN-CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN-CCS rainfall estimation is obtained.

On the synergy of SMAP, AMSR2 AND SENTINEL-1 for retrieving soil moisture

NASA Astrophysics Data System (ADS)

Santi, E.; Paloscia, S.; Pettinato, S.; Brocca, L.; Ciabatta, L.; Entekhabi, D.

2018-03-01

An algorithm for retrieving soil moisture content (SMC) from synergic use of both active and passive microwave acquisitions is presented. The algorithm takes advantage of the integration of microwave data from SMAP, Sentinel-1 and AMSR2 for overcoming the SMAP radar failure and obtaining a SMC product at enhanced resolution (0.1° × 0.1°) and improved accuracy with respect to the original SMAP radiometric SMC product. A disaggregation technique based on the Smoothing filter based intensity modulation (SFIM) allows combining the radiometric and SAR data. Disaggregated microwave data are used as inputs of an Artificial Neural Networks (ANN) based algorithm, which is able to exploit the synergy between active and passive acquisitions. The algorithm is defined, trained and tested using the SMEX02 experimental dataset and data simulated by forward electromagnetic models based on the Radiative Transfer Theory. Then the algorithm is adapted to satellite data and tested using one year of SMAP, AMSR2 and Sentinel-1 co-located data on a flat agricultural area located in the Po Valley, in northern Italy. Spatially distributed SMC values at 0.1° × 0.1° resolution generated by the Soil Water Balance Model (SWBM) are considered as reference for this purpose. The synergy of SMAP, Sentinel-1 and AMSR2 allowed increasing the correlation between estimated and reference SMC from R ≅ 0.68 of the SMAP based retrieval up to R ≅ 0.86 of the combination SMAP + Sentinel-1 + AMSR2. The corresponding Root Mean Square Error (RMSE) decreased from RMSE ≅ 0.04 m3/m3 to RMSE ≅ 0.024 m3/m3.

Evaluation of SMAP Level 2 Soil Moisture Algorithms Using SMOS Data

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas J.; Zhao, Tianjie; Cosh, Michael; Chan, Steven; O'Neill, Peggy; Njoku, Eni; Colliander, Andreas; Kerr, Yann; Shi, J. C.

2011-01-01

The objectives of the SMAP (Soil Moisture Active Passive) mission are global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolution, respectively. SMAP will provide soil moisture with a spatial resolution of 10 km with a 3-day revisit time at an accuracy of 0.04 m3/m3 [1]. In this paper we contribute to the development of the Level 2 soil moisture algorithm that is based on passive microwave observations by exploiting Soil Moisture Ocean Salinity (SMOS) satellite observations and products. SMOS brightness temperatures provide a global real-world, rather than simulated, test input for the SMAP radiometer-only soil moisture algorithm. Output of the potential SMAP algorithms will be compared to both in situ measurements and SMOS soil moisture products. The investigation will result in enhanced SMAP pre-launch algorithms for soil moisture.

Passive Microwave Remote Sensing of Colorado Watersheds Using Calibrated, Enhanced-Resolution Brightness Temperatures (CETB) from AMSR-E and SSM/I for Estimation of Snowmelt Timing

NASA Astrophysics Data System (ADS)

Johnson, M.; Ramage, J. M.; Troy, T. J.; Brodzik, M. J.

2017-12-01

Understanding the timing of snowmelt is critical for water resources management in snow-dominated watersheds. Passive microwave remote sensing has been used to estimate melt-refreeze events through brightness temperature satellite observations taken with sensors like the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E). Previous studies were limited to lower resolution ( 25 km) datasets, making it difficult to quantify the snowpack in heterogeneous, high-relief areas. This study investigates the use of newly available passive microwave calibrated, enhanced-resolution brightness temperatures (CETB) produced at the National Snow and Ice Data Center to estimate melt timing at much higher spatial resolution ( 3-6 km). CETB datasets generated from SSM/I and AMSR-E records will be used to examine three mountainous basins in Colorado. The CETB datasets retain twice-daily (day/night) observations of brightness temperatures. Therefore, we employ the diurnal amplitude variation (DAV) method to detect melt onset and melt occurrences to determine if algorithms developed for legacy data are valid with the improved CETB dataset. We compare melt variability with nearby stream discharge records to determine an optimum melt onset algorithm using the newly reprocessed data. This study investigates the effectiveness of the CETB product for several locations in Colorado (North Park, Rabbit Ears, Fraser) that were the sites of previous ground/airborne surveys during the NASA Cold Land Processes Field Experiment (CLPX 2002-2003). In summary, this work lays the foundation for the utilization of higher resolution reprocessed CETB data for snow evolution more broadly in a range of environments. Consequently, the new processing methods and improved spatial resolution will enable hydrologists to better analyze trends in snow-dominated mountainous watersheds for more effective water resources management.

Interannual and Decadal Variability of Ocean Surface Latent Heat Flux as Seen from Passive Microwave Satellite Algorithms

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Jackson, Darren L.; Wick, Gary A.; Roberts, Brent; Miller, Tim L.

2007-01-01

Ocean surface turbulent fluxes are critical links in the climate system since they mediate energy exchange between the two fluid systems (ocean and atmosphere) whose combined heat transport determines the basic character of Earth's climate. Deriving physically-based latent and sensible heat fluxes from satellite is dependent on inferences of near surface moisture and temperature from coarser layer retrievals or satellite radiances. Uncertainties in these "retrievals" propagate through bulk aerodynamic algorithms, interacting as well with error properties of surface wind speed, also provided by satellite. By systematically evaluating an array of passive microwave satellite algorithms, the SEAFLUX project is providing improved understanding of these errors and finding pathways for reducing or eliminating them. In this study we focus on evaluating the interannual variability of several passive microwave-based estimates of latent heat flux starting from monthly mean gridded data. The algorithms considered range from those based essentially on SSM/I (e.g. HOAPS) to newer approaches that consider additional moisture information from SSM/T-2 or AMSU-B and lower tropospheric temperature data from AMSU-A. On interannual scales, variability arising from ENSO events and time-lagged responses of ocean turbulent and radiative fluxes in other ocean basins (as well as the extratropical Pacific) is widely recognized, but still not well quantified. Locally, these flux anomalies are of order 10-20 W/sq m and present a relevant "target" with which to verify algorithm performance in a climate context. On decadal time scales there is some evidence from reanalyses and remotely-sensed fluxes alike that tropical ocean-averaged latent heat fluxes have increased 5-10 W/sq m since the early 1990s. However, significant uncertainty surrounds this estimate. Our work addresses the origin of these uncertainties and provides statistics on time series of tropical ocean averages, regional space / time correlation analysis, and separation of contributions by variations in wind and near surface humidity deficit. Comparison to variations in reanalysis data sets is also provided for reference.

Mapping and Assessing Variability in the Antarctic Marginal Ice Zone, the Pack Ice and Coastal Polynyas

NASA Astrophysics Data System (ADS)

Stroeve, Julienne; Jenouvrier, Stephanie

2016-04-01

Sea ice variability within the marginal ice zone (MIZ) and polynyas plays an important role for phytoplankton productivity and krill abundance. Therefore mapping their spatial extent, seasonal and interannual variability is essential for understanding how current and future changes in these biological active regions may impact the Antarctic marine ecosystem. Knowledge of the distribution of different ice types to the total Antarctic sea ice cover may also help to shed light on the factors contributing towards recent expansion of the Antarctic ice cover in some regions and contraction in others. The long-term passive microwave satellite data record provides the longest and most consistent data record for assessing different ice types. However, estimates of the amount of MIZ, consolidated pack ice and polynyas depends strongly on what sea ice algorithm is used. This study uses two popular passive microwave sea ice algorithms, the NASA Team and Bootstrap to evaluate the distribution and variability in the MIZ, the consolidated pack ice and coastal polynyas. Results reveal the NASA Team algorithm has on average twice the MIZ and half the consolidated pack ice area as the Bootstrap algorithm. Polynya area is also larger in the NASA Team algorithm, and the timing of maximum polynya area may differ by as much as 5 months between algorithms. These differences lead to different relationships between sea ice characteristics and biological processes, as illustrated here with the breeding success of an Antarctic seabird.

Simulation of Seasonal Snow Microwave TB Using Coupled Multi-Layered Snow Evolution and Microwave Emission Models

NASA Technical Reports Server (NTRS)

Brucker, Ludovic; Royer, Alain; Picard, Ghislain; Langlois, Alex; Fily, Michel

2014-01-01

The accurate quantification of SWE has important societal benefits, including improving domestic and agricultural water planning, flood forecasting and electric power generation. However, passive-microwave SWE algorithms suffer from variations in TB due to snow metamorphism, difficult to distinguish from those due to SWE variations. Coupled snow evolution-emission models are able to predict snow metamorphism, allowing us to account for emissivity changes. They can also be used to identify weaknesses in the snow evolution model. Moreover, thoroughly evaluating coupled models is a contribution toward the assimilation of TB, which leads to a significant increase in the accuracy of SWE estimates.

Foundations for statistical-physical precipitation retrieval from passive microwave satellite measurements. I - Brightness-temperature properties of a time-dependent cloud-radiation model

NASA Technical Reports Server (NTRS)

Smith, Eric A.; Mugnai, Alberto; Cooper, Harry J.; Tripoli, Gregory J.; Xiang, Xuwu

1992-01-01

The relationship between emerging microwave brightness temperatures (T(B)s) and vertically distributed mixtures of liquid and frozen hydrometeors was investigated, using a cloud-radiation model, in order to establish the framework for a hybrid statistical-physical rainfall retrieval algorithm. Although strong relationships were found between the T(B) values and various rain parameters, these correlations are misleading in that the T(B)s are largely controlled by fluctuations in the ice-particle mixing ratios, which in turn are highly correlated to fluctuations in liquid-particle mixing ratios. However, the empirically based T(B)-rain-rate (T(B)-RR) algorithms can still be used as tools for estimating precipitation if the hydrometeor profiles used for T(B)-RR algorithms are not specified in an ad hoc fashion.

Effects of weather on the retrieval of sea ice concentration and ice type from passive microwave data

NASA Technical Reports Server (NTRS)

Maslanik, J. A.

1992-01-01

Effects of wind, water vapor, and cloud liquid water on ice concentration and ice type calculated from passive microwave data are assessed through radiative transfer calculations and observations. These weather effects can cause overestimates in ice concentration and more substantial underestimates in multi-year ice percentage by decreasing polarization and by decreasing the gradient between frequencies. The effect of surface temperature and air temperature on the magnitudes of weather-related errors is small for ice concentration and substantial for multiyear ice percentage. The existing weather filter in the NASA Team Algorithm addresses only weather effects over open ocean; the additional use of local open-ocean tie points and an alternative weather correction for the marginal ice zone can further reduce errors due to weather. Ice concentrations calculated using 37 versus 18 GHz data show little difference in total ice covered area, but greater differences in intermediate concentration classes. Given the magnitude of weather-related errors in ice classification from passive microwave data, corrections for weather effects may be necessary to detect small trends in ice covered area and ice type for climate studies.

Glacial Boundary Features Delineated Using Enhanced-resolution Passive-microwave Data to Determine Melt Season Variation of the Vatnajokull Ice Cap, Iceland

NASA Astrophysics Data System (ADS)

Marzillier, D. M.; Ramage, J. M.

2017-12-01

Temperate glaciers such as those seen in Iceland experience high annual mass flux, thereby responding to small scale changes in Earth's climate. Decadal changes in the glacial margins of Iceland's ice caps are observable in the Landsat record, however twice daily AMSR-E Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) allow for observation on a daily temporal scale and a 3.125 km spatial scale, which can in turn be connected to patterns seen over longer periods of time. Passive microwave data allow for careful observation of melt onset and duration in Iceland's glacial regions by recording changes in emissivity of the ice surface, known as brightness temperature (TB), which is sensitive to fluctuations in the liquid water content of snow and ice seen during melting in glaciated regions. Enhanced resolution of this data set allows for a determination of a threshold that defines the melting season. The XPGR snowmelt algorithm originally presented by Abdalati and Steffen (1995) is used as a comparison with the diurnal amplitude variation (DAV) values on Iceland's Vatnajokull ice cap located at 64.4N, -16.8W. Ground-based air temperature data in this region, digital elevation models (DEMs), and river discharge dominated by glacial runoff are used to confirm the glacial response to changes in global climate. Results show that Iceland glaciers have a bimodal distribution of brightness temperature delineating when the snow/ice is melting and refreezing. Ground based temperatures have increased on a decadal trend. Clear glacial boundaries are visible on the passive microwave delineating strong features, and we are working to understand their variability and contribution to glacier evolution. The passive microwave data set allows connections to be made between observations seen on a daily scale and the long term glacier changes observed by the Landsat satellite record that integrates the overall glacier changes.

Evaluation of Radar Vegetation Indices for Vegetation Water Content Estimation Using Data from a Ground-Based SMAP Simulator

NASA Technical Reports Server (NTRS)

Srivastava, Prashant K.; O'Neill, Peggy; Cosh, Michael; Lang, Roger; Joseph, Alicia

2015-01-01

Vegetation water content (VWC) is an important component of microwave soil moisture retrieval algorithms. This paper aims to estimate VWC using L band active and passive radar/radiometer datasets obtained from a NASA ground-based Soil Moisture Active Passive (SMAP) simulator known as ComRAD (Combined Radar/Radiometer). Several approaches to derive vegetation information from radar and radiometer data such as HH, HV, VV, Microwave Polarization Difference Index (MPDI), HH/VV ratio, HV/(HH+VV), HV/(HH+HV+VV) and Radar Vegetation Index (RVI) are tested for VWC estimation through a generalized linear model (GLM). The overall analysis indicates that HV radar backscattering could be used for VWC content estimation with highest performance followed by HH, VV, MPDI, RVI, and other ratios.

The Passive Microwave Neural Network Precipitation Retrieval (PNPR) for AMSU/MHS and ATMS cross-track scanning radiometers

NASA Astrophysics Data System (ADS)

Sano', Paolo; Casella, Daniele; Panegrossi, Giulia; Cinzia Marra, Anna; Dietrich, Stefano

2016-04-01

Spaceborne microwave cross-track scanning radiometers, originally developed for temperature and humidity sounding, have shown great capabilities to provide a significant contribution in precipitation monitoring both in terms of measurement quality and spatial/temporal coverage. The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for cross-track scanning radiometers, originally developed for the Advanced Microwave Sounding Unit/Microwave Humidity Sounder (AMSU-A/MHS) radiometers (on board the European MetOp and U.S. NOAA satellites), was recently newly designed to exploit the Advanced Technology Microwave Sounder (ATMS) on board the Suomi-NPP satellite and the future JPSS satellites. The PNPR algorithm is based on the Artificial Neural Network (ANN) approach. The main PNPR-ATMS algorithm changes with respect to PNPR-AMSU/MHS are the design and implementation of a new ANN able to manage the information derived from the additional ATMS channels (respect to the AMSU-A/MHS radiometer) and a new screening procedure for not-precipitating pixels. In order to achieve maximum consistency of the retrieved surface precipitation, both PNPR algorithms are based on the same physical foundation. The PNPR is optimized for the European and the African area. The neural network was trained using a cloud-radiation database built upon 94 cloud-resolving simulations over Europe and the Mediterranean and over the African area and radiative transfer model simulations of TB vectors consistent with the AMSU-A/MHS and ATMS channel frequencies, viewing angles, and view-angle dependent IFOV sizes along the scan projections. As opposed to other ANN precipitation retrieval algorithms, PNPR uses a unique ANN that retrieves the surface precipitation rate for all types of surface backgrounds represented in the training database, i.e., land (vegetated or arid), ocean, snow/ice or coast. This approach prevents different precipitation estimates from being inconsistent with one another when an observed precipitation system extends over two or more types of surfaces. As input data, the PNPR algorithm incorporates the TBs from selected channels, and various additional TBs-derived variables. Ancillary geographical/geophysical inputs (i.e., latitude, terrain height, surface type, season) are also considered during the training phase. The PNPR algorithm outputs consist of both the surface precipitation rate (along with the information on precipitation phase: liquid, mixed, solid) and a pixel-based quality index. We will illustrate the main features of the PNPR algorithm and will show results of a verification study over Europe and Africa. The study is based on the available ground-based radar and/or rain gauge network observations over the European area. In addition, results of the comparison with rainfall products available from the NASA/JAXA Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) (over the African area) and Global Precipitation Measurement (GPM) Dual frequency Precipitation Radar (DPR) will be shown. The analysis is built upon a two-years coincidence dataset of AMSU/MHS and ATMS observations with PR (2013-2014) and DPR (2014-2015). The PNPR is developed within the EUMETSAT H/SAF program (Satellite Application Facility for Operational Hydrology and Water Management), where it is used operationally towards the full exploitation of all microwave radiometers available in the GPM era. The algorithm will be tailored to the future European Microwave Sounder (MWS) onboard the MetOp-Second Generation (MetOp-SG) satellites.

High resolution change estimation of soil moisture and its assimilation into a land surface model

NASA Astrophysics Data System (ADS)

Narayan, Ujjwal

Near surface soil moisture plays an important role in hydrological processes including infiltration, evapotranspiration and runoff. These processes depend non-linearly on soil moisture and hence sub-pixel scale soil moisture variability characterization is important for accurate modeling of water and energy fluxes at the pixel scale. Microwave remote sensing has evolved as an attractive technique for global monitoring of near surface soil moisture. A radiative transfer model has been tested and validated for soil moisture retrieval from passive microwave remote sensing data under a full range of vegetation water content conditions. It was demonstrated that soil moisture retrieval errors of approximately 0.04 g/g gravimetric soil moisture are attainable with vegetation water content as high as 5 kg/m2. Recognizing the limitation of low spatial resolution associated with passive sensors, an algorithm that uses low resolution passive microwave (radiometer) and high resolution active microwave (radar) data to estimate soil moisture change at the spatial resolution of radar operation has been developed and applied to coincident Passive and Active L and S band (PALS) and Airborne Synthetic Aperture Radar (AIRSAR) datasets acquired during the Soil Moisture Experiments in 2002 (SMEX02) campaign with root mean square error of 10% and a 4 times enhancement in spatial resolution. The change estimation algorithm has also been used to estimate soil moisture change at 5 km resolution using AMSR-E soil moisture product (50 km) in conjunction with the TRMM-PR data (5 km) for a 3 month period demonstrating the possibility of high resolution soil moisture change estimation using satellite based data. Soil moisture change is closely related to precipitation and soil hydraulic properties. A simple assimilation framework has been implemented to investigate whether assimilation of surface layer soil moisture change observations into a hydrologic model will potentially improve it performance. Results indicate an improvement in model prediction of near surface and deep layer soil moisture content when the update is performed to the model state as compared to free model runs. It is also seen that soil moisture change assimilation is able to mitigate the effect of erroneous precipitation input data.

Evaluation of Passive Multilayer Cloud Detection Using Preliminary CloudSat and CALIPSO Cloud Profiles

NASA Astrophysics Data System (ADS)

Minnis, P.; Sun-Mack, S.; Chang, F.; Huang, J.; Nguyen, L.; Ayers, J. K.; Spangenberg, D. A.; Yi, Y.; Trepte, C. R.

2006-12-01

During the last few years, several algorithms have been developed to detect and retrieve multilayered clouds using passive satellite data. Assessing these techniques has been difficult due to the need for active sensors such as cloud radars and lidars that can "see" through different layers of clouds. Such sensors have been available only at a few surface sites and on aircraft during field programs. With the launch of the CALIPSO and CloudSat satellites on April 28, 2006, it is now possible to observe multilayered systems all over the globe using collocated cloud radar and lidar data. As part of the A- Train, these new active sensors are also matched in time ad space with passive measurements from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer - EOS (AMSR-E). The Clouds and the Earth's Radiant Energy System (CERES) has been developing and testing algorithms to detect ice-over-water overlapping cloud systems and to retrieve the cloud liquid path (LWP) and ice water path (IWP) for those systems. One technique uses a combination of the CERES cloud retrieval algorithm applied to MODIS data and a microwave retrieval method applied to AMSR-E data. The combination of a CO2-slicing cloud retireval technique with the CERES algorithms applied to MODIS data (Chang et al., 2005) is used to detect and analyze such overlapped systems that contain thin ice clouds. A third technique uses brightness temperature differences and the CERES algorithms to detect similar overlapped methods. This paper uses preliminary CloudSat and CALIPSO data to begin a global scale assessment of these different methods. The long-term goals are to assess and refine the algorithms to aid the development of an optimal combination of the techniques to better monitor ice 9and liquid water clouds in overlapped conditions.

Evaluation of Passive Multilayer Cloud Detection Using Preliminary CloudSat and CALIPSO Cloud Profiles

NASA Astrophysics Data System (ADS)

Minnis, P.; Sun-Mack, S.; Chang, F.; Huang, J.; Nguyen, L.; Ayers, J. K.; Spangenberg, D. A.; Yi, Y.; Trepte, C. R.

2005-05-01

During the last few years, several algorithms have been developed to detect and retrieve multilayered clouds using passive satellite data. Assessing these techniques has been difficult due to the need for active sensors such as cloud radars and lidars that can "see" through different layers of clouds. Such sensors have been available only at a few surface sites and on aircraft during field programs. With the launch of the CALIPSO and CloudSat satellites on April 28, 2006, it is now possible to observe multilayered systems all over the globe using collocated cloud radar and lidar data. As part of the A- Train, these new active sensors are also matched in time ad space with passive measurements from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer - EOS (AMSR-E). The Clouds and the Earth's Radiant Energy System (CERES) has been developing and testing algorithms to detect ice-over-water overlapping cloud systems and to retrieve the cloud liquid path (LWP) and ice water path (IWP) for those systems. One technique uses a combination of the CERES cloud retrieval algorithm applied to MODIS data and a microwave retrieval method applied to AMSR-E data. The combination of a CO2-slicing cloud retireval technique with the CERES algorithms applied to MODIS data (Chang et al., 2005) is used to detect and analyze such overlapped systems that contain thin ice clouds. A third technique uses brightness temperature differences and the CERES algorithms to detect similar overlapped methods. This paper uses preliminary CloudSat and CALIPSO data to begin a global scale assessment of these different methods. The long-term goals are to assess and refine the algorithms to aid the development of an optimal combination of the techniques to better monitor ice 9and liquid water clouds in overlapped conditions.

Passive Microwave Precipitation Retrieval Uncertainty Characterized based on Field Campaign Data over Complex Terrain

NASA Astrophysics Data System (ADS)

Derin, Y.; Anagnostou, E. N.; Anagnostou, M.; Kalogiros, J. A.; Casella, D.; Marra, A. C.; Panegrossi, G.; Sanò, P.

2017-12-01

Difficulties in representation of high rainfall variability over mountainous areas using ground based sensors make satellite remote sensing techniques attractive for hydrologic studies over these regions. Even though satellite-based rainfall measurements are quasi global and available at high spatial resolution, these products have uncertainties that necessitate use of error characterization and correction procedures based upon more accurate in situ rainfall measurements. Such measurements can be obtained from field campaigns facilitated by research quality sensors such as locally deployed weather radar and in situ weather stations. This study uses such high quality and resolution rainfall estimates derived from dual-polarization X-band radar (XPOL) observations from three field experiments in Mid-Atlantic US East Coast (NASA IPHEX experiment), the Olympic Peninsula of Washington State (NASA OLYMPEX experiment), and the Mediterranean to characterize the error characteristics of multiple passive microwave (PMW) sensor retrievals. The study first conducts an independent error analysis of the XPOL radar reference rainfall fields against in situ rain gauges and disdrometer observations available by the field experiments. Then the study evaluates different PMW precipitation products using the XPOL datasets (GR) over the three aforementioned complex terrain study areas. We extracted matchups of PMW/GR rainfall based on a matching methodology that identifies GR volume scans coincident with PMW field-of-view sampling volumes, and scaled GR parameters to the satellite products' nominal spatial resolution. The following PMW precipitation retrieval algorithms are evaluated: the NASA Goddard PROFiling algorithm (GPROF), standard and climatology-based products (V 3, 4 and 5) from four PMW sensors (SSMIS, MHS, GMI, and AMSR2), and the precipitation products based on the algorithms Cloud Dynamics and Radiation Database (CDRD) for SSMIS and Passive microwave Neural network Precipitation Retrieval (PNPR) for AMSU/MHS, developed at ISAC-CNR within the EUMETSAT H-SAF. We will present error analysis results for the different PMW rainfall retrievals and discuss dependences on precipitation type, elevation and precipitation microphysics (derived from XPOL).

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part 2; Evaluation of Estimates Using Independent Data

NASA Technical Reports Server (NTRS)

Yang, Song; Olson, William S.; Wang, Jian-Jian; Bell, Thomas L.; Smith, Eric A.; Kummerow, Christian D.

2004-01-01

Rainfall rate estimates from space-borne k&ents are generally accepted as reliable by a majority of the atmospheric science commu&y. One-of the Tropical Rainfall Measuring Mission (TRh4M) facility rain rate algorithms is based upon passive microwave observations fiom the TRMM Microwave Imager (TMI). Part I of this study describes improvements in the TMI algorithm that are required to introduce cloud latent heating and drying as additional algorithm products. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, OP5resolution estimates of surface rain rate over ocean fiom the improved TMI algorithm are well correlated with independent radar estimates (r approx. 0.88 over the Tropics), but bias reduction is the most significant improvement over forerunning algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm, and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly, 2.5 deg. -resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data are limited, TMI estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with: (a) additional contextual information brought to the estimation problem, and/or; (b) physically-consistent and representative databases supporting the algorithm. A model of the random error in instantaneous, 0.5 deg-resolution rain rate estimates appears to be consistent with the levels of error determined from TMI comparisons to collocated radar. Error model modifications for non-raining situations will be required, however. Sampling error appears to represent only a fraction of the total error in monthly, 2S0-resolution TMI estimates; the remaining error is attributed to physical inconsistency or non-representativeness of cloud-resolving model simulated profiles supporting the algorithm.

Estimating the Thickness of Sea Ice Snow Cover in the Weddell Sea from Passive Microwave Brightness Temperatures

NASA Technical Reports Server (NTRS)

Arrigo, K. R.; vanDijken, G. L.; Comiso, J. C.

1996-01-01

Passive microwave satellite observations have frequently been used to observe changes in sea ice cover and concentration. Comiso et al. showed that there may also be a direct relationship between the thickness of snow cover (h(sub s)) on ice and microwave emissivity at 90 GHz. Because the in situ experiment of experiment of Comiso et al. was limited to a single station, the relationship is re-examined in this paper in a more general context and using more extensive in situ microwave observations and measurements of h from the Weddell Sea 1986 and 1989 winter cruises. Good relationships were found to exist between h(sub s) sand the emissivity at 90 GHz - 10 GHz and the emissivity at 90 GHz - 18.7 GHz when the standard deviation of h(sub s) was less than 50% of the mean and when h(sub s) was less than 0.25 m. The reliance of these relationships on h(sub s) is most likely caused by the limited penetration through the snow of radiation at 90 GHz. When the algorithm was applied to the Special Sensor Microwave/Imager (SSM/I) satellite data from the Weddell Sea, the resulting mean h(sub s) agreed within 5% of the mean calculated from greater than 1400 in situ observations.

Wideband Agile Digital Microwave Radiometer

NASA Technical Reports Server (NTRS)

Gaier, Todd C.; Brown, Shannon T.; Ruf, Christopher; Gross, Steven

2012-01-01

The objectives of this work were to take the initial steps needed to develop a field programmable gate array (FPGA)- based wideband digital radiometer backend (>500 MHz bandwidth) that will enable passive microwave observations with minimal performance degradation in a radiofrequency-interference (RFI)-rich environment. As manmade RF emissions increase over time and fill more of the microwave spectrum, microwave radiometer science applications will be increasingly impacted in a negative way, and the current generation of spaceborne microwave radiometers that use broadband analog back ends will become severely compromised or unusable over an increasing fraction of time on orbit. There is a need to develop a digital radiometer back end that, for each observation period, uses digital signal processing (DSP) algorithms to identify the maximum amount of RFI-free spectrum across the radiometer band to preserve bandwidth to minimize radiometer noise (which is inversely related to the bandwidth). Ultimately, the objective is to incorporate all processing necessary in the back end to take contaminated input spectra and produce a single output value free of manmade signals to minimize data rates for spaceborne radiometer missions. But, to meet these objectives, several intermediate processing algorithms had to be developed, and their performance characterized relative to typical brightness temperature accuracy re quirements for current and future microwave radiometer missions, including those for measuring salinity, soil moisture, and snow pack.

A microwave systems approach to measuring root zone soil moisture

NASA Technical Reports Server (NTRS)

Newton, R. W.; Paris, J. F.; Clark, B. V.

1983-01-01

Computer microwave satellite simulation models were developed and the program was used to test the ability of a coarse resolution passive microwave sensor to measure soil moisture over large areas, and to evaluate the effect of heterogeneous ground covers with the resolution cell on the accuracy of the soil moisture estimate. The use of realistic scenes containing only 10% to 15% bare soil and significant vegetation made it possible to observe a 60% K decrease in brightness temperature from a 5% soil moisture to a 35% soil moisture at a 21 cm microwave wavelength, providing a 1.5 K to 2 K per percent soil moisture sensitivity to soil moisture. It was shown that resolution does not affect the basic ability to measure soil moisture with a microwave radiometer system. Experimental microwave and ground field data were acquired for developing and testing a root zone soil moisture prediction algorithm. The experimental measurements demonstrated that the depth of penetration at a 21 cm microwave wavelength is not greater than 5 cm.

Integrated approach using multi-platform sensors for enhanced high-resolution daily ice cover product

NASA Astrophysics Data System (ADS)

Bonev, George; Gladkova, Irina; Grossberg, Michael; Romanov, Peter; Helfrich, Sean

2016-09-01

The ultimate objective of this work is to improve characterization of the ice cover distribution in the polar areas, to improve sea ice mapping and to develop a new automated real-time high spatial resolution multi-sensor ice extent and ice edge product for use in operational applications. Despite a large number of currently available automated satellite-based sea ice extent datasets, analysts at the National Ice Center tend to rely on original satellite imagery (provided by satellite optical, passive microwave and active microwave sensors) mainly because the automated products derived from satellite optical data have gaps in the area coverage due to clouds and darkness, passive microwave products have poor spatial resolution, automated ice identifications based on radar data are not quite reliable due to a considerable difficulty in discriminating between the ice cover and rough ice-free ocean surface due to winds. We have developed a multisensor algorithm that first extracts maximum information on the sea ice cover from imaging instruments VIIRS and MODIS, including regions covered by thin, semitransparent clouds, then supplements the output by the microwave measurements and finally aggregates the results into a cloud gap free daily product. This ability to identify ice cover underneath thin clouds, which is usually masked out by traditional cloud detection algorithms, allows for expansion of the effective coverage of the sea ice maps and thus more accurate and detailed delineation of the ice edge. We have also developed a web-based monitoring system that allows comparison of our daily ice extent product with the several other independent operational daily products.

Full Wave Analysis of Passive Microwave Monolithic Integrated Circuit Devices Using a Generalized Finite Difference Time Domain (GFDTD) Algorithm

NASA Technical Reports Server (NTRS)

Lansing, Faiza S.; Rascoe, Daniel L.

1993-01-01

This paper presents a modified Finite-Difference Time-Domain (FDTD) technique using a generalized conformed orthogonal grid. The use of the Conformed Orthogonal Grid, Finite Difference Time Domain (GFDTD) enables the designer to match all the circuit dimensions, hence eliminating a major source o error in the analysis.

Snowmelt Pattern and Lake Ice Phenology around Tibetan Plateau Estimated from Enhanced Resolution Passive Microwave Data

NASA Astrophysics Data System (ADS)

Xiong, C.; Shi, J.; Wang, T.

2017-12-01

Snow and ice is very sensitive to the climate change. Rising air temperature will cause the snowmelt time change. In contrast, the change in snow state will have feedback on climate through snow albedo. The snow melt timing is also correlated with the associated runoff. Ice phenology describes the seasonal cycle of lake ice cover and includes freeze-up and breakup periods and ice cover duration, which is an important weather and climate indicator. It is also important for lake-atmosphere interactions and hydrological and ecological processes. The enhanced resolution (up to 3.125 km) passive microwave data is used to estimate the snowmelt pattern and lake ice phenology on and around Tibetan Plateau. The enhanced resolution makes the estimation of snowmelt and lake ice phenology in more spatial detail compared to previous 25 km gridded passive microwave data. New algorithm based on smooth filters and change point detection was developed to estimate the snowmelt and lake ice freeze-up and break-up timing. Spatial and temporal pattern of snowmelt and lake ice phonology are estimated. This study provides an objective evidence of climate change impact on the cryospheric system on Tibetan Plateau. The results show significant earlier snowmelt and lake ice break-up in some regions.

Calibration of Passive Microwave Polarimeters that Use Hybrid Coupler-Based Correlators

NASA Technical Reports Server (NTRS)

Piepmeier, J. R.

2003-01-01

Four calibration algorithms are studied for microwave polarimeters that use hybrid coupler-based correlators: 1) conventional two-look of hot and cold sources, 2) three looks of hot and cold source combinations, 3) two-look with correlated source, and 4) four-look combining methods 2 and 3. The systematic errors are found to depend on the polarimeter component parameters and accuracy of calibration noise temperatures. A case study radiometer in four different remote sensing scenarios was considered in light of these results. Applications for Ocean surface salinity, Ocean surface winds, and soil moisture were found to be sensitive to different systematic errors. Finally, a standard uncertainty analysis was performed on the four-look calibration algorithm, which was found to be most sensitive to the correlated calibration source.

A Review on Passive and Integrated Near-Field Microwave Biosensors

PubMed Central

Guha, Subhajit; Jamal, Farabi Ibne

2017-01-01

In this paper we review the advancement of passive and integrated microwave biosensors. The interaction of microwave with biological material is discussed in this paper. Passive microwave biosensors are microwave structures, which are fabricated on a substrate and are used for sensing biological materials. On the other hand, integrated biosensors are microwave structures fabricated in standard semiconductor technology platform (CMOS or BiCMOS). The CMOS or BiCMOS sensor technology offers a more compact sensing approach which has the potential in the future for point of care testing systems. Various applications of the passive and the integrated sensors have been discussed in this review paper. PMID:28946617

Research support of the WETNET Program

NASA Technical Reports Server (NTRS)

Estes, John E.; Mcgwire, Kenneth C.; Scepan, Joseph; Henderson, SY; Lawless, Michael

1995-01-01

This study examines various aspects of the Microwave Vegetation Index (MVI). MVI is a derived signal created by differencing the spectral response of the 37 GHz horizontally and vertically polarized passive microwave signals. The microwave signal employed to derive this index is thought to be primarily influenced by vegetation structure, vegetation growth, standing water, and precipitation. The state of California is the study site for this research. Imagery from the Special Sensor Microwave/Imager (SSM/I) is used for the creation of MVI datasets analyzed in this research. The object of this research is to determine whether MVI corresponds with some quantifiable vegetation parameter (such as vegetation density) or whether the index is more affected by known biogeophysical parameters such antecedent precipitation. A secondary question associated with the above is whether the vegetation attributes that MVI is employed to determine can be more easily and accurately evaluated by other remote sensing means. An important associated question to be addressed in the study is the effect of different multi-temporal composting techniques on the derived MVI dataset. This work advances our understanding of the fundamental nature of MVI by studying vegetation as a mixture of structural types, such as forest and grassland. The study further advances our understanding by creating multitemporal precipitation datasets to compare the affects of precipitation upon MVI. This work will help to lay the groundwork for the use of passive microwave spectral information either as an adjunct to visible and near infrared imagery in areas where that is feasible or for the use of passive microwave alone in areas of moderate cloud coverage. In this research, an MVI dataset, spanning the period February 15, 1989 through April 25, 1990, has been created using National Aeronautic and Space Administration (NASA) supplied brightness temperature data. Information from the DMSP satellite 37 GHz wavelength SSM/I sensor in both horizontal and vertical polarization has been processed using the MVI algorithm. In conjunction with the MVI algorithm a multitemporal compositing technique was used to create datasets that correspond to 14 day periods. In this technical report, Section Two contains background information on the State of California and the three MVI study sites. Section Three describes the methods used to create the MVI and independent variables datasets. Section Four presents the results of the experiment. Section Five summarizes and concludes the work.

A Bayesian approach to microwave precipitation profile retrieval

NASA Technical Reports Server (NTRS)

Evans, K. Franklin; Turk, Joseph; Wong, Takmeng; Stephens, Graeme L.

1995-01-01

A multichannel passive microwave precipitation retrieval algorithm is developed. Bayes theorem is used to combine statistical information from numerical cloud models with forward radiative transfer modeling. A multivariate lognormal prior probability distribution contains the covariance information about hydrometeor distribution that resolves the nonuniqueness inherent in the inversion process. Hydrometeor profiles are retrieved by maximizing the posterior probability density for each vector of observations. The hydrometeor profile retrieval method is tested with data from the Advanced Microwave Precipitation Radiometer (10, 19, 37, and 85 GHz) of convection over ocean and land in Florida. The CP-2 multiparameter radar data are used to verify the retrieved profiles. The results show that the method can retrieve approximate hydrometeor profiles, with larger errors over land than water. There is considerably greater accuracy in the retrieval of integrated hydrometeor contents than of profiles. Many of the retrieval errors are traced to problems with the cloud model microphysical information, and future improvements to the algorithm are suggested.

Comparison of snow depth retrieval algorithm in Northeastern China based on AMSR2 and FY3B-MWRI data

NASA Astrophysics Data System (ADS)

Fan, Xintong; Gu, Lingjia; Ren, Ruizhi; Zhou, Tingting

2017-09-01

Snow accumulation has a very important influence on the natural environment and human activities. Meanwhile, improving the estimation accuracy of passive microwave snow depth (SD) retrieval is a hotspot currently. Northeastern China is a typical snow study area including many different land cover types, such as forest, grassland and farmland. Especially, there is relatively stable snow accumulation in January every year. The brightness temperatures which are observed by the Advanced Microwave Scanning Radiometer 2 (AMSR2) on GCOM-W1 and FengYun3B Microwave Radiation Imager (FY3B-MWRI) in the same period in 2013 are selected as the study data in the research. The results of snow depth retrieval using AMSR2 standard algorithm and Jiang's FY operational algorithm are compared in the research. Moreover, to validate the accuracy of the two algorithms, the retrieval results are compared with the SD data observed at the national meteorological stations in Northeastern China. Furthermore, the retrieval SD is also compared with AMSR2 and FY standard SD products, respectively. The root mean square errors (RMSE) results using AMSR2 standard algorithms and FY operational algorithm are close in the forest surface, which are 6.33cm and 6.28cm, respectively. However, The FY operational algorithm shows a better result than the AMSR2 standard algorithms in the grassland and farmland surface. The RMSE results using FY operational algorithm in the grassland and farmland surface are 2.44cm and 6.13cm, respectively.

Computer-Aided Design of Low-Noise Microwave Circuits

NASA Astrophysics Data System (ADS)

Wedge, Scott William

1991-02-01

Devoid of most natural and manmade noise, microwave frequencies have detection sensitivities limited by internally generated receiver noise. Low-noise amplifiers are therefore critical components in radio astronomical antennas, communications links, radar systems, and even home satellite dishes. A general technique to accurately predict the noise performance of microwave circuits has been lacking. Current noise analysis methods have been limited to specific circuit topologies or neglect correlation, a strong effect in microwave devices. Presented here are generalized methods, developed for computer-aided design implementation, for the analysis of linear noisy microwave circuits comprised of arbitrarily interconnected components. Included are descriptions of efficient algorithms for the simultaneous analysis of noisy and deterministic circuit parameters based on a wave variable approach. The methods are therefore particularly suited to microwave and millimeter-wave circuits. Noise contributions from lossy passive components and active components with electronic noise are considered. Also presented is a new technique for the measurement of device noise characteristics that offers several advantages over current measurement methods.

Application of SeaWinds Scatterometer and TMI-SSM/I Rain Rates to Hurricane Analysis and Forecasting

NASA Technical Reports Server (NTRS)

Atlas, Robert; Hou, Arthur; Reale, Oreste

2004-01-01

Results provided by two different assimilation methodologies involving data from passive and active space-borne microwave instruments are presented. The impact of the precipitation estimates produced by the TRMM Microwave Imager (TMI) and Special Sensor Microwave/Imager (SSM/I) in a previously developed 1D variational continuous assimilation algorithm for assimilating tropical rainfall is shown on two hurricane cases. Results on the impact of the SeaWinds scatterometer on the intensity and track forecast of a mid-Atlantic hurricane are also presented. This work is the outcome of a collaborative effort between NASA and NOAA and indicates the substantial improvement in tropical cyclone forecasting that can result from the assimilation of space-based data in global atmospheric models.

[The progress in retrieving land surface temperature based on thermal infrared and microwave remote sensing technologies].

PubMed

Zhang, Jia-Hua; Li, Xin; Yao, Feng-Mei; Li, Xian-Hua

2009-08-01

Land surface temperature (LST) is an important parameter in the study on the exchange of substance and energy between land surface and air for the land surface physics process at regional and global scales. Many applications of satellites remotely sensed data must provide exact and quantificational LST, such as drought, high temperature, forest fire, earthquake, hydrology and the vegetation monitor, and the models of global circulation and regional climate also need LST as input parameter. Therefore, the retrieval of LST using remote sensing technology becomes one of the key tasks in quantificational remote sensing study. Normally, in the spectrum bands, the thermal infrared (TIR, 3-15 microm) and microwave bands (1 mm-1 m) are important for retrieval of the LST. In the present paper, firstly, several methods for estimating the LST on the basis of thermal infrared (TIR) remote sensing were synthetically reviewed, i. e., the LST measured with an ground-base infrared thermometer, the LST retrieval from mono-window algorithm (MWA), single-channel algorithm (SCA), split-window techniques (SWT) and multi-channels algorithm(MCA), single-channel & multi-angle algorithm and multi-channels algorithm & multi-angle algorithm, and retrieval method of land surface component temperature using thermal infrared remotely sensed satellite observation. Secondly, the study status of land surface emissivity (epsilon) was presented. Thirdly, in order to retrieve LST for all weather conditions, microwave remotely sensed data, instead of thermal infrared data, have been developed recently, and the LST retrieval method from passive microwave remotely sensed data was also introduced. Finally, the main merits and shortcomings of different kinds of LST retrieval methods were discussed, respectively.

Aquarius salinity and wind retrieval using the cap algorithm and application to water cycle observation in the Indian ocean and subcontinent

USDA-ARS?s Scientific Manuscript database

Aquarius is a combined passive/active L-band microwave instrument developed to map the ocean surface salinity field from space. The primary science objective of this mission is to monitor the seasonal and interannual variation of the large scale features of the surface salinity field in the open oc...

Cloud cover determination in polar regions from satellite imagery

NASA Technical Reports Server (NTRS)

Barry, R. G.; Key, J.

1989-01-01

The objectives are to develop a suitable validation data set for evaluating the effectiveness of the International Satellite Cloud Climatology Project (ISCCP) algorithm for cloud retrieval in polar regions, to identify limitations of current procedures and to explore potential means to remedy them using textural classifiers, and to compare synoptic cloud data from model runs with observations. Toward the first goal, a polar data set consisting of visible, thermal, and passive microwave data was developed. The AVHRR and SMMR data were digitally merged to a polar stereographic projection with an effective pixel size of 5 sq km. With this data set, two unconventional methods of classifying the imagery for the analysis of polar clouds and surfaces were examined: one based on fuzzy sets theory and another based on a trained neural network. An algorithm for cloud detection was developed from an early test version of the ISCCP algorithm. This algorithm includes the identification of surface types with passive microwave, then temporal tests at each pixel location in the cloud detection phase. Cloud maps and clear sky radiance composites for 5 day periods are produced. Algorithm testing and validation was done with both actural AVHRR/SMMR data, and simulated imagery. From this point in the algorithm, groups of cloud pixels are examined for their spectral and textural characteristics, and a procedure is developed for the analysis of cloud patterns utilizing albedo, IR temperature, and texture. In a completion of earlier work, empirical analyses of arctic cloud cover were explored through manual interpretations of DMSP imagery and compared to U.S. Air Force 3D-nephanalysis. Comparisons of observed cloudiness from existing climatologies to patterns computed by the GISS climate model were also made.

A passive and active microwave-vector radiative transfer (PAM-VRT) model

NASA Astrophysics Data System (ADS)

Yang, Jun; Min, Qilong

2015-11-01

A passive and active microwave vector radiative transfer (PAM-VRT) package has been developed. This fast and accurate forward microwave model, with flexible and versatile input and output components, self-consistently and realistically simulates measurements/radiation of passive and active microwave sensors. The core PAM-VRT, microwave radiative transfer model, consists of five modules: gas absorption (two line-by-line databases and four fast models); hydrometeor property of water droplets and ice (spherical and nonspherical) particles; surface emissivity (from Community Radiative Transfer Model (CRTM)); vector radiative transfer of successive order of scattering (VSOS); and passive and active microwave simulation. The PAM-VRT package has been validated against other existing models, demonstrating good accuracy. The PAM-VRT not only can be used to simulate or assimilate measurements of existing microwave sensors, but also can be used to simulate observation results at some new microwave sensors.

Radio Frequency Interference Detection for Passive Remote Sensing Using Eigenvalue Analysis

NASA Technical Reports Server (NTRS)

Schoenwald, Adam; Kim, Seung-Jun; Mohammed-Tano, Priscilla

2017-01-01

Radio frequency interference (RFI) can corrupt passive remote sensing measurements taken with microwave radiometers. With the increasingly utilized spectrum and the push for larger bandwidth radiometers, the likelihood of RFI contamination has grown significantly. In this work, an eigenvalue-based algorithm is developed to detect the presence of RFI and provide estimates of RFI-free radiation levels. Simulated tests show that the proposed detector outperforms conventional kurtosis-based RFI detectors in the low-to-medium interferece-to-noise-power-ratio (INR) regime under continuous wave (CW) and quadrature phase shift keying (QPSK) RFIs.

Radio Frequency Interference Detection for Passive Remote Sensing Using Eigenvalue Analysis

NASA Technical Reports Server (NTRS)

Schoenwald, Adam J.; Kim, Seung-Jun; Mohammed, Priscilla N.

2017-01-01

Radio frequency interference (RFI) can corrupt passive remote sensing measurements taken with microwave radiometers. With the increasingly utilized spectrum and the push for larger bandwidth radiometers, the likelihood of RFI contamination has grown significantly. In this work, an eigenvalue-based algorithm is developed to detect the presence of RFI and provide estimates of RFI-free radiation levels. Simulated tests show that the proposed detector outperforms conventional kurtosis-based RFI detectors in the low-to-medium interference-to-noise-power-ratio (INR) regime under continuous wave (CW) and quadrature phase shift keying (QPSK) RFIs.

Towards improving the NASA standard soil moisture retrieval algorithm and product

NASA Astrophysics Data System (ADS)

Mladenova, I. E.; Jackson, T. J.; Njoku, E. G.; Bindlish, R.; Cosh, M. H.; Chan, S.

2013-12-01

Soil moisture mapping using passive-based microwave remote sensing techniques has proven to be one of the most effective ways of acquiring reliable global soil moisture information on a routine basis. An important step in this direction was made by the launch of the Advanced Microwave Scanning Radiometer on the NASA's Earth Observing System Aqua satellite (AMSR-E). Along with the standard NASA algorithm and operational AMSR-E product, the easy access and availability of the AMSR-E data promoted the development and distribution of alternative retrieval algorithms and products. Several evaluation studies have demonstrated issues with the standard NASA AMSR-E product such as dampened temporal response and limited range of the final retrievals and noted that the available global passive-based algorithms, even though based on the same electromagnetic principles, produce different results in terms of accuracy and temporal dynamics. Our goal is to identify the theoretical causes that determine the reduced sensitivity of the NASA AMSR-E product and outline ways to improve the operational NASA algorithm, if possible. Properly identifying the underlying reasons that cause the above mentioned features of the NASA AMSR-E product and differences between the alternative algorithms requires a careful examination of the theoretical basis of each approach. Specifically, the simplifying assumptions and parametrization approaches adopted by each algorithm to reduce the dimensionality of unknowns and characterize the observing system. Statistically-based error analyses, which are useful and necessary, provide information on the relative accuracy of each product but give very little information on the theoretical causes, knowledge that is essential for algorithm improvement. Thus, we are currently examining the possibility of improving the standard NASA AMSR-E global soil moisture product by conducting a thorough theoretically-based review of and inter-comparisons between several well established global retrieval techniques. A detailed discussion focused on the theoretical basis of each approach and algorithms sensitivity to assumptions and parametrization approaches will be presented. USDA is an equal opportunity provider and employer.

[Atmospheric Influences Analysis on the Satellite Passive Microwave Remote Sensing].

PubMed

Qiu, Yu-bao; Shi, Li-juan; Shi, Jian-cheng; Zhao, Shao-jie

2016-02-01

Passive microwave remote sensing offers its all-weather work capabilities, but atmospheric influences on satellite microwave brightness temperature were different under different atmospheric conditions and environments. In order to clarify atmospheric influences on Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), atmospheric radiation were simulated based on AMSR-E configuration under clear sky and cloudy conditions, by using radiative transfer model and atmospheric conditions data. Results showed that atmospheric water vapor was the major factor for atmospheric radiation under clear sky condition. Atmospheric transmittances were almost above 0.98 at AMSR-E's low frequencies (< 18.7 GHz) and the microwave brightness temperature changes caused by atmosphere can be ignored in clear sky condition. Atmospheric transmittances at 36.5 and 89 GHz were 0.896 and 0.756 respectively. The effects of atmospheric water vapor needed to be corrected when using microwave high-frequency channels to inverse land surface parameters in clear sky condition. But under cloud cover or cloudy conditions, cloud liquid water was the key factor to cause atmospheric radiation. When sky was covered by typical stratus cloud, atmospheric transmittances at 10.7, 18.7 and 36.5 GHz were 0.942, 0.828 and 0.605 respectively. Comparing with the clear sky condition, the down-welling atmospheric radiation caused by cloud liquid water increased up to 75.365 K at 36.5 GHz. It showed that the atmospheric correction under different clouds covered condition was the primary work to improve the accuracy of land surface parameters inversion of passive microwave remote sensing. The results also provided the basis for microwave atmospheric correction algorithm development. Finally, the atmospheric sounding data was utilized to calculate the atmospheric transmittance of Hailaer Region, Inner Mongolia province, in July 2013. The results indicated that atmospheric transmittances were close to 1 at C-band and X-band. 89 GHz was greatly influenced by water vapor and its atmospheric transmittance was not more than 0.7. Atmospheric transmittances in Hailaer Region had a relatively stable value in summer, but had about 0.1 fluctuations with the local water vapor changes.

Target detection using microwave irradiances from natural sources: A passive, local and global surveillance system

NASA Technical Reports Server (NTRS)

Stacey, J. M.

1984-01-01

Detection of metal objects on or near the Earth's surface was investigated using existing, passive, microwave sensors operating from Earth orbit. The range equations are derived from basic microwave principles and theories and the expressions are given explicitly to estimate the signal to noise ratio for detecting metal targets operating as bistatic scatterers. Actual measurements are made on a range of metal objects observed from orbit using existing passive microwave receiving systems. The details of the measurements and the results are tabulated and discussed. The advantages of a passive microwave sensor as it is applied to surveillance of metal objects as viewed from aerial platforms or from orbit, are examined.

Building the GPM-GV Column from the GPM Cold season Precipitation Experiment (Invited)

NASA Astrophysics Data System (ADS)

Nesbitt, S. W.; Duffy, G. A.; Gleicher, K.; McFarquhar, G. M.; Kulie, M.; Williams, C. R.; Petersen, W. A.; Munchak, S. J.; Tokay, A.; Skofronick Jackson, G.; Chandrasekar, C. V.; Kollias, P.; Hudak, D. R.; Tanelli, S.

2013-12-01

Within the context of the Drop Size Distribution Working Group (DSDWG) of the Global Precipitation Mission-Ground Validation (GPM-GV) program, a major science and satellite precipitation algorithm validation focus is on quantitatively determining the variability of microphysical properties of precipitation in the vertical column, as well as the radiative properties of those particles at GPM-relevant microwave frequencies. The GPM Cold season Precipitation Experiment, or GCPEx, was conducted to address both of these objectives in mid-latitude winter precipitation. Radar observations at C, X, Ku, Ka, and W band from ground based scanning radars, profiling radars, and aircraft, as well as an aircraft passive microwave imager from GCPEx, conducted in early 2012 near Barrie, Ontario, Canada, can be used to constrain the observed reflectivites and brightness temperatures in snow as well as construct radar dual frequency ratios (DFRs) that can be used to identify regimes of microwave radiative properties in observed hydrometeor columns. These data can be directly matched with aircraft and ground based in situ microphysical probes, such as 2-D and bulk aircraft probes and surface disdrometers, to place the microphysical and microwave scattering and emission properties of the snow in context throughout the column of hydrometeors. In this presentation, particle scattering regimes will be identified in GCPEx hydrometeor columns storm events using a clustering technique in a multi-frequency DFR-near Rayleigh radar reflectivity phase space using matched ground-based and aircraft-based radar and passive microwave data. These data will be interpreted using matched in situ disdrometer and aircraft probe microphysical data (particle size distributions, habit identification, fall speed, mass-diameter relationships) derived during the events analyzed. This database is geared towards evaluating scattering simulations and the choice of integral particle size distributions for snow precipitation retrieval algorithms for ground and spaceborne radars at relevant wavelengths. A comparison of results for different cases with varying synoptic forcing and microphysical evolution will be presented.

Towards SMOS: The 2006 National Airborne Field Experiment Plan

NASA Astrophysics Data System (ADS)

Walker, J. P.; Merlin, O.; Panciera, R.; Kalma, J. D.

2006-05-01

The 2006 National Airborne Field Experiment (NAFE) is the second in a series of two intensive experiments to be conducted in different parts of Australia. The NAFE'05 experiment was undertaken in the Goulburn River catchment during November 2005, with the objective to provide high resolution data for process level understanding of soil moisture retrieval, scaling and data assimilation. The NAFE'06 experiment will be undertaken in the Murrumbidgee catchment during November 2006, with the objective to provide data for SMOS (Soil Moisture and Ocean Salinity) level soil moisture retrieval, downscaling and data assimilation. To meet this objective, PLMR (Polarimetric L-band Multibeam Radiometer) and supporting instruments (TIR and NDVI) will be flown at an altitude of 10,000 ft AGL to provide 1km resolution passive microwave data (and 20m TIR) across a 50km x 50km area every 2-3 days. This will both simulate a SMOS pixel and provide the 1km soil moisture data required for downscale verification, allowing downscaling and near-surface soil moisture assimilation techniques to be tested with remote sensing data which is consistent with that from current (MODIS) and planned (SMOS) satellite sensors.. Additionally, two transects will be flown across the area to provide both 1km multi-angular passive microwave data for SMOS algorithm development, and on the same day, 50m resolution passive microwave data for algorithm verification. The study area contains a total of 13 soil moisture profile and rainfall monitoring sites for assimilation verification, and the transect fight lines are planned to go through 5 of these. Ground monitoring of surface soil moisture and vegetation for algorithm verification will be targeted at these 5 focus farms, with soil moisture measurements made at 250m spacing for 1km resolution flights and 50m spacing for 50m resolution flights. While this experiment has a particular emphasis on the remote sensing of soil moisture, it is open for collaboration from interested scientists from all disciplines of environmental remote sensing and its application. See www.nafe.unimelb.edu.au for more detailed information on these experiments.

High Resolution Monthly Oceanic Rainfall Based on Microwave Brightness Temperature Histograms

NASA Astrophysics Data System (ADS)

Shin, D.; Chiu, L. S.

2005-12-01

A statistical emission-based passive microwave retrieval algorithm has been developed by Wilheit, Chang and Chiu (1991) to estimate space/time oceanic rainfall. The algorithm has been applied to Special Sensor Microwave Imager (SSM/I) data taken on board the Defense Meteorological Satellite Program (DMSP) satellites to provide monthly oceanic rainfall over 2.5ox2.5o and 5ox5o latitude-longitude boxes by the Global Precipitation Climatology Project-Polar Satellite Precipitation Data Center (GPCP-PSPDC, URL: http://gpcp-pspdc.gmu.edu/) as part of NASA's contribution to the GPCP. The algorithm has been modified and applied to the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) data to produce a TRMM Level 3 standard product (3A11) over 5ox5o latitude/longitude boxes. In this study, the algorithm code is modified to retrieve rain rates at 2.5ox2.5o and 1ox1o resolutions for TMI. Two months of TMI data have been tested and the results compared with the monthly mean rain rates derived from TRMM Level 2 TMI rain profile algorithm (2A12) and the original 5ox5o data from 3A11. The rainfall pattern is very similar to the monthly average of 2A12, although the intensity is slightly higher. Details in the rain pattern, such as rain shadow due to island blocking, which were not discernible from the low resolution products, are now easily discernible. The spatial average of the higher resolution rain rates are in general slightly higher than lower resolution rain rates, although a Student-t test shows no significant difference. This high resolution product will be useful for the calibration of IR rain estimates for the production of the GPCP merge rain product.

NPP ATMS Snowfall Rate Product

NASA Technical Reports Server (NTRS)

Meng, Huan; Ferraro, Ralph; Kongoli, Cezar; Wang, Nai-Yu; Dong, Jun; Zavodsky, Bradley; Yan, Banghua

2015-01-01

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2015). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. NCEP CMORPH analysis has shown that integration of ATMS SFR has improved the performance of CMORPH-Snow. The ATMS SFR product is also being assessed at several NWS Weather Forecast Offices for its usefulness in weather forecast.

Effects of Uncertainty in TRMM Precipitation Radar Path Integrated Attenuation on Interannual Variations of Tropical Oceanic Rainfall

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Fitzjarrald, Dan E.; Kummerow, Christian D.; Arnold, James E. (Technical Monitor)

2002-01-01

Considerable uncertainty surrounds the issue of whether precipitation over the tropical oceans (30 deg N/S) systematically changes with interannual sea-surface temperature (SST) anomalies that accompany El Nino (warm) and La Nina (cold) events. Time series of rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM Precipitation Radar (PR) over the tropical oceans show marked differences with estimates from two TRMM Microwave Imager (TMI) passive microwave algorithms. We show that path-integrated attenuation derived from the effects of precipitation on the radar return from the ocean surface exhibits interannual variability that agrees closely with the TMI time series. Further analysis of the frequency distribution of PR (2A25 product) rain rates suggests that the algorithm incorporates the attenuation measurement in a very conservative fashion so as to optimize the instantaneous rain rates. Such an optimization appears to come at the expense of monitoring interannual climate variability.

A multisensor approach to sea ice classification for the validation of DMSP-SSM/I passive microwave derived sea ice products

NASA Technical Reports Server (NTRS)

Steffen, K.; Schweiger, A. J.

1990-01-01

The validation of sea ice products derived from the Special Sensor Microwave Imager (SSM/I) on board a DMSP platform is examined using data from the Landsat MSS and NOAA-AVHRR sensors. Image processing techniques for retrieving ice concentrations from each type of imagery are developed and results are intercompared to determine the ice parameter retrieval accuracy of the SSM/I NASA-Team algorithm. For case studies in the Beaufort Sea and East Greenland Sea, average retrieval errors of the SSM/I algorithm are between 1.7 percent for spring conditions and 4.3 percent during freeze up in comparison with Landsat derived ice concentrations. For a case study in the East Greenland Sea, SSM/I derived ice concentration in comparison with AVHRR imagery display a mean error of 9.6 percent.

BOREAS HYD-4 Areal Snow Course Data

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Knapp, David E. (Editor); Metcalfe, John R.; Goodison, Barry E.; Walker, Anne; Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-4 team focused on collecting data during the 1994 winter focused field campaign (FFCW) to improve the understanding of winter processes within the boreal forest. Knowledge of snow cover and its variability in the boreal forest is fundamental if BOREAS is to achieve its goals of understanding the processes and states involved in the exchange of energy and water. The development and validation of remote sensing algorithms will provide the means to extend the knowledge of these processes and states from the local to the regional scale. A specific thrust of the hydrology research is the development and validation of snow cover algorithms from airborne passive microwave measurements. Airborne remote sensing data (gamma, passive microwave) were acquired along a series of flight lines established in the vicinity of the BOREAS study areas. Ground snow surveys were conducted along selected sections of these aircraft flight lines. These calibration segments were typically 10-20 km in length, and ground data were collected at one to two kilometer intervals. The data are provided in tabular ASCII files. The HYD-04 areal snow course data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Microwave remote sensing of sea ice in the AIDJEX Main Experiment. [Arctic Ice Dynamics Joint Experiment

NASA Technical Reports Server (NTRS)

Campbell, W. J.; Wayenberg, J.; Ramseyer, J. B.; Ramseier, R. O.; Vant, M. R.; Weaver, R.; Redmond, A.; Arsenault, L.; Gloersen, P.; Zwally, H. J.

1978-01-01

A microwave remote sensing program of sea ice in the Beaufort Sea was conducted during the Arctic Ice Dynamics Joint Experiment (AIDJEX). Several types of both passive and active sensors were used to perform surface and aircraft measurements during all seasons of the year. In situ observations were made of physical properties (salinity, temperature, density, surface roughness), dielectric properties, and passive microwave measurements were made of first-year, multiyear, and first-year/multiyear mixtures. Airborne passive microwave measurements were performed with the electronically scanning microwave radiometer while airborne active microwave measurements were performed by synthetic aperture radar, X- and L-band radar, and a scatterometer.

Improving Database Simulations for Bayesian Precipitation Retrieval using Non-Spherical Ice Particles

NASA Astrophysics Data System (ADS)

Ringerud, S.; Skofronick Jackson, G.; Kulie, M.; Randel, D.

2016-12-01

NASA's Global Precipitation Measurement Mission (GPM) provides a wealth of both active and passive microwave observations aimed at furthering understanding of global precipitation and the hydrologic cycle. Employing a constellation of passive microwave radiometers increases global coverage and sampling, while the core satellite acts as a transfer standard, enabling consistent retrievals across individual constellation members. The transfer standard is applied in the form of a physically based a priori database constructed for use in Bayesian retrieval algorithms for each radiometer. The database is constructed using hydrometeor profiles optimized for the best fit to simultaneous active/passive core satellite measurements via the GPM Combined Algorithm. Initial validation of GPM rainfall products using the combined database suggests high retrieval errors for convective precipitation over land and at high latitudes. In such regimes, the signal from ice scattering observed at the higher microwave frequencies becomes particularly important for detecting and retrieving precipitation. For cross-track sounders such as MHS and SAPHIR, this signal is crucial. It is therefore important that the scattering signals associated with precipitation are accurately represented and modeled in the retrieval database. In the current GPM combined retrieval and constellation databases, ice hydrometeors are represented as "fluffy spheres", with assumed density and scattering parameters calculated using Mie theory. Resulting simulated Tb agree reasonably well at frequencies up to 89 GHz, but show significant biases at higher frequencies. In this work the database is recreated using an ensemble of non-spherical ice particles with single scattering properties calculated using discrete dipole approximation. Simulated Tb agreement is significantly improved across the high frequencies, decreasing biases by an order of magnitude in several of the channels. The new database is applied for a sample of GPM constellation retrievals and the retrieved precipitation rates compared, to demonstrate areas where the use of more complex ice particles will have the greatest effect upon the final retrievals.

Some comments on passive microwave measurement of rain

NASA Technical Reports Server (NTRS)

Wilheit, Thomas T.

1986-01-01

It is argued that because microwave radiation interacts much more strongly with hydrometeors than with cloud particles, microwave measurements from space offer a significant chance of making global precipitation estimates. Over oceans, passive microwave measurements are essentially attenuation measurements that can be very closely related to the rain rate independently of the details of the drop-size distribution. Over land, scattering of microwave radiation by the hydrometeors, especially in the ice phase, can be used to estimate rainfall. In scattering, the details of the drop-size distribution are very important and it is therefore more difficult to achieve a high degree of accuracy. The SSM/I (Special Sensor Microwave Imager), a passive microwave imaging sensor that will be launched soon, will have dual-polarized channels at 85.5 GHz that will be very sensitive to scattering by frozen hydrometeors. Other sensors being considered for the future space missions would extend the ability to estimate rain rates from space. The ideal spaceborne precipitation-measurement system would use the complementary strengths of passive microwave, radar, and visible/infrared measurements.

Ultra-Wideband Radar Measurements of Thickness of Snow Over Sea Ice

NASA Technical Reports Server (NTRS)

Kanagaratnam, P.; Markus, T.; Lytle, V.; Heavey, B.; Jansen, P.; Prescott, G.; Gogineni, S.

2007-01-01

An accurate knowledge of snow thickness and its variability over sea ice is crucial for determining the overall polar heat and freshwater budget, which influences the global climate. Recently, algorithms have been developed to extract snow thicknesses from passive microwave satellite data. However, validation of these data over the large footprint of the passive microwave sensor has been a challenge. The only method used thus far has been with meter sticks during ship cruises. To address this problem, we developed an ultra wideband frequency-modulated continuous-wave (FM-CW) radar to measure snow thickness over sea ice. We made snow-thickness measurements over Antarctic sea ice by operating the radar from a sled during September and October, 2003. We performed radar measurements over 11 stations with varying snow thickness between 4 and 85 cm. We observed excellent agreement between radar estimates of snow thickness with physical measurements, achieving a correlation coefficient of 0.95 and a vertical resolution of about 3 cm.

Sensitivity of Support Vector Machine Predictions of Passive Microwave Brightness Temperature Over Snow-covered Terrain in High Mountain Asia

NASA Astrophysics Data System (ADS)

Ahmad, J. A.; Forman, B. A.

2017-12-01

High Mountain Asia (HMA) serves as a water supply source for over 1.3 billion people, primarily in south-east Asia. Most of this water originates as snow (or ice) that melts during the summer months and contributes to the run-off downstream. In spite of its critical role, there is still considerable uncertainty regarding the total amount of snow in HMA and its spatial and temporal variation. In this study, the NASA Land Information Systems (LIS) is used to model the hydrologic cycle over the Indus basin. In addition, the ability of support vector machines (SVM), a machine learning technique, to predict passive microwave brightness temperatures at a specific frequency and polarization as a function of LIS-derived land surface model output is explored in a sensitivity analysis. Multi-frequency, multi-polarization passive microwave brightness temperatures as measured by the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) over the Indus basin are used as training targets during the SVM training process. Normalized sensitivity coefficients (NSC) are then computed to assess the sensitivity of a well-trained SVM to each LIS-derived state variable. Preliminary results conform with the known first-order physics. For example, input states directly linked to physical temperature like snow temperature, air temperature, and vegetation temperature have positive NSC's whereas input states that increase volume scattering such as snow water equivalent or snow density yield negative NSC's. Air temperature exhibits the largest sensitivity coefficients due to its inherent, high-frequency variability. Adherence of this machine learning algorithm to the first-order physics bodes well for its potential use in LIS as the observation operator within a radiance data assimilation system aimed at improving regional- and continental-scale snow estimates.

Arctic multiyear ice classification and summer ice cover using passive microwave satellite data

NASA Astrophysics Data System (ADS)

Comiso, J. C.

1990-08-01

The ability to classify and monitor Arctic multiyear sea ice cover using multispectral passive microwave data is studied. Sea ice concentration maps during several summer minima have been analyzed to obtain estimates of ice surviving the summer. The results are compared with multiyear ice concentrations derived from data the following winter, using an algorithm that assumes a certain emissivity for multiyear ice. The multiyear ice cover inferred from the winter data is approximately 25 to 40% less than the summer ice cover minimum, suggesting that even during winter when the emissivity of sea ice is most stable, passive microwave data may account for only a fraction of the total multiyear ice cover. The difference of about 2×106 km2 is considerably more than estimates of advection through Fram Strait during the intervening period. It appears that as in the Antarctic, some multiyear ice floes in the Arctic, especially those near the summer marginal ice zone, have first-year ice or intermediate signatures in the subsequent winter. A likely mechanism for this is the intrusion of seawater into the snow-ice interface, which often occurs near the marginal ice zone or in areas where snow load is heavy. Spatial variations in melt and melt ponding effects also contribute to the complexity of the microwave emissivity of multiyear ice. Hence the multiyear ice data should be studied in conjunction with the previous summer ice data to obtain a more complete characterization of the state of the Arctic ice cover. The total extent and actual areas of the summertime Arctic pack ice were estimated to be 8.4×106 km2 and 6.2×106 km2, respectively, and exhibit small interannual variability during the years 1979 through 1985, suggesting a relatively stable ice cover.

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.

Stratiform/convective rain delineation for TRMM microwave imager

NASA Astrophysics Data System (ADS)

Islam, Tanvir; Srivastava, Prashant K.; Dai, Qiang; Gupta, Manika; Wan Jaafar, Wan Zurina

2015-10-01

This article investigates the potential for using machine learning algorithms to delineate stratiform/convective (S/C) rain regimes for passive microwave imager taking calibrated brightness temperatures as only spectral parameters. The algorithms have been implemented for the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI), and calibrated as well as validated taking the Precipitation Radar (PR) S/C information as the target class variables. Two different algorithms are particularly explored for the delineation. The first one is metaheuristic adaptive boosting algorithm that includes the real, gentle, and modest versions of the AdaBoost. The second one is the classical linear discriminant analysis that includes the Fisher's and penalized versions of the linear discriminant analysis. Furthermore, prior to the development of the delineation algorithms, a feature selection analysis has been conducted for a total of 85 features, which contains the combinations of brightness temperatures from 10 GHz to 85 GHz and some derived indexes, such as scattering index, polarization corrected temperature, and polarization difference with the help of mutual information aided minimal redundancy maximal relevance criterion (mRMR). It has been found that the polarization corrected temperature at 85 GHz and the features derived from the "addition" operator associated with the 85 GHz channels have good statistical dependency to the S/C target class variables. Further, it has been shown how the mRMR feature selection technique helps to reduce the number of features without deteriorating the results when applying through the machine learning algorithms. The proposed scheme is able to delineate the S/C rain regimes with reasonable accuracy. Based on the statistical validation experience from the validation period, the Matthews correlation coefficients are in the range of 0.60-0.70. Since, the proposed method does not rely on any a priori information, this makes it very suitable for other microwave sensors having similar channels to the TMI. The method could possibly benefit the constellation sensors in the Global Precipitation Measurement (GPM) mission era.

Mapping and assessing variability in the Antarctic marginal ice zone, pack ice and coastal polynyas in two sea ice algorithms with implications on breeding success of snow petrels

NASA Astrophysics Data System (ADS)

Stroeve, Julienne C.; Jenouvrier, Stephanie; Campbell, G. Garrett; Barbraud, Christophe; Delord, Karine

2016-08-01

Sea ice variability within the marginal ice zone (MIZ) and polynyas plays an important role for phytoplankton productivity and krill abundance. Therefore, mapping their spatial extent as well as seasonal and interannual variability is essential for understanding how current and future changes in these biologically active regions may impact the Antarctic marine ecosystem. Knowledge of the distribution of MIZ, consolidated pack ice and coastal polynyas in the total Antarctic sea ice cover may also help to shed light on the factors contributing towards recent expansion of the Antarctic ice cover in some regions and contraction in others. The long-term passive microwave satellite data record provides the longest and most consistent record for assessing the proportion of the sea ice cover that is covered by each of these ice categories. However, estimates of the amount of MIZ, consolidated pack ice and polynyas depend strongly on which sea ice algorithm is used. This study uses two popular passive microwave sea ice algorithms, the NASA Team and Bootstrap, and applies the same thresholds to the sea ice concentrations to evaluate the distribution and variability in the MIZ, the consolidated pack ice and coastal polynyas. Results reveal that the seasonal cycle in the MIZ and pack ice is generally similar between both algorithms, yet the NASA Team algorithm has on average twice the MIZ and half the consolidated pack ice area as the Bootstrap algorithm. Trends also differ, with the Bootstrap algorithm suggesting statistically significant trends towards increased pack ice area and no statistically significant trends in the MIZ. The NASA Team algorithm on the other hand indicates statistically significant positive trends in the MIZ during spring. Potential coastal polynya area and amount of broken ice within the consolidated ice pack are also larger in the NASA Team algorithm. The timing of maximum polynya area may differ by as much as 5 months between algorithms. These differences lead to different relationships between sea ice characteristics and biological processes, as illustrated here with the breeding success of an Antarctic seabird.

An inter-sensor comparison of the microwave signatures of Arctic sea ice

NASA Technical Reports Server (NTRS)

Onstott, R. G.

1986-01-01

Active and passive microwave and physical properties of Arctic sea ice in the marginal ice zone were measured during the summer. Results of an intercomparison of data acquired by an aircraft synthetic aperture radar, a passive microwave imager and a helicopter-mounted scatterometer indicate that early-to-mid summer sea ice microwave signatures are dominated by snowpack characteristics. Measurements show that the greatest contrast between thin first-year and multiyear sea ice occurs when operating actively between 5 and 10 GHz. Significant information about the state of melt of snow and ice is contained in the active and passive microwave signatures.

A Blended Global Snow Product using Visible, Passive Microwave and Scatterometer Satellite Data

NASA Technical Reports Server (NTRS)

Foster, James L.; Hall, Dorothy K.; Eylander, John B.; Riggs, George A.; Nghiem, Son V.; Tedesco, Marco; Kim, Edward; Montesano, Paul M.; Kelly, Richard E. J.; Casey, Kimberly A.;

Soil Moisture Active Passive (SMAP) L-Band Microwave Radiometer Post-Launch Calibration

NASA Technical Reports Server (NTRS)

Peng, Jinzheng; Piepmeier, Jeffrey R.; Misra, Sidharth; Dinnat, Emmanuel P.; Hudson, Derek; Le Vine, David M.; De Amici, Giovanni; Mohammed, Priscilla N.; Yueh, Simon H.; Meissner, Thomas

2016-01-01

The SMAP microwave radiometer is a fully-polarimetric L-band radiometer flown on the SMAP satellite in a 6 AM/ 6 PM sun-synchronous orbit at 685 km altitude. Since April, 2015, the radiometer is under calibration and validation to assess the quality of the radiometer L1B data product. Calibration methods including the SMAP L1B TA2TB (from Antenna Temperature (TA) to the Earth's surface Brightness Temperature (TB)) algorithm and TA forward models are outlined, and validation approaches to calibration stability/quality are described in this paper including future work. Results show that the current radiometer L1B data satisfies its requirements.

Soil Moisture ActivePassive (SMAP) L-Band Microwave Radiometer Post-Launch Calibration

NASA Technical Reports Server (NTRS)

Peng, Jinzheng; Piepmeier, Jeffrey R.; Misra, Sidharth; Dinnat, Emmanuel P.; Hudson, Derek; Le Vine, David M.; De Amici, Giovanni; Mohammed, Priscilla N.; Yueh, Simon H.; Meissner, Thomas

2016-01-01

The SMAP microwave radiometer is a fully-polarimetric L-band radiometer flown on the SMAP satellite in a 6 AM/ 6 PM sun-synchronous orbit at 685 km altitude. Since April, 2015, the radiometer is under calibration and validation to assess the quality of the radiometer L1B data product. Calibration methods including the SMAP L1B TA2TB (from Antenna Temperature (TA) to the Earth’s surface Brightness Temperature (TB)) algorithm and TA forward models are outlined, and validation approaches to calibration stability/quality are described in this paper including future work. Results show that the current radiometer L1B data satisfies its requirements.

Monolithic microwave integrated circuit devices for active array antennas

NASA Technical Reports Server (NTRS)

Mittra, R.

1984-01-01

Two different aspects of active antenna array design were investigated. The transition between monolithic microwave integrated circuits and rectangular waveguides was studied along with crosstalk in multiconductor transmission lines. The boundary value problem associated with a discontinuity in a microstrip line is formulated. This entailed, as a first step, the derivation of the propagating as well as evanescent modes of a microstrip line. The solution is derived to a simple discontinuity problem: change in width of the center strip. As for the multiconductor transmission line problem. A computer algorithm was developed for computing the crosstalk noise from the signal to the sense lines. The computation is based on the assumption that these lines are terminated in passive loads.

Global Soil Moisture from the Aquarius/SAC-D Satellite: Description and Initial Assessment

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas; Cosh, Michael; Zhao, Tianjie; O'Neil, Peggy

2015-01-01

Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology and with soil moisture from other satellite missions (Advanced Microwave Scanning Radiometer for the Earth Observing System and Soil Moisture Ocean Salinity). Initial assessments were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011-June 2013 (rmse = 0.031 m(exp 3)/m(exp 3), Bias = -0.007 m(exp 3)/m(exp 3), and R = 0.855). This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive-active soil moisture algorithms. The products are routinely available through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.

Modelling the Passive Microwave Signature from Land Surfaces: A Review of Recent Results and Application to the L-Band SMOS SMAP Soil Moisture Retrieval Algorithms

NASA Technical Reports Server (NTRS)

Wigneron, J.-P.; Jackson, T. J.; O'Neill, P.; De Lannoy, G.; De Rosnay, P.; Walker, J. P.; Ferrazzoli, P.; Mironov, V.; Bircher, S.; Grant, J. P.;

Correlation studies of passive and active microwave data in the marginal ice zone

NASA Technical Reports Server (NTRS)

Comiso, J. C.

1991-01-01

The microwave radiative and backscatter characteristics of sea ice in an Arctic marginal ice zone have been studied using near-simultaneous passive and active synthetic aperture radar microwave data. Intermediate-resolution multichannel passive microwave data were registered and analyzed. Passive and active microwave data generally complement each other as the two sensors are especially sensitive to different physical properties of the sea ice. In the inner pack, undeformed first-year ice is observed to have low backscatter values but high brightness temperatures while multiyear ice has generally high backscatter values and low brightness temperatures. However, in the marginal ice zone, the signature and backscatter for multiyear ice are considerably different and closer to those of first-year ice. Some floes identified by photography as snow-covered thick ice have backscatter similar to that of new ice or open water while brash ice has backscatter similar to or higher than that of ridged ice.

Early Results from the Global Precipitation Measurement (GPM) Mission in Japan

NASA Astrophysics Data System (ADS)

Kachi, Misako; Kubota, Takuji; Masaki, Takeshi; Kaneko, Yuki; Kanemaru, Kaya; Oki, Riko; Iguchi, Toshio; Nakamura, Kenji; Takayabu, Yukari N.

2015-04-01

The Global Precipitation Measurement (GPM) mission is an international collaboration to achieve highly accurate and highly frequent global precipitation observations. The GPM mission consists of the GPM Core Observatory jointly developed by U.S. and Japan and Constellation Satellites that carry microwave radiometers and provided by the GPM partner agencies. The Dual-frequency Precipitation Radar (DPR) was developed by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT), and installed on the GPM Core Observatory. The GPM Core Observatory chooses a non-sun-synchronous orbit to carry on diurnal cycle observations of rainfall from the Tropical Rainfall Measuring Mission (TRMM) satellite and was successfully launched at 3:37 a.m. on February 28, 2014 (JST), while the Constellation Satellites, including JAXA's Global Change Observation Mission (GCOM) - Water (GCOM-W1) or "SHIZUKU," are launched by each partner agency sometime around 2014 and contribute to expand observation coverage and increase observation frequency JAXA develops the DPR Level 1 algorithm, and the NASA-JAXA Joint Algorithm Team develops the DPR Level 2 and DPR-GMI combined Level2 algorithms. JAXA also develops the Global Rainfall Map (GPM-GSMaP) algorithm, which is a latest version of the Global Satellite Mapping of Precipitation (GSMaP), as national product to distribute hourly and 0.1-degree horizontal resolution rainfall map. Major improvements in the GPM-GSMaP algorithm is; 1) improvements in microwave imager algorithm based on AMSR2 precipitation standard algorithm, including new land algorithm, new coast detection scheme; 2) Development of orographic rainfall correction method for warm rainfall in coastal area (Taniguchi et al., 2012); 3) Update of database, including rainfall detection over land and land surface emission database; 4) Development of microwave sounder algorithm over land (Kida et al., 2012); and 5) Development of gauge-calibrated GSMaP algorithm (Ushio et al., 2013). In addition to those improvements in the algorithms number of passive microwave imagers and/or sounders used in the GPM-GSMaP was increased compared to the previous version. After the early calibration and validation of the products and evaluation that all products achieved the release criteria, all GPM standard products and the GPM-GSMaP product has been released to the public since September 2014. The GPM products can be downloaded via the internet through the JAXA G-Portal (https://www.gportal.jaxa.jp).

Precipitable Water Variability Using SSM/I and GOES VAS Pathfinder Data Sets

NASA Technical Reports Server (NTRS)

Lerner, Jeffrey A.; Jedlovec, Gary J.; Kidder, Stanley Q.

1996-01-01

Determining moisture variability for all weather scenes is critical to understanding the earth's hydrologic cycle and global climate changes. Remote sensing from geostationary satellites provides the necessary temporal and spatial resolutions necessary for global change studies. Due to antenna size constraints imposed with the use of microwave radiometers, geostationary satellites have carried instruments passively measuring radiation at infrared wavelengths or shorter. The shortfall of using infrared instruments in moisture studies lies in its inability to sense terrestrial radiation through clouds. Microwave emissions, on the other hand, are mostly unaffected by cloudy atmospheres. Land surface emissivity at microwave frequencies exhibit both high temporal and spatial variability thus confining moisture retrievals at microwave frequencies to over marine atmospheres (a near uniform cold background). This study intercompares the total column integrated water content Precipitable Water, (PW) as derived from both the Special Sensor Microwave Imager (SSM/I) and the Geostationary Operational Environmental Satellite (GOES) VISSR Atmospheric Sounder (VAS) pathfinder data sets. PW is a bulk parameter often used to quantify moisture variability and is important to understanding the earth's hydrologic cycle and climate system. This research has been spawned in an effort to combine two different algorithms which together can lead to a more comprehensive quantification of global water vapor. The approach taken here is to intercompare two independent PW retrieval algorithms and to validate the resultant retrievals against an existing data set, namely the European Center for Medium range Weather Forecasts (ECMWF) model analysis data.

Inter-comparison of the EUMETSAT H-SAF and NASA PPS precipitation products over Western Europe.

NASA Astrophysics Data System (ADS)

Kidd, Chris; Panegrossi, Giulia; Ringerud, Sarah; Stocker, Erich

2017-04-01

The development of precipitation retrieval techniques utilising passive microwave satellite observations has achieved a good degree of maturity through the use of physically-based schemes. The DMSP Special Sensor Microwave Imager/Sounder (SSMIS) has been the mainstay of passive microwave observations over the last 13 years forming the basis of many satellite precipitation products, including NASA's Precipitation Processing System (PPS) and EUMETSAT's Hydrological Satellite Application Facility (H-SAF). The NASA PPS product utilises the Goddard Profiling (GPROF; currently 2014v2-0) retrieval scheme that provides a physically consistent retrieval scheme through the use of coincident active/passive microwave retrievals from the Global Precipitation Measurement (GPM) mission core satellite. The GPM combined algorithm retrieves hydrometeor profiles optimized for consistency with both Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI); these profiles form the basis of the GPROF database which can be utilized for any constellation radiometer within the framework a Bayesian retrieval scheme. The H-SAF product (PR-OBS-1 v1.7) is based on a physically-based Bayesian technique where the a priori information is provided by a Cloud Dynamic Radiation Database (CDRD). Meteorological parameter constraints, derived from synthetic dynamical-thermodynamical-hydrological meteorological profile variables, are used in conjunction with multi-hydrometeor microphysical profiles and multispectral PMW brightness temperature vectors into a specialized a priori knowledge database underpinning and guiding the algorithm's Bayesian retrieval solver. This paper will present the results of an inter-comparison of the NASA PPS GPROF and EUMETSAT H-SAF PR-OBS-1 products over Western Europe for the period from 1 January 2015 through 31 December 2016. Surface radar is derived from the UKMO-derived Nimrod European radar product, available at 15 minute/5 km resolution. Initial results show that overall the correlations between the two satellite precipitation products and surface radar precipitation estimates are similar, particularly for cases where there is extensive precipitation; however, the H-SAF tends to have poorer correlations in situations where rain is light or limited in extent. Similarly, RMSEs for the GPROF scheme tend to a smaller than those of the H-SAF retrievals. The difference in the performance can be traced to the identification of precipitation; the GPROF2014v2-0 scheme overestimates the occurrence and extent of the precipitation, generating a significant amount of light precipitation. The H-SAF scheme has a lower precipitation threshold of about 0.25 mmh-1 while overestimating moderate and higher precipitation intensities.

Source analysis of spaceborne microwave radiometer interference over land

NASA Astrophysics Data System (ADS)

Guan, Li; Zhang, Sibo

2016-03-01

Satellite microwave thermal emissions mixed with signals from active sensors are referred to as radiofrequency interference (RFI). Based on Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) observations from June 1 to 16, 2011, RFI over Europe was identified and analyzed using the modified principal component analysis algorithm in this paper. The X band AMSR-E measurements in England and Italy are mostly affected by the stable, persistent, active microwave transmitters on the surface, while the RFI source of other European countries is the interference of the reflected geostationary TV satellite downlink signals to the measurements of spaceborne microwave radiometers. The locations and intensities of the RFI induced by the geostationary TV and communication satellites changed with time within the observed period. The observations of spaceborne microwave radiometers in ascending portions of orbits are usually interfered with over European land, while no RFI was detected in descending passes. The RFI locations and intensities from the reflection of downlink radiation are highly dependent upon the relative geometry between the geostationary satellite and the measuring passive sensor. Only these fields of view of a spaceborne instrument whose scan azimuths are close to the azimuth relative to the geostationary satellite are likely to be affected by RFI.

Microwave remote sensing of soil moisture, volume 1. [Guymon, Oklahoma and Dalhart, Texas

NASA Technical Reports Server (NTRS)

Mcfarland, M. J. (Principal Investigator); Theis, S. W.; Rosenthal, W. D.; Jones, C. L.

1982-01-01

Multifrequency sensor data from NASA's C-130 aircraft were used to determine which of the all weather microwave sensors demonstrated the highest correlation to surface soil moisture over optimal bare soil conditions, and to develop and test techniques which use visible/infrared sensors to compensate for the vegetation effect in this sensor's response to soil moisture. The L-band passive microwave radiometer was found to be the most suitable single sensor system to estimate soil moisture over bare fields. The perpendicular vegetation index (PVI) as determined from the visible/infrared sensors was useful as a measure of the vegetation effect on the L-band radiometer response to soil moisture. A linear equation was developed to estimate percent field capacity as a function of L-band emissivity and the vegetation index. The prediction algorithm improves the estimation of moisture significantly over predictions from L-band emissivity alone.

Global Precipitation Measurement

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Skofronick-Jackson, Gail; Kummerow, Christian D.; Shepherd, James Marshall

2008-01-01

This chapter begins with a brief history and background of microwave precipitation sensors, with a discussion of the sensitivity of both passive and active instruments, to trace the evolution of satellite-based rainfall techniques from an era of inference to an era of physical measurement. Next, the highly successful Tropical Rainfall Measuring Mission will be described, followed by the goals and plans for the Global Precipitation Measurement (GPM) Mission and the status of precipitation retrieval algorithm development. The chapter concludes with a summary of the need for space-based precipitation measurement, current technological capabilities, near-term algorithm advancements and anticipated new sciences and societal benefits in the GPM era.

Remote Sensing of Cloud Properties using Ground-based Measurements of Zenith Radiance

NASA Technical Reports Server (NTRS)

Chiu, J. Christine; Marshak, Alexander; Knyazikhin, Yuri; Wiscombe, Warren J.; Barker, Howard W.; Barnard, James C.; Luo, Yi

2006-01-01

An extensive verification of cloud property retrievals has been conducted for two algorithms using zenith radiances measured by the Atmospheric Radiation Measurement (ARM) Program ground-based passive two-channel (673 and 870 nm) Narrow Field-Of-View Radiometer. The underlying principle of these algorithms is that clouds have nearly identical optical properties at these wavelengths, but corresponding spectral surface reflectances (for vegetated surfaces) differ significantly. The first algorithm, the RED vs. NIR, works for a fully three-dimensional cloud situation. It retrieves not only cloud optical depth, but also an effective radiative cloud fraction. Importantly, due to one-second time resolution of radiance measurements, we are able, for the first time, to capture detailed changes in cloud structure at the natural time scale of cloud evolution. The cloud optical depths tau retrieved by this algorithm are comparable to those inferred from both downward fluxes in overcast situations and microwave brightness temperatures for broken clouds. Moreover, it can retrieve tau for thin patchy clouds, where flux and microwave observations fail to detect them. The second algorithm, referred to as COUPLED, couples zenith radiances with simultaneous fluxes to infer 2. In general, the COUPLED and RED vs. NIR algorithms retrieve consistent values of tau. However, the COUPLED algorithm is more sensitive to the accuracies of measured radiance, flux, and surface reflectance than the RED vs. NIR algorithm. This is especially true for thick overcast clouds where it may substantially overestimate z.

Snow cover of the Upper Colorado River Basin from satellite passive microwave and visual imagery

USGS Publications Warehouse

Josberger, E.G.; Beauvillain, E.

1989-01-01

A comparison of passive microwave images from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and visual images from the Defense Meteorological Satellite Program (DMSP) of the Upper Colorado River Basin shows that passive microwave satellite imagery can be used to determine the extent of the snow cover. Eight cloud-free DMSP images throughout the winter of 1985-1986 show the extent of the snowpack, which, when compared to the corresponding SMMR images, determine the threshold microwave characteristics for snow-covered pixels. With these characteristics, the 27 sequential SMMR images give a unique view of the temporal history of the snow cover extent through the first half of the water year. -from Authors

Potential of bias correction for downscaling passive microwave and soil moisture data

USDA-ARS?s Scientific Manuscript database

Passive microwave satellites such as SMOS (Soil Moisture and Ocean Salinity) or SMAP (Soil Moisture Active Passive) observe brightness temperature (TB) and retrieve soil moisture at a spatial resolution greater than most hydrological processes. Bias correction is proposed as a simple method to disag...

Variations in West Antarctic Ice Front and Passive Microwave Brightness Temperature for 8 Years Duration in 2000s

NASA Astrophysics Data System (ADS)

Kim, J.; Yu, J.; Wang, L.; Liu, H.

2017-12-01

Changes in Antarctic ice sheet are caused by various reasons such as changes in Holocene climate, precipitation, and ocean temperature. Such issues of changes in ice sheet has been mainly focused on the Antarctic peninsula, and it is known that ice retreat of the area is caused by changes in atmospheric and ocean temperatures. For the case of West Antarctica, ice front change research is relatively rarely conducted except the Pine island glacier area. This study has monitored ice front changes of West Antarctica and compared the patterns with the changes in brightness temperature based on remote sensing techniques. We used 2000 Radarsat-1 and 2008 Rasarsat-2 SAR data to delineate coastlines of whole West Antarctica based on the locally thresholding adaptive algorithm. The delineated coast lines are analyzed to figure out ice front change patterns between the duration. The variations in brightness temperature for the same duration are calculated based on Defense Meteorological Satellite Program (DMSP)'s Special Sensor Microwave/Images-Special Sensor Microwave Imager/Sounder (SSM/I-SSMIS) passive microwave data. The results show ice front of West Antarctica shows advancing trend except the pine island glacier area. The brightness temperature had decreasing trend during the study period. It infers that changes in ice front and brightness temperature of West Antarctica have considerable relationships. It is expected that a long term monitoring of the relationship would contribute understanding ice dynamics of West Antarctica significantly.

Estimating the time of melt onset and freeze onset over Arctic sea-ice area using active and passive microwave data

USGS Publications Warehouse

Belchansky, Gennady I.; Douglas, David C.; Mordvintsev, Ilia N.; Platonov, Nikita G.

2004-01-01

Accurate calculation of the time of melt onset, freeze onset, and melt duration over Arctic sea-ice area is crucial for climate and global change studies because it affects accuracy of surface energy balance estimates. This comparative study evaluates several methods used to estimate sea-ice melt and freeze onset dates: (1) the melt onset database derived from SSM/I passive microwave brightness temperatures (Tbs) using Drobot and Anderson's [J. Geophys. Res. 106 (2001) 24033] Advanced Horizontal Range Algorithm (AHRA) and distributed by the National Snow and Ice Data Center (NSIDC); (2) the International Arctic Buoy Program/Polar Exchange at the Sea (IABP/POLES) surface air temperatures (SATs); (3) an elaborated version of the AHRA that uses IABP/POLES to avoid anomalous results (Passive Microwave and Surface Temperature Analysis [PMSTA]); (4) another elaborated version of the AHRA that uses Tb variance to avoid anomalous results (Mean Differences and Standard Deviation Analysis [MDSDA]); (5) Smith's [J. Geophys. Res. 103 (1998) 27753] vertically polarized Tb algorithm for estimating melt onset in multiyear (MY) ice (SSM/I 19V–37V); and (6) analyses of concurrent backscattering cross section (σ°) and brightness temperature (Tb) from OKEAN-01 satellite series. Melt onset and freeze onset maps were created and compared to understand how the estimates vary between different satellite instruments and methods over different Arctic sea-ice regions. Comparisons were made to evaluate relative sensitivities among the methods to slight adjustments of the Tbcalibration coefficients and algorithm threshold values. Compared to the PMSTA method, the AHRA method tended to estimate significantly earlier melt dates, likely caused by the AHRA's susceptibility to prematurely identify melt onset conditions. In contrast, the IABP/POLES surface air temperature data tended to estimate later melt and earlier freeze in all but perennial ice. The MDSDA method was least sensitive to small adjustments of the SMMR–SSM/I inter-satellite calibration coefficients. Differences among methods varied by latitude. Freeze onset dates among methods were most disparate in southern latitudes, and tended to converge northward. Surface air temperatures (IABP/POLES) indicated freeze onset well before the MDSDA method, especially in southern peripheral seas, while PMSTA freeze estimates were generally intermediate. Surface air temperature data estimated latest melt onset dates in southern latitudes, but earliest melt onset in northern latitudes. The PMSTA estimated earliest melt onset dates in southern regions, and converged with the MDSDA northward. Because sea-ice melt and freeze are dynamical transitional processes, differences among these methods are associated with differing sensitivities to changing stages of environmental and physical development. These studies contribute to the growing body of documentation about the levels of disparity obtained when Arctic seasonal transition parameters are estimated using various types of microwave data and algorithms.

Composite Configuration Interventional Therapy Robot for the Microwave Ablation of Liver Tumors

NASA Astrophysics Data System (ADS)

Cao, Ying-Yu; Xue, Long; Qi, Bo-Jin; Jiang, Li-Pei; Deng, Shuang-Cheng; Liang, Ping; Liu, Jia

2017-11-01

The existing interventional therapy robots for the microwave ablation of liver tumors have a poor clinical applicability with a large volume, low positioning speed and complex automatic navigation control. To solve above problems, a composite configuration interventional therapy robot with passive and active joints is developed. The design of composite configuration reduces the size of the robot under the premise of a wide range of movement, and the robot with composite configuration can realizes rapid positioning with operation safety. The cumulative error of positioning is eliminated and the control complexity is reduced by decoupling active parts. The navigation algorithms for the robot are proposed based on solution of the inverse kinematics and geometric analysis. A simulation clinical test method is designed for the robot, and the functions of the robot and the navigation algorithms are verified by the test method. The mean error of navigation is 1.488 mm and the maximum error is 2.056 mm, and the positioning time for the ablation needle is in 10 s. The experimental results show that the designed robot can meet the clinical requirements for the microwave ablation of liver tumors. The composite configuration is proposed in development of the interventional therapy robot for the microwave ablation of liver tumors, which provides a new idea for the structural design of medical robots.

Siberia snow depth climatology derived from SSM/I data using a combined dynamic and static algorithm

USGS Publications Warehouse

Grippa, M.; Mognard, N.; Le, Toan T.; Josberger, E.G.

2004-01-01

One of the major challenges in determining snow depth (SD) from passive microwave measurements is to take into account the spatiotemporal variations of the snow grain size. Static algorithms based on a constant snow grain size cannot provide accurate estimates of snow pack thickness, particularly over large regions where the snow pack is subjected to big spatial temperature variations. A recent dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from the Special Sensor Microwave/Imager (SSM/I) over the Northern Great Plains (NGP) in the US. In this paper, we develop a combined dynamic and static algorithm to estimate snow depth from 13 years of SSM/I observations over Central Siberia. This region is characterised by extremely cold surface air temperatures and by the presence of permafrost that significantly affects the ground temperature. The dynamic algorithm is implemented to take into account these effects and it yields accurate snow depths early in the winter, when thin snowpacks combine with cold air temperatures to generate rapid crystal growth. However, it is not applicable later in the winter when the grain size growth slows. Combining the dynamic algorithm to a static algorithm, with a temporally constant but spatially varying coefficient, we obtain reasonable snow depth estimates throughout the entire snow season. Validation is carried out by comparing the satellite snow depth monthly averages to monthly climatological data. We show that the location of the snow depth maxima and minima is improved when applying the combined algorithm, since its dynamic portion explicitly incorporate the thermal gradient through the snowpack. The results obtained are presented and evaluated for five different vegetation zones of Central Siberia. Comparison with in situ measurements is also shown and discussed. ?? 2004 Elsevier Inc. All rights reserved.

Assimilation of Passive and Active Microwave Soil Moisture Retrievals

NASA Technical Reports Server (NTRS)

Draper, C. S.; Reichle, R. H.; DeLannoy, G. J. M.; Liu, Q.

2012-01-01

Root-zone soil moisture is an important control over the partition of land surface energy and moisture, and the assimilation of remotely sensed near-surface soil moisture has been shown to improve model profile soil moisture [1]. To date, efforts to assimilate remotely sensed near-surface soil moisture at large scales have focused on soil moisture derived from the passive microwave Advanced Microwave Scanning Radiometer (AMSR-E) and the active Advanced Scatterometer (ASCAT; together with its predecessor on the European Remote Sensing satellites (ERS. The assimilation of passive and active microwave soil moisture observations has not yet been directly compared, and so this study compares the impact of assimilating ASCAT and AMSR-E soil moisture data, both separately and together. Since the soil moisture retrieval skill from active and passive microwave data is thought to differ according to surface characteristics [2], the impact of each assimilation on the model soil moisture skill is assessed according to land cover type, by comparison to in situ soil moisture observations.

Sea Surface Salinity and Wind Retrieval Algorithm Using Combined Passive-Active L-Band Microwave Data

NASA Technical Reports Server (NTRS)

Yueh, Simon H.; Chaubell, Mario J.

2011-01-01

Aquarius is a combined passive/active L-band microwave instrument developed to map the salinity field at the surface of the ocean from space. The data will support studies of the coupling between ocean circulation, the global water cycle, and climate. The primary science objective of this mission is to monitor the seasonal and interannual variation of the large scale features of the surface salinity field in the open ocean with a spatial resolution of 150 kilometers and a retrieval accuracy of 0.2 practical salinity units globally on a monthly basis. The measurement principle is based on the response of the L-band (1.413 gigahertz) sea surface brightness temperatures (T (sub B)) to sea surface salinity. To achieve the required 0.2 practical salinity units accuracy, the impact of sea surface roughness (e.g. wind-generated ripples and waves) along with several factors on the observed brightness temperature has to be corrected to better than a few tenths of a degree Kelvin. To the end, Aquarius includes a scatterometer to help correct for this surface roughness effect.

Soil Moisture Retrieval Through Changing Corn Using Active/Passive Microwave Remote Sensing

NASA Technical Reports Server (NTRS)

ONeill, P. E.; Joseph, A.; DeLannoy, G.; Lang, R.; Utku, C.; Kim, E.; Houser, P.; Gish, T.

2003-01-01

An extensive field experiment was conducted from May-early October, 2002 at the heavily instrumented USDA-ARS (U.S. Dept. of Agriculture-Agricultural Research Service) OPE3 (Optimizing Production Inputs for Economic and Environmental Enhancement) test site in Beltsville, MD to acquire data needed to address active/passive microwave algorithm, modeling, and ground validation issues for accurate soil moisture retrieval. During the experiment, a tower-mounted 1.4 GHz radiometer (Lrad) and a truck-mounted dual-frequency (1.6 and 4.75 GHz) radar system were deployed on the northern edge of the site. The soil in this portion of the field is a sandy loam (silt 23.5%, sand 60.3%, clay 16.1%) with a measured bulk density of 1.253 g/cu cm. Vegetation cover in the experiment consisted of a corn crop which was measured from just after planting on April 17, 2002 through senescence and harvesting on October 2. Although drought conditions prevailed during the summer, the corn yield was near average, with peak biomass reached in late July.

Four Decades of Microwave Satellite Soil Moisture Observations: Product validation and inter-satellite comparisons

NASA Astrophysics Data System (ADS)

Lanka, K.; Pan, M.; Wanders, N.; Kumar, D. N.; Wood, E. F.

2017-12-01

The satellite based passive and active microwave sensors enhanced our ability to retrieve soil moisture at global scales. It has been almost four decades since the first passive microwave satellite sensor was launched in 1978. Since then soil moisture has gained considerable attention in hydro-meteorological, climate, and agricultural research resulting in the deployment of two dedicated missions in the last decade, SMOS and SMAP. Signifying the four decades of microwave remote sensing of soil moisture, this work aims to present an overview of how our knowledge in this field has improved in terms of the design of sensors and their accuracy of retrieving soil moisture. We considered daily coverage, temporal performance, and spatial performance to assess the accuracy of products corresponding to eight passive sensors (SMMR, SSM/I, TMI, AMSR-E, WindSAT, AMSR2, SMOS and SMAP), two active sensors (ERS-Scatterometer, MetOp-ASCAT), and one active/passive merged soil moisture product (ESA-CCI combined product), using 1058 ISMN in-situ stations and the VIC LSM soil moisture simulations (VICSM) over the CONUS. Our analysis indicated that the daily coverage has increased from 30 % during 1980s to 85 % (during non-winter months) with the launch of dedicated soil moisture missions SMOS and SMAP. The temporal validation of passive and active soil moisture products with the ISMN data place the range of median RMSE as 0.06-0.10 m3/m3 and median correlation as 0.20-0.68. When TMI, AMSR-E and WindSAT are evaluated, the AMSR-E sensor is found to have produced the brightness temperatures with better quality, given that these sensors are paired with same retrieval algorithm (LPRM). The ASCAT product shows a significant improvement during the temporal validation of retrievals compared to its predecessor ERS, thanks to enhanced sensor configuration. The SMAP mission, through its improved sensor design and RFI handling, shows a high retrieval accuracy under all-topography conditions. Although the retrievals from the SMOS mission are affected by issues such as RFI, the accuracy is still comparable to or better than that of AMSR-E and ASCAT sensors. All soil moisture products have indicated better agreement with the ISMN data than the VICSM, which indicate that they produce soil moisture with better accuracy than the VICSM over the CONUS.

Passive Microwave Remote Sensing of Soil Moisture

NASA Technical Reports Server (NTRS)

Njoku, Eni G.; Entekhabi, Dara

1996-01-01

Microwave remote sensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive Microwave soil moisture sensors currently considered for space operation are in the range 10-20 km. The most useful frequency range for soil moisture sensing is 1-5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods. Particularly promising are methods for optimally assimilating passive microwave data into hydrologic models. Further studies are needed to investigate the effects on microwave observations of within-footprint spatial heterogeneity of vegetation cover and subsurface soil characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale soil moisture information from remote observations.

Microwave Soil Moisture Retrieval Under Trees

NASA Technical Reports Server (NTRS)

O'Neill, P.; Lang, R.; Kurum, M.; Joseph, A.; Jackson, T.; Cosh, M.

2008-01-01

Soil moisture is recognized as an important component of the water, energy, and carbon cycles at the interface between the Earth's surface and atmosphere. Current baseline soil moisture retrieval algorithms for microwave space missions have been developed and validated only over grasslands, agricultural crops, and generally light to moderate vegetation. Tree areas have commonly been excluded from operational soil moisture retrieval plans due to the large expected impact of trees on masking the microwave response to the underlying soil moisture. Our understanding of the microwave properties of trees of various sizes and their effect on soil moisture retrieval algorithms at L band is presently limited, although research efforts are ongoing in Europe, the United States, and elsewhere to remedy this situation. As part of this research, a coordinated sequence of field measurements involving the ComRAD (for Combined Radar/Radiometer) active/passive microwave truck instrument system has been undertaken. Jointly developed and operated by NASA Goddard Space Flight Center and George Washington University, ComRAD consists of dual-polarized 1.4 GHz total-power radiometers (LH, LV) and a quad-polarized 1.25 GHz L band radar sharing a single parabolic dish antenna with a novel broadband stacked patch dual-polarized feed, a quad-polarized 4.75 GHz C band radar, and a single channel 10 GHz XHH radar. The instruments are deployed on a mobile truck with an 19-m hydraulic boom and share common control software; real-time calibrated signals, and the capability for automated data collection for unattended operation. Most microwave soil moisture retrieval algorithms developed for use at L band frequencies are based on the tau-omega model, a simplified zero-order radiative transfer approach where scattering is largely ignored and vegetation canopies are generally treated as a bulk attenuating layer. In this approach, vegetation effects are parameterized by tau and omega, the microwave vegetation opacity and single scattering albedo. One goal of our current research is to determine whether the tau-omega model can work for tree canopies given the increased scatter from trees compared to grasses and crops, and. if so, what are effective values for tau and omega for trees.

Retrieving the properties of ice-phase precipitation with multi-frequency radar measurements

NASA Astrophysics Data System (ADS)

Mace, G. G.; Gergely, M.; Mascio, J.

2017-12-01

The objective of most retrieval algorithms applied to remote sensing measurements is the microphysical properties that a model might predict such as condensed water content, particle number, or effective size. However, because ice crystals grow and aggregate into complex non spherical shapes, the microphysical properties of interest are very much dependent on the physical characteristics of the precipitation such as how mass and crystal area are distributed as a function of particle size. Such physical properties also have a strong influence on how microwave electromagnetic energy scatters from ice crystals causing significant ambiguity in retrieval algorithms. In fact, passive and active microwave remote sensing measurements are typically nearly as sensitive to the ice crystal physical properties as they are to the microphysical characteristics that are typically the aim of the retrieval algorithm. There has, however, been active development of multi frequency algorithms recently that attempt to ameliorate and even exploit this sensitivity. In this paper, we will review these approaches and present practical applications of retrieving ice crystal properties such as mass- and area dimensional relationships from single and dual frequency radar measurements of precipitating ice using data collected aboard ship in the Southern Ocean and from remote sensors in the Rocky Mountains of the Western U.S.

Studies of Antarctic Sea Ice Concentrations from Satellite Data and Their Applications

NASA Technical Reports Server (NTRS)

Comiso, Josefino C.; Steffen, Konrad; Zukor, Dorothy J. (Technical Monitor)

2001-01-01

Large changes in the sea ice cover have been observed recently. Because of the relevance of such changes to climate change studies it is important that key ice concentration data sets used for evaluating such changes are interpreted properly. High and medium resolution visible and infrared satellite data are used in conjunction with passive microwave data to study the true characteristics of the Antarctic sea ice cover, assess errors in currently available ice concentration products, and evaluate the applications and limitations of the latter in polar process studies. Cloud-free high resolution data provide valuable information about the natural distribution, stage of formation, and composition of the ice cover that enables interpretation of the large spatial and temporal variability of the microwave emissivity of Antarctic sea ice. Comparative analyses of co-registered visible, infrared and microwave data were used to evaluate ice concentrations derived from standard ice algorithms (i.e., Bootstrap and Team) and investigate the 10 to 35% difference in derived values from large areas within the ice pack, especially in the Weddell Sea, Amundsen Sea, and Ross Sea regions. Landsat and OLS data show a predominance of thick consolidated ice in these areas and show good agreement with the Bootstrap Algorithm. While direct measurements were not possible, the lower values from the Team Algorithm results are likely due to layering within the ice and snow and/or surface flooding, which are known to affect the polarization ratio. In predominantly new ice regions, the derived ice concentration from passive microwave data is usually lower than the true percentage because the emissivity of new ice changes with age and thickness and is lower than that of thick ice. However, the product provides a more realistic characterization of the sea ice cover, and are more useful in polar process studies since it allows for the identification of areas of significant divergence and polynya activities. Also, heat and salinity fluxes are proportionately increased in these areas compared to those from the thicker ice areas. A slight positive trend in ice extent and area from 1978 through 2000 is observed consistent with slight continental cooling during the period. However, the confidence in this result is only moderate because the overlap period for key instruments is just one month and the sensitivity to changes in sensor characteristics, calibration and threshold for the ice edge is quite high.

A satellite snow depth multi-year average derived from SSM/I for the high latitude regions

USGS Publications Warehouse

Biancamaria, S.; Mognard, N.M.; Boone, A.; Grippa, M.; Josberger, E.G.

2008-01-01

The hydrological cycle for high latitude regions is inherently linked with the seasonal snowpack. Thus, accurately monitoring the snow depth and the associated aerial coverage are critical issues for monitoring the global climate system. Passive microwave satellite measurements provide an optimal means to monitor the snowpack over the arctic region. While the temporal evolution of snow extent can be observed globally from microwave radiometers, the determination of the corresponding snow depth is more difficult. A dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from Special Sensor Microwave/Imager (SSM/I) brightness temperatures and was validated over the U.S. Great Plains and Western Siberia. The purpose of this study is to assess the dynamic algorithm performance over the entire high latitude (land) region by computing a snow depth multi-year field for the time period 1987-1995. This multi-year average is compared to the Global Soil Wetness Project-Phase2 (GSWP2) snow depth computed from several state-of-the-art land surface schemes and averaged over the same time period. The multi-year average obtained by the dynamic algorithm is in good agreement with the GSWP2 snow depth field (the correlation coefficient for January is 0.55). The static algorithm, which assumes a constant snow grain size in space and time does not correlate with the GSWP2 snow depth field (the correlation coefficient with GSWP2 data for January is - 0.03), but exhibits a very high anti-correlation with the NCEP average January air temperature field (correlation coefficient - 0.77), the deepest satellite snow pack being located in the coldest regions, where the snow grain size may be significantly larger than the average value used in the static algorithm. The dynamic algorithm performs better over Eurasia (with a correlation coefficient with GSWP2 snow depth equal to 0.65) than over North America (where the correlation coefficient decreases to 0.29). ?? 2007 Elsevier Inc. All rights reserved.

Real Time Monitoring of Flooding from Microwave Satellite Observations

NASA Technical Reports Server (NTRS)

Galantowicz, John F.; Frey, Herb (Technical Monitor)

2002-01-01

We have developed a new method for making high-resolution flood extent maps (e.g., at the 30-100 m scale of digital elevation models) in real-time from low-resolution (20-70 km) passive microwave observations. The method builds a "flood-potential" database from elevations and historic flood imagery and uses it to create a flood-extent map consistent with the observed open water fraction. Microwave radiometric measurements are useful for flood monitoring because they sense surface water in clear-or-cloudy conditions and can provide more timely data (e.g., compared to radars) from relatively wide swath widths and an increasing number of available platforms (DMSP, ADEOS-II, Terra, NPOESS, GPM). The chief disadvantages for flood mapping are the radiometers' low resolution and the need for local calibration of the relationship between radiances and open-water fraction. We present our method for transforming microwave sensor-scale open water fraction estimates into high-resolution flood extent maps and describe 30-day flood map sequences generated during a retrospective study of the 1993 Great Midwest Flood. We discuss the method's potential improvement through as yet unimplemented algorithm enhancements and expected advancements in microwave radiometry (e.g., improved resolution and atmospheric correction).

Analytical and Numerical Studies of Active and Passive Microwave Ocean Remote Sensing

DTIC Science & Technology

2001-09-30

of both analytical and efficient numerical methods for electromagnetics and hydrodynamics. New insights regarding these phenomena can then be applied to improve microwave active and passive remote sensing of the ocean surface.

Active-Passive Microwave Remote Sensing of Martian Permafrost and Subsurface Water

NASA Technical Reports Server (NTRS)

Raizer, V.; Linkin, V. M.; Ozorovich, Y. R.; Smythe, W. D.; Zoubkov, B.; Babkin, F.

2000-01-01

The investigation of permafrost formation global distribution and their appearance in h less than or equal 1 m thick subsurface layer would be investigated successfully by employment of active-passive microwave remote sensing techniques.

Evaluation of multichannel Wiener filters applied to fine resolution passive microwave images of first-year sea ice

NASA Technical Reports Server (NTRS)

Full, William E.; Eppler, Duane T.

1993-01-01

The effectivity of multichannel Wiener filters to improve images obtained with passive microwave systems was investigated by applying Wiener filters to passive microwave images of first-year sea ice. Four major parameters which define the filter were varied: the lag or pixel offset between the original and the desired scenes, filter length, the number of lines in the filter, and the weight applied to the empirical correlation functions. The effect of each variable on the image quality was assessed by visually comparing the results. It was found that the application of multichannel Wiener theory to passive microwave images of first-year sea ice resulted in visually sharper images with enhanced textural features and less high-frequency noise. However, Wiener filters induced a slight blocky grain to the image and could produce a type of ringing along scan lines traversing sharp intensity contrasts.

Tactical Approaches for Making a Successful Satellite Passive Microwave ESDR

NASA Astrophysics Data System (ADS)

Hardman, M.; Brodzik, M. J.; Gotberg, J.; Long, D. G.; Paget, A. C.

2014-12-01

Our NASA MEaSUREs project is producing a new, enhanced resolution gridded Earth System Data Record for the entire satellite passive microwave (SMMR, SSM/I-SSMIS and AMSR-E) time series. Our project goals are twofold: to produce a well-documented, consistently processed, high-quality historical record at higher spatial resolutions than have previously been available, and to transition the production software to the NSIDC DAAC for ongoing processing after our project completion. In support of these goals, our distributed team at BYU and NSIDC faces project coordination challenges to produce a high-quality data set that our user community will accept as a replacement for the currently available historical versions of these data. We work closely with our DAAC liaison on format specifications, data and metadata plans, and project progress. In order for the user community to understand and support our project, we have solicited a team of Early Adopters who are reviewing and evaluating a prototype version of the data. Early Adopter feedback will be critical input to our final data content and format decisions. For algorithm transparency and accountability, we have released an Algorithm Theoretical Basis Document (ATBD) and detailed supporting technical documentation, with rationale for all algorithm implementation decisions. For distributed team management, we are using collaborative tools for software revision control and issue tracking. For reliably transitioning a research-quality image reconstruction software system to production-quality software suitable for use at the DAAC, we have adopted continuous integration methods for running automated regression testing. Our presentation will summarize bothadvantages and challenges of each of these tactics in ensuring production of a successful ESDR and an enduring production software system.

Ground-Based Passive Microwave Remote Sensing Observations of Soil Moisture at S and L Band with Insight into Measurement Accuracy

NASA Technical Reports Server (NTRS)

Laymon, Charles A.; Crosson, William L.; Jackson, Thomas J.; Manu, Andrew; Tsegaye, Teferi D.; Soman, V.; Arnold, James E. (Technical Monitor)

2001-01-01

Accurate estimates of spatially heterogeneous algorithm variables and parameters are required in determining the spatial distribution of soil moisture using radiometer data from aircraft and satellites. A ground-based experiment in passive microwave remote sensing of soil moisture was conducted in Huntsville, Alabama from July 1-14, 1996 to study retrieval algorithms and their sensitivity to variable and parameter specification. With high temporal frequency observations at S and L band, we were able to observe large scale moisture changes following irrigation and rainfall events, as well as diurnal behavior of surface moisture among three plots, one bare, one covered with short grass and another covered with alfalfa. The L band emitting depth was determined to be on the order of 0-3 or 0-5 cm below 0.30 cubic centimeter/cubic centimeter with an indication of a shallower emitting depth at higher moisture values. Surface moisture behavior was less apparent on the vegetated plots than it was on the bare plot because there was less moisture gradient and because of difficulty in determining vegetation water content and estimating the vegetation b parameter. Discrepancies between remotely sensed and gravimetric, soil moisture estimates on the vegetated plots point to an incomplete understanding of the requirements needed to correct for the effects of vegetation attenuation. Quantifying the uncertainty in moisture estimates is vital if applications are to utilize remotely-sensed soil moisture data. Computations based only on the real part of the complex dielectric constant and/or an alternative dielectric mixing model contribute a relatively insignificant amount of uncertainty to estimates of soil moisture. Rather, the retrieval algorithm is much more sensitive to soil properties, surface roughness and biomass.

Application of Artificial Neural Networks to the Development of Improved Multi-Sensor Retrievals of Near-Surface Air Temperature and Humidity Over Ocean

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, Franklin R.; Clayson, Carol Anne

2012-01-01

Improved estimates of near-surface air temperature and air humidity are critical to the development of more accurate turbulent surface heat fluxes over the ocean. Recent progress in retrieving these parameters has been made through the application of artificial neural networks (ANN) and the use of multi-sensor passive microwave observations. Details are provided on the development of an improved retrieval algorithm that applies the nonlinear statistical ANN methodology to a set of observations from the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit (AMSU-A) that are currently available from the NASA AQUA satellite platform. Statistical inversion techniques require an adequate training dataset to properly capture embedded physical relationships. The development of multiple training datasets containing only in-situ observations, only synthetic observations produced using the Community Radiative Transfer Model (CRTM), or a mixture of each is discussed. An intercomparison of results using each training dataset is provided to highlight the relative advantages and disadvantages of each methodology. Particular emphasis will be placed on the development of retrievals in cloudy versus clear-sky conditions. Near-surface air temperature and humidity retrievals using the multi-sensor ANN algorithms are compared to previous linear and non-linear retrieval schemes.

Machine Learning Algorithms for Automated Satellite Snow and Sea Ice Detection

NASA Astrophysics Data System (ADS)

Bonev, George

The continuous mapping of snow and ice cover, particularly in the arctic and poles, are critical to understanding the earth and atmospheric science. Much of the world's sea ice and snow covers the most inhospitable places, making measurements from satellite-based remote sensors essential. Despite the wealth of data from these instruments many challenges remain. For instance, remote sensing instruments reside on-board different satellites and observe the earth at different portions of the electromagnetic spectrum with different spatial footprints. Integrating and fusing this information to make estimates of the surface is a subject of active research. In response to these challenges, this dissertation will present two algorithms that utilize methods from statistics and machine learning, with the goal of improving on the quality and accuracy of current snow and sea ice detection products. The first algorithm aims at implementing snow detection using optical/infrared instrument data. The novelty in this approach is that the classifier is trained using ground station measurements of snow depth that are collocated with the reflectance observed at the satellite. Several classification methods are compared using this training data to identify the one yielding the highest accuracy and optimal space/time complexity. The algorithm is then evaluated against the current operational NASA snow product and it is found that it produces comparable and in some cases superior accuracy results. The second algorithm presents a fully automated approach to sea ice detection that integrates data obtained from passive microwave and optical/infrared satellite instruments. For a particular region of interest the algorithm generates sea ice maps of each individual satellite overpass and then aggregates them to a daily composite level, maximizing the amount of high resolution information available. The algorithm is evaluated at both, the individual satellite overpass level, and at the daily composite level. Results show that at the single overpass level for clear-sky regions, the developed multi-sensor algorithm performs with accuracy similar to that of the optical/infrared products, with the advantage of being able to also classify partially cloud-obscured regions with the help of passive microwave data. At the daily composite level, results show that the algorithm's performance with respect to total ice extent is in line with other daily products, with the novelty of being fully automated and having higher resolution.

Improvements to the swath-level near-surface atmospheric state parameter retrievals within the NRL Ocean Surface Flux System (NFLUX)

NASA Astrophysics Data System (ADS)

May, J. C.; Rowley, C. D.; Meyer, H.

2017-12-01

The Naval Research Laboratory (NRL) Ocean Surface Flux System (NFLUX) is an end-to-end data processing and assimilation system used to provide near-real-time satellite-based surface heat flux fields over the global ocean. The first component of NFLUX produces near-real-time swath-level estimates of surface state parameters and downwelling radiative fluxes. The focus here will be on the satellite swath-level state parameter retrievals, namely surface air temperature, surface specific humidity, and surface scalar wind speed over the ocean. Swath-level state parameter retrievals are produced from satellite sensor data records (SDRs) from four passive microwave sensors onboard 10 platforms: the Special Sensor Microwave Imager/Sounder (SSMIS) sensor onboard the DMSP F16, F17, and F18 platforms; the Advanced Microwave Sounding Unit-A (AMSU-A) sensor onboard the NOAA-15, NOAA-18, NOAA-19, Metop-A, and Metop-B platforms; the Advanced Technology Microwave Sounder (ATMS) sensor onboard the S-NPP platform; and the Advanced Microwave Scannin Radiometer 2 (AMSR2) sensor onboard the GCOM-W1 platform. The satellite SDRs are translated into state parameter estimates using multiple polynomial regression algorithms. The coefficients to the algorithms are obtained using a bootstrapping technique with all available brightness temperature channels for a given sensor, in addition to a SST field. For each retrieved parameter for each sensor-platform combination, unique algorithms are developed for ascending and descending orbits, as well as clear vs cloudy conditions. Each of the sensors produces surface air temperature and surface specific humidity retrievals. The SSMIS and AMSR2 sensors also produce surface scalar wind speed retrievals. Improvement is seen in the SSMIS retrievals when separate algorithms are used for the even and odd scans, with the odd scans performing better than the even scans. Currently, NFLUX treats all SSMIS scans as even scans. Additional improvement in all of the surface retrievals comes from using a 3-hourly SST field, as opposed to a daily SST field.

Synergistic estimation of surface parameters from jointly using optical and microwave observations in EOLDAS

NASA Astrophysics Data System (ADS)

Timmermans, Joris; Gomez-Dans, Jose; Lewis, Philip; Loew, Alexander; Schlenz, Florian

2017-04-01

The large amount of remote sensing data nowadays available provides a huge potential for monitoring crop development, drought conditions and water efficiency. This potential however not been realized yet because algorithms for land surface parameter retrieval mostly use data from only a single sensor. Consequently products that combine different low-level observations from different sensors are hard to find. The lack of synergistic retrieval is caused because it is easier to focus on single sensor types/footprints and temporal observation times, than to find a way to compensate for differences. Different sensor types (microwave/optical) require different radiative transfer (RT) models and also require consistency between the models to have any impact on the retrieval of soil moisture by a microwave instrument. Varying spatial footprints require first proper collocation of the data before one can scale between different resolutions. Considering these problems, merging optical and microwave observations have not been performed yet. The goal of this research was to investigate the potential of integrating optical and microwave RT models within the Earth Observation Land Data Assimilation System (EOLDAS) synergistically to derive biophysical parameters. This system uses a Bayesian data assimilation approach together with observation operators such as the PROSAIL model to estimate land surface parameters. For the purpose of enabling the system to integrate passive microwave radiation (from an ELBARRA II passive microwave radiometer), the Community Microwave Emission Model (CMEM) RT-model, was integrated within the EOLDAS system. In order to quantify the potential, a variety of land surface parameters was chosen to be retrieved from the system, in particular variables that a) impact only optical RT (such as leaf water content and leaf dry matter), b) only impact the microwave RT (such as soil moisture and soil temperature), and c) Leaf Area Index (LAI) that impacts both optical and microwave RT. The results show a high potential when both optical and microwave are used independently. Using only RapidEye only with SAIL RT model, LAI was estimated with R=0.68 with p=0.09, although estimating leaf water content and dry matter showed lower correlations |R|<0.4. The results for retrieving soil temperature and leaf area index retrievals using only (passive microwave) Elbarra-II observations were good with respectively R=[0.85, 0.79], P=[0.0, 0.0], when focusing on dry-spells (of at least 9 days) only the results respectively [R=0.73, and P=0.0], and R=0.89 and R=0.77 for respectively the trend and anomalies. Synergistically using optical and microwave shows also a good potential. This scenario shows that absolute errors improved (with RMSE=1.22 and S=0.89), but with degrading correlations (R=0.59 and P=0.04); the sparse optical observations only improved part of the temporal domain. However in general the synergistic retrieval showed good potential; microwave data provides better information concerning the overall trend of the retrieved LAI due to the regular acquisitions, while optical data provides better information concerning the absolute values of the LAI.

New Physical Algorithms for Downscaling SMAP Soil Moisture

NASA Astrophysics Data System (ADS)

Sadeghi, M.; Ghafari, E.; Babaeian, E.; Davary, K.; Farid, A.; Jones, S. B.; Tuller, M.

2017-12-01

The NASA Soil Moisture Active Passive (SMAP) mission provides new means for estimation of surface soil moisture at the global scale. However, for many hydrological and agricultural applications the spatial SMAP resolution is too low. To address this scale issue we fused SMAP data with MODIS observations to generate soil moisture maps at 1-km spatial resolution. In course of this study we have improved several existing empirical algorithms and introduced a new physical approach for downscaling SMAP data. The universal triangle/trapezoid model was applied to relate soil moisture to optical/thermal observations such as NDVI, land surface temperature and surface reflectance. These algorithms were evaluated with in situ data measured at 5-cm depth. Our results demonstrate that downscaling SMAP soil moisture data based on physical indicators of soil moisture derived from the MODIS satellite leads to higher accuracy than that achievable with empirical downscaling algorithms. Keywords: Soil moisture, microwave data, downscaling, MODIS, triangle/trapezoid model.

Satellite Observation Systems for Polar Climate Change Studies

NASA Technical Reports Server (NTRS)

Comiso, Josefino C.

2012-01-01

The key observational tools for detecting large scale changes of various parameters in the polar regions have been satellite sensors. The sensors include passive and active satellite systems in the visible, infrared and microwave frequencies. The monitoring started with Tiros and Nimbus research satellites series in the 1970s but during the period, not much data was stored digitally because of limitations and cost of the needed storage systems. Continuous global data came about starting with the launch of ocean color, passive microwave, and thermal infrared sensors on board Nimbus-7 and Synthetic Aperture Radar, Radar Altimeter and Scatterometer on board SeaSat satellite both launched in 1978. The Nimbus-7 lasted longer than expected and provided about 9 years of useful data while SeaSat quit working after 3 months but provided very useful data that became the baseline for follow-up systems with similar capabilities. Over the years, many new sensors were launched, some from Japan Aeronautics and Space Agency (JAXA), some from the European Space Agency (ESA) and more recently, from RuSSia, China, Korea, Canada and India. For polar studies, among the most useful sensors has been the passive microwave sensor which provides day/night and almost all weather observation of the surface. The sensor provide sea surface temperature, precipitation, wind, water vapor and sea ice concentration data that have been very useful in monitoring the climate of the region. More than 30 years of such data are now available, starting with the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7, the Special Scanning Microwave/Imager (SSM/I) on board a Defense Meteorological Satellite Program (DMSP) and the Advanced Microwave Scanning Radiometer on board the EOS/ Aqua satellite. The techniques that have been developed to derive geophysical parameters from data provided by these and other sensors and associated instrumental and algorithm errors and validation techniques will be discussed. An important issue is the organization and storage of hundreds of terabytes of data collected by even just a few of these satellite sensors. Advances in mass storage and computer technology have made it possible to overcome many of the collection and archival problems and the availability of comprehensive satellite data sets put together by NASA's Earth Observing System project will be discussed.

Botswana water and surface energy balance research program. Part 2: Large scale moisture and passive microwaves

NASA Technical Reports Server (NTRS)

Vandegriend, A. A.; Owe, M.; Chang, A. T. C.

1992-01-01

The Botswana water and surface energy balance research program was developed to study and evaluate the integrated use of multispectral satellite remote sensing for monitoring the hydrological status of the Earth's surface. The research program consisted of two major, mutually related components: a surface energy balance modeling component, built around an extensive field campaign; and a passive microwave research component which consisted of a retrospective study of large scale moisture conditions and Nimbus scanning multichannel microwave radiometer microwave signatures. The integrated approach of both components are explained in general and activities performed within the passive microwave research component are summarized. The microwave theory is discussed taking into account: soil dielectric constant, emissivity, soil roughness effects, vegetation effects, optical depth, single scattering albedo, and wavelength effects. The study site is described. The soil moisture data and its processing are considered. The relation between observed large scale soil moisture and normalized brightness temperatures is discussed. Vegetation characteristics and inverse modeling of soil emissivity is considered.

Monitoring Snow on ice as Critical Habitat for Ringed Seals

NASA Astrophysics Data System (ADS)

Kelly, B. P.; Moran, J.; Douglas, D. C.; Nghiem, S. V.

2007-12-01

Ringed seals are the primary prey of polar bears, and they are found in all seasonally ice covered seas of the northern hemisphere as well as in several freshwater lakes. The presence of snow covered sea ice is essential for successful ringed seal reproduction. Ringed seals abrade holes in the ice allowing them to surface and breathe under the snow cover. Where snow accumulates to sufficient depths, ringed seals excavate subnivean lairs above breathing holes. They rest, give birth, and nurse their young in those lairs. Temperatures within the lairs remain within a few degrees of freezing, well within the zone of thermal neutrality for newborn ringed seals, even at ambient temperatures of -30° C. High rates of seal mortality have been recorded when early snow melt caused lairs to collapse exposing newborn seals to predators and to subsequent extreme cold events. As melt onset dates come earlier in the Arctic Ocean, ringed seal populations (and the polar bears that depend upon them) will be increasingly challenged. We determined dates of lair abandonment by ringed seals fitted with radio transmitters in the Beaufort Sea (n = 60). We compared abandonment dates to melt onset dates measured in the field, as well as estimated dates derived from active (Ku-band backscatter) and passive (SSM/I) microwave satellite imagery. Date of snow melt significantly improved models of environmental influences on the timing of lair abandonment. We used an algorithm based on multi-channel means and variances of passive microwave data to detect melt onset dates. Those melt onset dates predicted the date of lair abandonment ± 3 days (r 2 = 0.982, p = 0.001). The predictive power of passive microwave proxies combined with their historical record suggest they could serve to monitor critical changes to ringed seal habitat.

Towards an improved soil moisture retrieval for organic-rich soils from SMOS passive microwave L-band observations

NASA Astrophysics Data System (ADS)

Bircher, Simone; Richaume, Philippe; Mahmoodi, Ali; Mialon, Arnaud; Fernandez-Moran, Roberto; Wigneron, Jean-Pierre; Demontoux, François; Jonard, François; Weihermüller, Lutz; Andreasen, Mie; Rautiainen, Kimmo; Ikonen, Jaakko; Schwank, Mike; Drusch, Mattias; Kerr, Yann H.

2017-04-01

From the passive L-band microwave radiometer onboard the Soil Moisture and Ocean Salinity (SMOS) space mission global surface soil moisture data is retrieved every 2 - 3 days. Thus far, the empirical L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model applied in the SMOS soil moisture retrieval algorithm is exclusively calibrated over test sites in dry and temperate climate zones. Furthermore, the included dielectric mixing model relating soil moisture to relative permittivity accounts only for mineral soils. However, soil moisture monitoring over the higher Northern latitudes is crucial since these regions are especially sensitive to climate change. A considerable positive feedback is expected if thawing of these extremely organic soils supports carbon decomposition and release to the atmosphere. Due to differing structural characteristics and thus varying bound water fractions, the relative permittivity of organic material is lower than that of the most mineral soils at a given water content. This assumption was verified by means of L-band relative permittivity laboratory measurements of organic and mineral substrates from various sites in Denmark, Finland, Scotland and Siberia using a resonant cavity. Based on these data, a simple empirical dielectric model for organic soils was derived and implemented in the SMOS Soil Moisture Level 2 Prototype Processor (SML2PP). Unfortunately, the current SMOS retrieved soil moisture product seems to show unrealistically low values compared to in situ soil moisture data collected from organic surface layers in North America, Europe and the Tibetan Plateau so that the impact of the dielectric model for organic soils cannot really be tested. A simplified SMOS processing scheme yielding higher soil moisture levels has recently been proposed and is presently under investigation. Furthermore, recalibration of the model parameters accounting for vegetation and roughness effects that were thus far only evaluated using the default dielectric model for mineral soils is ongoing for the "organic" L-MEB version. Additionally, in order to decide where a soil moisture retrieval using the "organic" dielectric model should be triggered, information on soil organic matter content in the soil surface layer has to be considered in the retrieval algorithm. For this purpose, SoilGrids (www.soilgrids.org) providing soil organic carbon content (SOCC) in g/kg is under study. A SOCC threshold based on the relation between the SoilGrids' SOCC and the presence of organic soil surface layers (relevant to alter the microwave L-band emissions from the land surface) in the SoilGrids' source soil profile information has to be established. In this communication, we present the current status of the above outlined studies with the objective to advance towards an improved soil moisture retrieval for organic-rich soils from SMOS passive microwave L-band observations.

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

NASA Astrophysics Data System (ADS)

Mugnai, A.; Smith, E. A.; Tripoli, G. J.; Bizzarri, B.; Casella, D.; Dietrich, S.; Di Paola, F.; Panegrossi, G.; Sanò, P.

2013-04-01

Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) is a EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) program, designed to deliver satellite products of hydrological interest (precipitation, soil moisture and snow parameters) over the European and Mediterranean region to research and operations users worldwide. Six satellite precipitation algorithms and concomitant precipitation products are the responsibility of various agencies in Italy. Two of these algorithms have been designed for maximum accuracy by restricting their inputs to measurements from conical and cross-track scanning passive microwave (PMW) radiometers mounted on various low Earth orbiting satellites. They have been developed at the Italian National Research Council/Institute of Atmospheric Sciences and Climate in Rome (CNR/ISAC-Rome), and are providing operational retrievals of surface rain rate and its phase properties. Each of these algorithms is physically based, however, the first of these, referred to as the Cloud Dynamics and Radiation Database (CDRD) algorithm, uses a Bayesian-based solution solver, while the second, referred to as the PMW Neural-net Precipitation Retrieval (PNPR) algorithm, uses a neural network-based solution solver. Herein we first provide an overview of the two initial EU research and applications programs that motivated their initial development, EuroTRMM and EURAINSAT (European Satellite Rainfall Analysis and Monitoring at the Geostationary Scale), and the current H-SAF program that provides the framework for their operational use and continued development. We stress the relevance of the CDRD and PNPR algorithms and their precipitation products in helping secure the goals of H-SAF's scientific and operations agenda, the former helpful as a secondary calibration reference to other algorithms in H-SAF's complete mix of algorithms. Descriptions of the algorithms' designs are provided including a few examples of their performance. This aspect of the development of the two algorithms is placed in the context of what we refer to as the TRMM era, which is the era denoting the active and ongoing period of the Tropical Rainfall Measuring Mission (TRMM) that helped inspire their original development. In 2015, the ISAC-Rome precipitation algorithms will undergo a transformation beginning with the upcoming Global Precipitation Measurement (GPM) mission, particularly the GPM Core Satellite technologies. A few years afterward, the first pair of imaging and sounding Meteosat Third Generation (MTG) satellites will be launched, providing additional technological advances. Various of the opportunities presented by the GPM Core and MTG satellites for improving the current CDRD and PNPR precipitation retrieval algorithms, as well as extending their product capability, are discussed.

Arctic sea-ice variations from time-lapse passive microwave imagery

USGS Publications Warehouse

Campbell, W.J.; Ramseier, R.O.; Zwally, H.J.; Gloersen, P.

1980-01-01

This paper presents: (1) a short historical review of the passive microwave research on sea ice which established the observational and theoretical base permitting the interpretation of the first passive microwave images of Earth obtained by the Nimbus-5 ESMR; (2) the construction of a time-lapse motion picture film of a 16-month set of serial ESMR images to aid in the formidable data analysis task; and (3) a few of the most significant findings resulting from an early analysis of these data, using selected ESMR images to illustrate these findings. ?? 1980 D. Reidel Publishing Co.

Silicon carbide passive heating elements in microwave-assisted organic synthesis.

PubMed

Kremsner, Jennifer M; Kappe, C Oliver

2006-06-09

Microwave-assisted organic synthesis in nonpolar solvents is investigated utilizing cylinders of sintered silicon carbide (SiC)--a chemically inert and strongly microwave absorbing material--as passive heating elements (PHEs). These heating inserts absorb microwave energy and subsequently transfer the generated thermal energy via conduction phenomena to the reaction mixture. The use of passive heating elements allows otherwise microwave transparent or poorly absorbing solvents such as hexane, carbon tetrachloride, tetrahydrofuran, dioxane, or toluene to be effectively heated to temperatures far above their boiling points (200-250 degrees C) under sealed vessel microwave conditions. This opens up the possibility to perform microwave synthesis in unpolar solvent environments as demonstrated successfully for several organic transformations, such as Claisen rearrangements, Diels-Alder reactions, Michael additions, N-alkylations, and Dimroth rearrangements. This noninvasive technique is a particularly valuable tool in cases where other options to increase the microwave absorbance of the reaction medium, such as the addition of ionic liquids as heating aids, are not feasible due to an incompatibility of the ionic liquid with a particular substrate. The SiC heating elements are thermally and chemically resistant to 1500 degrees C and compatible with any solvent or reagent.

Active and Passive Microwave Determination of the Circulation and Characteristics of Weddell and Ross Sea Ice

NASA Technical Reports Server (NTRS)

Drinkwater, Mark R.; Liu, Xiang

2000-01-01

A combination of satellite microwave data sets are used in conjunction with ECMWF (Medium Range Weather Forecasts) and NCEP (National Center for Environment Prediction) meteorological analysis fields to investigate seasonal variability in the circulation and sea-ice dynamics of the Weddell and Ross Seas. Results of sea-ice tracking using SSM/I (Special Sensor Microwave Imager), Scatterometer and SAR images are combined with in-situ data derived from Argos buoys and GPS drifters to validate observed drift patterns. Seasonal 3-month climatologies of ice motion and drift speed variance illustrate the response of the sea-ice system to seasonal forcing. A melt-detection algorithm is used to track the onset of seasonal melt, and to determine the extent and duration of atmospherically-led surface melting during austral summer. Results show that wind-driven drift regulates the seasonal distribution and characteristics of sea-ice and the intensity of the cyclonic Gyre circulation in these two regions.

Microwave remote sensing of sea ice in the AIDJEX Main Experiment

USGS Publications Warehouse

Campbell, W.J.; Wayenberg, J.; Ramseyer, J.B.; Ramseier, R.O.; Vant, M.R.; Weaver, R.; Redmond, A.; Arsenaul, L.; Gloersen, P.; Zwally, H.J.; Wilheit, T.T.; Chang, T.C.; Hall, D.; Gray, L.; Meeks, D.C.; Bryan, M.L.; Barath, F.T.; Elachi, C.; Leberl, F.; Farr, Tom

1978-01-01

During the AIDJEX Main Experiment, April 1975 through May 1976, a comprehensive microwave sensing program was performed on the sea ice of the Beaufort Sea. Surface and aircraft measurements were obtained during all seasons using a wide variety of active and passive microwave sensors. The surface program obtained passive microwave measurements of various ice types using four antennas mounted on a tracked vehicle. In three test regions, each with an area of approximately 1.5 ?? 104 m2, detailed ice crystallographic, dielectric properties, and brightness temperatures of first-year, multiyear, and first-year/multiyear mixtures were measured. A NASA aircraft obtained passive microwave measurements of the entire area of the AIDJEX manned station array (triangle) during each of 18 flights. This verified the earlier reported ability to distinguish first-year and multiyear ice types and concentration and gave new information on ways to observe ice mixtures and thin ice types. The active microwave measurements from aircraft included those from an X- and L-band radar and from a scatterometer. The former is used to study a wide variety of ice features and to estimate deformations, while both are equally usable to observe ice types. With the present data, only the scatterometer can be used to distinguish positively multiyear from first-year and various types of thin ice. This is best done using coupled active and passive microwave sensing. ?? 1978 D. Reidel Publishing Company.

A Prototype Hail Detection Algorithm and Hail Climatology Developed with the Advanced Microwave Sounding Unit (AMSU)

NASA Technical Reports Server (NTRS)

Ferraro, Ralph; Beauchamp, James; Cecil, Dan; Heymsfeld, Gerald

2015-01-01

In previous studies published in the open literature, a strong relationship between the occurrence of hail and the microwave brightness temperatures (primarily at 37 and 85 GHz) was documented. These studies were performed with the Nimbus-7 SMMR, the TRMM Microwave Imager (TMI) and most recently, the Aqua AMSR-E sensor. This lead to climatologies of hail frequency from TMI and AMSR-E, however, limitations include geographical domain of the TMI sensor (35 S to 35 N) and the overpass time of the Aqua satellite (130 am/pm local time), both of which reduce an accurate mapping of hail events over the global domain and the full diurnal cycle. Nonetheless, these studies presented exciting, new applications for passive microwave sensors. Since 1998, NOAA and EUMETSAT have been operating the AMSU-A/B and the MHS on several operational satellites: NOAA-15 through NOAA-19; MetOp-A and -B. With multiple satellites in operation since 2000, the AMSU/MHS sensors provide near global coverage every 4 hours, thus, offering a much larger time and temporal sampling than TRMM or AMSR-E. With similar observation frequencies near 30 and 85 GHz and additionally three at the 183 GHz water vapor band, the potential to detect strong convection associated with severe storms on a more comprehensive time and space scale exists. In this study, we develop a prototype AMSU-based hail detection algorithm through the use of collocated satellite and surface hail reports over the continental U.S. for a 12-year period (2000-2011). Compared with the surface observations, the algorithm detects approximately 40 percent of hail occurrences. The simple threshold algorithm is then used to generate a hail climatology that is based on all available AMSU observations during 2000-11 that is stratified in several ways, including total hail occurrence by month (March through September), total annual, and over the diurnal cycle. Independent comparisons are made compared to similar data sets derived from other satellite, ground radar and surface reports. The algorithm was also applied to global land measurements for a single year and showed close agreement with other satellite based hail climatologies. Such a product could serve as a prototype for use with a future geostationary based microwave sensor such as NASA's proposed PATH mission.

Passive Polarimetric Microwave Signatures Observed Over Antarctica

USDA-ARS?s Scientific Manuscript database

WindSat satellite-based fully polarimetric passive microwave observations, expressed in the form of the Stokes vector, were analyzed over the Antarctic ice sheet. The vertically and horizontally polarized brightness temperatures (first two Stokes components) from WindSat are shown to be consistent w...

High spatial resolution passive microwave sounding systems

NASA Technical Reports Server (NTRS)

Staelin, D. H.; Rosenkranz, P. W.; Bonanni, P. G.; Gasiewski, A. W.

1986-01-01

Two extensive series of flights aboard the ER-2 aircraft were conducted with the MIT 118 GHz imaging spectrometer together with a 53.6 GHz nadir channel and a TV camera record of the mission. Other microwave sensors, including a 183 GHz imaging spectrometer were flown simultaneously by other research groups. Work also continued on evaluating the impact of high-resolution passive microwave soundings upon numerical weather prediction models.

Microwave remote sensing: Active and passive. Volume 1 - Microwave remote sensing fundamentals and radiometry

NASA Technical Reports Server (NTRS)

Ulaby, F. T.; Moore, R. K.; Fung, A. K.

1981-01-01

The three components of microwave remote sensing (sensor-scene interaction, sensor design, and measurement techniques), and the applications to geoscience are examined. The history of active and passive microwave sensing is reviewed, along with fundamental principles of electromagnetic wave propagation, antennas, and microwave interaction with atmospheric constituents. Radiometric concepts are reviewed, particularly for measurement problems for atmospheric and terrestrial sources of natural radiation. Particular attention is given to the emission by atmospheric gases, clouds, and rain as described by the radiative transfer function. Finally, the operation and performance characteristics of radiometer receivers are discussed, particularly for measurement precision, calibration techniques, and imaging considerations.

Spatially enhanced passive microwave derived soil moisture: capabilities and opportunities

USDA-ARS?s Scientific Manuscript database

Low frequency passive microwave remote sensing is a proven technique for soil moisture retrieval, but its coarse resolution restricts the range of applications. Downscaling, otherwise known as disaggregation, has been proposed as the solution to spatially enhance these coarse resolution soil moistur...

Report from the Passive Microwave Data Set Management Workshop

NASA Technical Reports Server (NTRS)

Armstrong, Ed; Conover, Helen; Goodman, Michael; Krupp, Brian; Liu, Zhong; Moses, John; Ramapriyan, H. K.; Scott, Donna; Smith, Deborah; Weaver, Ronald

2011-01-01

Passive microwave data sets are some of the most important data sets in the Earth Observing System Data and Information System (EOSDIS), providing data as far back as the early 1970s. The widespread use of passive microwave (PM) radiometer data has led to their collection and distribution over the years at several different Earth science data centers. The user community is often confused by this proliferation and the uneven spread of information about the data sets. In response to this situation, a Passive Microwave Data Set Management Workshop was held 17 ]19 May 2011 at the Global Hydrology Resource Center, sponsored by the NASA Earth Science Data and Information System (ESDIS) Project. The workshop attendees reviewed all primary (Level 1 ]3) PM data sets from NASA and non ]NASA sensors held by NASA Distributed Active Archive Centers (DAACs), as well as high ]value data sets from other NASA ]funded organizations. This report provides the key findings and recommendations from the workshop as well as detailed tabluations of the datasets considered.

An Orbital "Virtual Radar" from TRMM Passive Microwave and Lightning Observations

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.

2004-01-01

The retrieval of vertical structure from joint passive microwave and lightning observations is demonstrated. Three years of data from the TRMM (Tropical Rainfall Measuring Mission) are used as a training dataset for regression and classification neural networks; the TMI (TRMM Microwave Imager) and LIS (Lightning Imaging Sensor) provide the inputs, the PR (Precipitation Radar) provides the training targets. Both vertical reflectivity profile categorization (into 9 convective, 7 stratiform, 2 mixed and 6 anvil types) and geophysical parameters (surface rainfall, vertically integrated liquid (VIL), ice water content (IWC) and echo tops) are retrieved. Retrievals are successful over both land and ocean surfaces. The benefit of using lightning observations as inputs to these retrievals is quantitatively demonstrated; lightning essentially provides an additional convective/stratiform discriminator, and is most important for isolation of midlevel (tops in the mixed phase region) convective profile types (this is because high frequency passive microwave observations already provide good convective/stratiform discrimination for deep convective profiles). This is highly relevant as midlevel convective profiles account for an extremely large fraction of tropical rainfall, and yet are most difficult to discriminate from comparable-depth stratiform profile types using passive microwave observations alone.

Active/Passive Remote Sensing of the Ocean Surface at Microwave Frequencies

DTIC Science & Technology

1999-09-30

This report summarizes research activities and results obtained under grant N000l4-99-1-0627 "Active/Passive Remote Sensing of the Ocean Surface at...Measurements were completed during April 1999 by the Microwave Remote Sensing Laboratory at the University of Massachusetts.

Collaboration on Development and Validation of the AMSR-E Snow Water Equivalent Algorithm

NASA Technical Reports Server (NTRS)

Armstrong, Richard L.

2000-01-01

The National Snow and Ice Data Center (NSIDC) has produced a global SMMR and SSM/I Level 3 Brightness Temperature data set in the Equal Area Scalable Earth (EASE) Grid for the period 1978 to 2000. Processing of current data is-ongoing. The EASE-Grid passive microwave data sets are appropriate for algorithm development and validation prior to the launch of AMSR-E. Having the lower frequency channels of SMMR (6.6 and 10.7 GHz) and the higher frequency channels of SSM/I (85.5 GHz) in the same format will facilitate the preliminary development of applications which could potentially make use of similar frequencies from AMSR-E (6.9, 10.7, 89.0 GHz).

Temperature Crosstalk Sensitivity of the Kummerow Rainfall Algorithm

NASA Technical Reports Server (NTRS)

Spencer, Roy W.; Petrenko, Boris

1999-01-01

Even though the signal source for passive microwave retrievals is thermal emission, retrievals of non-temperature geophysical parameters typically do not explicitly take into account the effects of temperature change on the retrievals. For global change research, changes in geophysical parameters (e.g. water vapor, rainfall, etc.) are referenced to the accompanying changes in temperature. If the retrieval of a certain parameter has a cross-talk response from temperature change alone, the retrievals might not be very useful for climate research. We investigated the sensitivity of the Kummerow rainfall retrieval algorithm to changes in air temperature. It was found that there was little net change in total rainfall with air temperature change. However, there were non-negligible changes within individual rain rate categories.

Advances in Assimilation of Satellite-Based Passive Microwave Observations for Soil-Moisture Estimation

NASA Technical Reports Server (NTRS)

De Lannoy, Gabrielle J. M.; Pauwels, Valentijn; Reichle, Rolf H.; Draper, Clara; Koster, Randy; Liu, Qing

2012-01-01

Satellite-based microwave measurements have long shown potential to provide global information about soil moisture. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS, [1]) mission as well as the future National Aeronautics and Space Administration (NASA) Soil Moisture Active and Passive (SMAP, [2]) mission measure passive microwave emission at L-band frequencies, at a relatively coarse (40 km) spatial resolution. In addition, SMAP will measure active microwave signals at a higher spatial resolution (3 km). These new L-band missions have a greater sensing depth (of -5cm) compared with past and present C- and X-band microwave sensors. ESA currently also disseminates retrievals of SMOS surface soil moisture that are derived from SMOS brightness temperature observations and ancillary data. In this research, we address two major challenges with the assimilation of recent/future satellite-based microwave measurements: (i) assimilation of soil moisture retrievals versus brightness temperatures for surface and root-zone soil moisture estimation and (ii) scale-mismatches between satellite observations, models and in situ validation data.

NASA Sea Ice Validation Program for the Defense Meteorological Satellite Program Special Sensor Microwave Imager

NASA Technical Reports Server (NTRS)

Cavalieri, Donald J. (Editor); Crawford, John P.; Drinkwater, Mark R.; Emery, William J.; Eppler, Duane T.; Farmer, L. Dennis; Fowler, Charles W.; Goodberlet, Mark; Jentz, Robert R.; Milman, Andrew

1992-01-01

The history of the program is described along with the SSM/I sensor, including its calibration and geolocation correction procedures used by NASA, SSM/I data flow, and the NASA program to distribute polar gridded SSM/I radiances and sea ice concentrations (SIC) on CD-ROMs. Following a discussion of the NASA algorithm used to convert SSM/I radiances to SICs, results of 95 SSM/I-MSS Landsat IC comparisons for regions in both the Arctic and the Antarctic are presented. The Landsat comparisons show that the overall algorithm accuracy under winter conditions is 7 pct. on average with 4 pct. negative bias. Next, high resolution active and passive microwave image mosaics from coordinated NASA and Navy aircraft underflights over regions of the Beaufort and Chukchi seas in March 1988 were used to show that the algorithm multiyear IC accuracy is 11 pct. on average with a positive bias of 12 pct. Ice edge crossings of the Bering Sea by the NASA DC-8 aircraft were used to show that the SSM/I 15 pct. ice concentration contour corresponds best to the location of the initial bands at the ice edge. Finally, a summary of results and recommendations for improving the SIC retrievals from spaceborne radiometers are provided.

Optimum Image Formation for Spaceborne Microwave Radiometer Products.

PubMed

Long, David G; Brodzik, Mary J

2016-05-01

This paper considers some of the issues of radiometer brightness image formation and reconstruction for use in the NASA-sponsored Calibrated Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature Earth System Data Record project, which generates a multisensor multidecadal time series of high-resolution radiometer products designed to support climate studies. Two primary reconstruction algorithms are considered: the Backus-Gilbert approach and the radiometer form of the scatterometer image reconstruction (SIR) algorithm. These are compared with the conventional drop-in-the-bucket (DIB) gridded image formation approach. Tradeoff study results for the various algorithm options are presented to select optimum values for the grid resolution, the number of SIR iterations, and the BG gamma parameter. We find that although both approaches are effective in improving the spatial resolution of the surface brightness temperature estimates compared to DIB, SIR requires significantly less computation. The sensitivity of the reconstruction to the accuracy of the measurement spatial response function (MRF) is explored. The partial reconstruction of the methods can tolerate errors in the description of the sensor measurement response function, which simplifies the processing of historic sensor data for which the MRF is not known as well as modern sensors. Simulation tradeoff results are confirmed using actual data.

Retrieval of Ocean Surface Windspeed and Rainrate from the Hurricane Imaging Radiometer (HIRAD) Brightness Temperature Observations

NASA Technical Reports Server (NTRS)

Biswas, Sayak K.; Jones, Linwood; Roberts, Jason; Ruf, Christopher; Ulhorn, Eric; Miller, Timothy

2012-01-01

The Hurricane Imaging Radiometer (HIRAD) is a new airborne synthetic aperture passive microwave radiometer capable of wide swath imaging of the ocean surface wind speed under heavy precipitation e.g. in tropical cyclones. It uses interferometric signal processing to produce upwelling brightness temperature (Tb) images at its four operating frequencies 4, 5, 6 and 6.6 GHz [1,2]. HIRAD participated in NASA s Genesis and Rapid Intensification Processes (GRIP) mission during 2010 as its first science field campaign. It produced Tb images with 70 km swath width and 3 km resolution from a 20 km altitude. From this, ocean surface wind speed and column averaged atmospheric liquid water content can be retrieved across the swath. The column averaged liquid water then could be related to an average rain rate. The retrieval algorithm (and the HIRAD instrument itself) is a direct descendant of the nadir-only Stepped Frequency Microwave Radiometer that is used operationally by the NOAA Hurricane Research Division to monitor tropical cyclones [3,4]. However, due to HIRAD s slant viewing geometry (compared to nadir viewing SFMR) a major modification is required in the algorithm. Results based on the modified algorithm from the GRIP campaign will be presented in the paper.

Parameterization of L-, C- and X-band Radiometer-based Soil Moisture Retrieval Algorithm Using In-situ Validation Sites

NASA Astrophysics Data System (ADS)

Gao, Y.; Colliander, A.; Burgin, M. S.; Walker, J. P.; Chae, C. S.; Dinnat, E.; Cosh, M. H.; Caldwell, T. G.

2017-12-01

Passive microwave remote sensing has become an important technique for global soil moisture estimation over the past three decades. A number of missions carrying sensors at different frequencies that are capable for soil moisture retrieval have been launched. Among them, there are Japan Aerospace Exploration Agency's (JAXA's) Advanced Microwave Scanning Radiometer-EOS (AMSR-E) launched in May 2002 on the National Aeronautics and Space Administration (NASA) Aqua satellite (ceased operation in October 2011), European Space Agency's (ESA's) Soil Moisture and Ocean Salinity (SMOS) mission launched in November 2009, JAXA's Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite launched in May 2012, and NASA's Soil Moisture Active Passive (SMAP) mission launched in January 2015. Therefore, there is an opportunity to develop a consistent inter-calibrated long-term soil moisture data record based on the availability of these four missions. This study focuses on the parametrization of the tau-omega model at L-, C- and X-band using the brightness temperature (TB) observations from the four missions and the in-situ soil moisture and soil temperature data from core validation sites across various landcover types. The same ancillary data sets as the SMAP baseline algorithm are applied for retrieval at different frequencies. Preliminary comparison of SMAP and AMSR2 TB observations against forward-simulated TB at the Yanco site in Australia showed a generally good agreement with each other and higher correlation for the vertical polarization (R=0.96 for L-band and 0.93 for C- and X-band). Simultaneous calibrations of the vegetation parameter b and roughness parameter h at both horizontal and vertical polarizations are also performed. Finally, a set of model parameters for successfully retrieving soil moisture at different validation sites at L-, C- and X-band respectively are presented. The research described in this paper is supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Copyright 2017. All rights reserved.

Estimating terrestrial snow depth with the Topex-Poseidon altimeter and radiometer

USGS Publications Warehouse

Papa, F.; Legresy, B.; Mognard, N.M.; Josberger, E.G.; Remy, F.

2002-01-01

Active and passive microwave measurements obtained by the dual-frequency Topex-Poseidon radar altimeter from the Northern Great Plains of the United States are used to develop a snow pack radar backscatter model. The model results are compared with daily time series of surface snow observations made by the U.S. National Weather Service. The model results show that Ku-band provides more accurate snow depth determinations than does C-band. Comparing the snow depth determinations derived from the Topex-Poseidon nadir-looking passive microwave radiometers with the oblique-looking Satellite Sensor Microwave Imager (SSM/I) passive microwave observations and surface observations shows that both instruments accurately portray the temporal characteristics of the snow depth time series. While both retrievals consistently underestimate the actual snow depths, the Topex-Poseidon results are more accurate.

Snowfall Rate Retrieval Using Passive Microwave Measurements and Its Applications in Weather Forecast and Hydrology

NASA Technical Reports Server (NTRS)

Meng, Huan; Ferraro, Ralph; Kongoli, Cezar; Yan, Banghua; Zavodsky, Bradley; Zhao, Limin; Dong, Jun; Wang, Nai-Yu

2015-01-01

(AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has also been developed. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. It employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derives the probability of snowfall. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model. A method adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The SFR products are being used mainly in two communities: hydrology and weather forecast. Global blended precipitation products traditionally do not include snowfall derived from satellites because such products were not available operationally in the past. The ATMS and AMSU/MHS SFR now provide the winter precipitation information for these blended precipitation products. Weather forecasters mainly rely on radar and station observations for snowfall forecast. The SFR products can fill in gaps where no conventional snowfall data are available to forecasters. The products can also be used to confirm radar and gauge snowfall data and increase forecasters' confidence in their prediction.

Satellite passive microwave rain rate measurement over croplands during spring, summer and fall

NASA Technical Reports Server (NTRS)

Spencer, R. W.

1984-01-01

Rain-rate algorithms for spring, summer and fall that have been developed from comparisons between the brightness temperatures measured by the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and rain rates derived from operational WSR-57 radars over land are described. Data were utilized from a total of 25 SMMR passes and 234 radars, resulting in about 12,000 observations of about 1600 sq/km areas. Multiple correlation coefficients of 0.63, 0.80 and 0.75 are achieved for the spring, summer and fall algorithms, respectively. Most of this information is in the form of multifrequency contrast in brightness temperature, which is interpreted as a measurement of the degree to which the land-emitted radiation is attenuated by the rain systems. The SMMR 37-GHz channel has more information on rain rate than any other channel. By combining the lower frequency channels with the 37-GHz observations, variations in land and precipitation thermometric temperatures can be removed, leaving rain attenuation as the major effect on brightness temperature. Polarization screening at 37 GHz is found to be sufficient to screen out cases of wet ground, which is only important when the ground is relatively vegetation free. Heavy rain cases are found to be significant part of the algorithms' success, because of the strong microwve signatures (low-brightness temperatures) that result from the presence of precipitation-sized ice in the upper portions of heavily precipitating storms. If IR data are combined with the summer microwave data, an improved (0.85) correlation with radar rain rates is achieved.

Coupling the snow thermodynamic model SNOWPACK with the microwave emission model of layered snowpacks for subarctic and arctic snow water equivalent retrievals

NASA Astrophysics Data System (ADS)

Langlois, A.; Royer, A.; Derksen, C.; Montpetit, B.; Dupont, F.; GoïTa, K.

2012-12-01

Satellite-passive microwave remote sensing has been extensively used to estimate snow water equivalent (SWE) in northern regions. Although passive microwave sensors operate independent of solar illumination and the lower frequencies are independent of atmospheric conditions, the coarse spatial resolution introduces uncertainties to SWE retrievals due to the surface heterogeneity within individual pixels. In this article, we investigate the coupling of a thermodynamic multilayered snow model with a passive microwave emission model. Results show that the snow model itself provides poor SWE simulations when compared to field measurements from two major field campaigns. Coupling the snow and microwave emission models with successive iterations to correct the influence of snow grain size and density significantly improves SWE simulations. This method was further validated using an additional independent data set, which also showed significant improvement using the two-step iteration method compared to standalone simulations with the snow model.

Passive Microwave Soil Moisture Retrieval through Combined Radar/Radiometer Ground Based Simulator with Special Reference to Dielectric Schemes

NASA Astrophysics Data System (ADS)

Srivastava, Prashant K., ,, Dr.; O'Neill, Peggy, ,, Dr.

2014-05-01

Soil moisture is an important element for weather and climate prediction, hydrological sciences, and applications. Hence, measurements of this hydrologic variable are required to improve our understanding of hydrological processes, ecosystem functions, and the linkages between the Earth's water, energy, and carbon cycles (Srivastava et al. 2013). The retrieval of soil moisture depends not only on parameterizations in the retrieval algorithm but also on the soil dielectric mixing models used (Behari 2005). Although a number of soil dielectric mixing models have been developed, testing these models for soil moisture retrieval has still not been fully explored, especially with SMAP-like simulators. The main objective of this work focuses on testing different dielectric models for soil moisture retrieval using the Combined Radar/Radiometer (ComRAD) ground-based L-band simulator developed jointly by NASA/GSFC and George Washington University (O'Neill et al., 2006). The ComRAD system was deployed during a field experiment in 2012 in order to provide long active/passive measurements of two crops under controlled conditions during an entire growing season. L-band passive data were acquired at a look angle of 40 degree from nadir at both horizontal & vertical polarization. Currently, there are many dielectric models available for soil moisture retrieval; however, four dielectric models (Mironov, Dobson, Wang & Schmugge and Hallikainen) were tested here and found to be promising for soil moisture retrieval (some with higher performances). All the above-mentioned dielectric models were integrated with Single Channel Algorithms using H (SCA-H) and V (SCA-V) polarizations for the soil moisture retrievals. All the ground-based observations were collected from test site-United States Department of Agriculture (USDA) OPE3, located a few miles away from NASA GSFC. Ground truth data were collected using a theta probe and in situ sensors which were then used for validation. Analysis indicated a higher performance in terms of soil moisture retrieval accuracy for the Mironov dielectric model (RMSE of 0.035 m3/m3), followed by Dobson, Wang & Schmugge, and Hallikainen. This analysis indicates that Mironov dielectric model is promising for passive-only microwave soil moisture retrieval and could be a useful choice for SMAP satellite soil moisture retrieval. Keywords: Dielectric models; Single Channel Algorithm, Combined Radar/Radiometer, Soil moisture; L band References: Behari, J. (2005). Dielectric Behavior of Soil (pp. 22-40). Springer Netherlands O'Neill, P. E., Lang, R. H., Kurum, M., Utku, C., & Carver, K. R. (2006), Multi-Sensor Microwave Soil Moisture Remote Sensing: NASA's Combined Radar/Radiometer (ComRAD) System. In IEEE MicroRad, 2006 (pp. 50-54). IEEE. Srivastava, P. K., Han, D., Rico Ramirez, M. A., & Islam, T. (2013), Appraisal of SMOS soil moisture at a catchment scale in a temperate maritime climate. Journal of Hydrology, 498, 292-304. USDA OPE3 web site at http://www.ars.usda.gov/Research/.

Satellite Retrieval of Atmospheric Water Budget over Gulf of Mexico- Caribbean Basin: Seasonal Variability

NASA Technical Reports Server (NTRS)

Smith, Eric A.; Santos, Pablo; Einaudi, Franco (Technical Monitor)

2001-01-01

This study presents results from a multi-satellite/multi-sensor retrieval system designed to obtain the atmospheric water budget over the open ocean. A combination of hourly-sampled monthly datasets derived from the GOES-8 5 Imager and the DMSP 7-channel passive microwave radiometer (SSM/I) have been acquired for the Gulf of Mexico-Caribbean Sea basin. Whereas the methodology is being tested over this basin, the retrieval system is designed for portability to any open-ocean region. Algorithm modules using the different datasets to retrieve individual geophysical parameters needed in the water budget equation are designed in a manner that takes advantage of the high temporal resolution of the GOES-8 measurements, as well as the physical relationships inherent to the SSM/I passive microwave signals in conjunction with water vapor, cloud liquid water, and rainfall. The methodology consists of retrieving the precipitation, surface evaporation, and vapor-cloud water storage terms in the atmospheric water balance equation from satellite techniques, with the water vapor advection term being obtained as the residue needed for balance. Thus, we have sought to develop a purely satellite-based method for obtaining the full set of terms in the atmospheric water budget equation without requiring in situ sounding information on the wind profile. The algorithm is partly validated by first cross-checking all the algorithm components through multiple-algorithm retrieval intercomparisons. More fundamental validation is obtained by directly comparing water vapor transports into the targeted basin diagnosed from the satellite algorithm to those obtained observationally from a network of land-based upper air stations that nearly uniformly surround the basin. Total columnar atmospheric water budget results will be presented for an extended annual cycle consisting of the months of October-97, January-98, April-98, July-98, October-98, and January-1999. These results are used to emphasize the changing relationship in E-P, as well as in the varying roles of storage and advection in balancing E-P both on daily and monthly time scales and on localized and basin space scales. Results from the algorithm-to-algorithm intercomparisons will also be presented in the context of sensitivity testing to help understand the intrinsic uncertainties in the water budget terms.

Seasonal Parameterizations of the Tau-Omega Model Using the ComRAD Ground-Based SMAP Simulator

NASA Technical Reports Server (NTRS)

O'Neill, P.; Joseph, A.; Srivastava, P.; Cosh, M.; Lang, R.

2014-01-01

NASA's Soil Moisture Active Passive (SMAP) mission is scheduled for launch in November 2014. In the prelaunch time frame, the SMAP team has focused on improving retrieval algorithms for the various SMAP baseline data products. The SMAP passive-only soil moisture product depends on accurate parameterization of the tau-omega model to achieve the required accuracy in soil moisture retrieval. During a field experiment (APEX12) conducted in the summer of 2012 under dry conditions in Maryland, the Combined Radar/Radiometer (ComRAD) truck-based SMAP simulator collected active/passive microwave time series data at the SMAP incident angle of 40 degrees over corn and soybeans throughout the crop growth cycle. A similar experiment was conducted only over corn in 2002 under normal moist conditions. Data from these two experiments will be analyzed and compared to evaluate how changes in vegetation conditions throughout the growing season in both a drought and normal year can affect parameterizations in the tau-omega model for more accurate soil moisture retrieval.

Comparison of DMSP SSM/I and Landsat 7 ETM+ Sea Ice Concentrations During Summer Melt

NASA Technical Reports Server (NTRS)

Cavalieri, Donald J.; Markus, Thorsten; Ivanoff, Alvaro; Koblinsky, Chester J. (Technical Monitor)

2001-01-01

As part of NASA's EOS Aqua sea ice validation program for the Advanced Microwave Scanning Radiometer (AMSR-E), Landsat 7 Enhanced Thematic Mapper (ETM+) images were acquired to develop a sea ice concentration data set with which to validate AMSR-E sea ice concentration retrievals. The standard AMSR-E Arctic sea ice concentration product will be obtained with the enhanced NASA Team (NT2) algorithm. The goal of this study is to assess the accuracy to which the NT2 algorithm, using DMSP Special Sensor Microwave Imager radiances, retrieves sea ice concentrations under summer melt conditions. Melt ponds are currently the largest source of error in the determination of Arctic sea ice concentrations with satellite passive microwave sensors. To accomplish this goal, Landsat 7 ETM+ images of Baffin Bay were acquired under clear sky conditions on the 26th and 27th of June 2000 and used to generate high-resolution sea ice concentration maps with which to compare the NT2 retrievals. Based on a linear regression analysis of 116 25-km samples, we find that overall the NT2 retrievals agree well with the Landsat concentrations. The regression analysis yields a correlation coefficient of 0.98. In areas of high melt ponding, the NT2 retrievals underestimate the sea ice concentrations by about 12% compared to the Landsat values.

Top/bottom multisensor remote sensing of Arctic sea ice

NASA Technical Reports Server (NTRS)

Comiso, J. C.; Wadhams, P.; Krabill, W. B.; Swift, R. N.; Crawford, J. P.

1991-01-01

Results are presented on the Aircraft/Submarine Sea Ice Project experiment carried out in May 1987 to investigate concurrently the top and the bottom features of the Arctic sea-ice cover. Data were collected nearly simultaneously by instruments aboard two aircraft and a submarine, which included passive and active (SAR) microwave sensors, upward looking and sidescan sonars, a lidar profilometer, and an IR sensor. The results described fall into two classes of correlations: (1) quantitative correlations between profiles, such as ice draft (sonar), ice elevation (laser), SAR backscatter along the track line, and passive microwave brightness temperatures; and (2) qualitative and semiquantitative correlations between corresponding areas of imagery (i.e., passive microwave, AR, and sidescan sonar).

Advanced Passive Microwave Radiometer Technology for GPM Mission

NASA Technical Reports Server (NTRS)

Smith, Eric A.; Im, Eastwood; Kummerow, Christian; Principe, Caleb; Ruf, Christoper; Wilheit, Thomas; Starr, David (Technical Monitor)

2002-01-01

An interferometer-type passive microwave radiometer based on MMIC receiver technology and a thinned array antenna design is being developed under the Instrument Incubator Program (TIP) on a project entitled the Lightweight Rainfall Radiometer (LRR). The prototype single channel aircraft instrument will be ready for first testing in 2nd quarter 2003, for deployment on the NASA DC-8 aircraft and in a ground configuration manner; this version measures at 10.7 GHz in a crosstrack imaging mode. The design for a two (2) frequency preliminary space flight model at 19 and 35 GHz (also in crosstrack imaging mode) has also been completed, in which the design features would enable it to fly in a bore-sighted configuration with a new dual-frequency space radar (DPR) under development at the Communications Research Laboratory (CRL) in Tokyo, Japan. The DPR will be flown as one of two primary instruments on the Global Precipitation Measurement (GPM) mission's core satellite in the 2007 time frame. The dual frequency space flight design of the ERR matches the APR frequencies and will be proposed as an ancillary instrument on the GPM core satellite to advance space-based precipitation measurement by enabling better microphysical characterization and coincident volume data gathering for exercising combined algorithm techniques which make use of both radar backscatter and radiometer attenuation information to constrain rainrate solutions within a physical algorithm context. This talk will discuss the design features, performance capabilities, applications plans, and conical/polarametric imaging possibilities for the LRR, as well as a brief summary of the project status and schedule.

Towards a Multisensor Approach to Improve on Current TRMM Retrievals of Clouds and Precipitation

NASA Technical Reports Server (NTRS)

Stephens, Graeme L.; LEcuyer, Tristan S.; Austin, Richard T.

2002-01-01

The Tropical Rainfall Measuring Mission (TRMM) was designed to measure tropical rainfall and its variation from a low inclination orbiting satellite. The TRMM payload was carefully chosen to overcome a number of limitations of past satellite observing systems. This payload is predicated on the combination of active and passive observations from the TRMM Precipitation Radar (PR) and TRMM Microwave Imager (TMI) and Visible and Infrared Scanner (VIRS). Our research over the past three years has been devoted to the challenge of developing the most effective way of combining complementary information from these sensors to provide the most consistent estimate of precipitation. We have approached this problem from three directions. The first was to carry out preliminary analysis of passive microwave and infrared data from the TMI and VIRS instruments to understand the character of clear and cloudy skies in the basis defined by polarization and brightness temperature differences. Using this information as a foundation, the properties of two retrieval algorithms were analyzed, one for retrieving ice clouds from VIRS that was developed in parallel with this project and the other for rainfall from the TMI. Finally, the knowledge gleaned from each of these studies, coupled with ancillary data from NWP models and a broadband radiative transfer model, was used to create and algorithm for synthesizing the principal components of the Earth's energy budget from the basic building blocks of the atmosphere, gases, clouds, and precipitation. Principal results from each of these areas of research and their role in the TRMM and climate communities are summarized.

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. TMI rain depths are significantly higher than corresponding rain depths from the PR in the ITCZ, but are similar in magnitude elsewhere.

Resonator reset in circuit QED by optimal control for large open quantum systems

NASA Astrophysics Data System (ADS)

Boutin, Samuel; Andersen, Christian Kraglund; Venkatraman, Jayameenakshi; Ferris, Andrew J.; Blais, Alexandre

2017-10-01

We study an implementation of the open GRAPE (gradient ascent pulse engineering) algorithm well suited for large open quantum systems. While typical implementations of optimal control algorithms for open quantum systems rely on explicit matrix exponential calculations, our implementation avoids these operations, leading to a polynomial speedup of the open GRAPE algorithm in cases of interest. This speedup, as well as the reduced memory requirements of our implementation, are illustrated by comparison to a standard implementation of open GRAPE. As a practical example, we apply this open-system optimization method to active reset of a readout resonator in circuit QED. In this problem, the shape of a microwave pulse is optimized such as to empty the cavity from measurement photons as fast as possible. Using our open GRAPE implementation, we obtain pulse shapes, leading to a reset time over 4 times faster than passive reset.

An intercomparison of available soil moisture estimates from thermal-infrared and passive microwave remote sensing and land-surface modeling

USDA-ARS?s Scientific Manuscript database

Remotely-sensed soil moisture studies have mainly focused on retrievals using active and passive microwave (MW) sensors whose measurements provided a direct relationship to soil moisture (SM). MW sensors present obvious advantages such as the ability to retrieve through non-precipitating cloud cover...

Frequency band justifications for passive sensors 10.0 to 385 GHz, chapter 2. [for monitoring earth resources and the environment

NASA Technical Reports Server (NTRS)

1976-01-01

Sensitivity requirements of the various measurements obtained by microwave sensors, and radiometry techniques are described. Analytical techniques applied to detailed sharing analyses are discussed. A bibliography of publications pertinent to the scientific justification of frequency requirements for passive microwave remote sensing is included.

Assimilation of active and passive microwave observations for improved estimates of soil moisture and crop growth

USDA-ARS?s Scientific Manuscript database

An Ensemble Kalman Filter-based data assimilation framework that links a crop growth model with active and passive (AP) microwave models was developed to improve estimates of soil moisture (SM) and vegetation biomass over a growing season of soybean. Complementarities in AP observations were incorpo...

An assessment of the differences between spatial resolution and grid size for the SMAP enhanced soil moisture product over homogeneous sites

USDA-ARS?s Scientific Manuscript database

Satellite-based passive microwave remote sensing typically involves a scanning antenna that makes measurements at irregularly spaced locations. These locations can change on a day to day basis. Soil moisture products derived from satellite-based passive microwave remote sensing are usually resampled...

Spatial and Temporal Variations of Surface Characteristics on the Greenland Ice Sheet as Derived from Passive Microwave Observations

NASA Technical Reports Server (NTRS)

Anderson, Mark; Rowe, Clinton; Kuivinen, Karl; Mote, Thomas

1996-01-01

The primary goals of this research were to identify and begin to comprehend the spatial and temporal variations in surface characteristics of the Greenland ice sheet using passive microwave observations, physically-based models of the snowpack and field observations of snowpack and firn properties.

Use of GOES, SSM/I, TRMM Satellite Measurements Estimating Water Budget Variations in Gulf of Mexico - Caribbean Sea Basins

NASA Technical Reports Server (NTRS)

Smith, Eric A.

2004-01-01

This study presents results from a multi-satellite/multi-sensor retrieval system designed to obtain the atmospheric water budget over the open ocean. A combination of 3ourly-sampled monthly datasets derived from the GOES-8 5-channel Imager, the TRMM TMI radiometer, and the DMSP 7-channel passive microwave radiometers (SSM/I) have been acquired for the combined Gulf of Mexico-Caribbean Sea basin. Whereas the methodology has been tested over this basin, the retrieval system is designed for portability to any open-ocean region. Algorithm modules using the different datasets to retrieve individual geophysical parameters needed in the water budget equation are designed in a manner that takes advantage of the high temporal resolution of the GOES-8 measurements, as well as the physical relationships inherent to the TRMM and SSM/I passive microwave measurements in conjunction with water vapor, cloud liquid water, and rainfall. The methodology consists of retrieving the precipitation, surface evaporation, and vapor-cloud water storage terms in the atmospheric water balance equation from satellite techniques, with the water vapor advection term being obtained as the residue needed for balance. Thus, the intent is to develop a purely satellite-based method for obtaining the full set of terms in the atmospheric water budget equation without requiring in situ sounding information on the wind profile. The algorithm is validated by cross-checking all the algorithm components through multiple- algorithm retrieval intercomparisons. A further check on the validation is obtained by directly comparing water vapor transports into the targeted basin diagnosed from the satellite algorithms to those obtained observationally from a network of land-based upper air stations that nearly uniformly surround the basin, although it is fair to say that these checks are more effective m identifying problems in estimating vapor transports from a leaky operational radiosonde network than in verifying the transport estimates determined from the satellite algorithm system Total columnar atmospheric water budget results are presented for an extended annual cycle consisting of the months of October-97, January-98, April-98, July-98,October-98, and January 1999. These results are used to emphasize the changing relationship in E-P, as well as in the varying roles of storage and advection in balancing E-P both on daily and monthly time scales and on localized and basin space scales. Results from the algorithm-to-algorithm intercomparisons are also presented in the context of sensitivity testing to help understand the intrinsic uncertainties in evaluating the water budget terms by an all-satellite algorithm approach.

Monthly-Diurnal Water Budget Variability Over Gulf of Mexico-Caribbean Sea Basin from Satellite Observations

NASA Technical Reports Server (NTRS)

Smith, E. A.; Santos, P.

2006-01-01

This study presents results from a multi-satellite/multi-sensor retrieval system design d to obtain the atmospheric water budget over the open ocean. A combination of hourly-sampled monthly datasets derived from the GOES-8 5-channel Imager, the TRMM TMI radiometer, and the DMSP 7-channel passive microwave radiometers (SSM/I) have been acquired for the combined Gulf of Mexico-Caribbean Sea basin. Whereas the methodology has been tested over this basin, the retrieval system is designed for portability to any open-ocean region. Algorithm modules using the different datasets to retrieve individual geophysical parameters needed in the water budget equation are designed in a manner that takes advantage of the high temporal resolution of the GOES-8 measurements, as well as the physical relationships inherent to the TRMM and SSM/I passive microwave measurements in conjunction with water vapor, cloud liquid water, and rainfall. The methodology consists of retrieving the precipitation, surface evaporation, and vapor-cloud water storage terms in the atmospheric water balance equation from satellite techniques, with the water vapor advection term being obtained as the residue needed for balance. Thus, the intent is to develop a purely satellite-based method for obtaining the full set of terms in the atmospheric water budget equation without requiring in situ sounding information on the wind profile. The algorithm is validated by cross-checking all the algorithm components through multiple-algorithm retrieval intercomparisons. A further check on the validation is obtained by directly comparing water vapor transports into the targeted basin diagnosed from the satellite algorithms to those obtained observationally from a network of land-based upper air stations that nearly uniformly surround the basin, although it is fair to say that these checks are more effective in identifying problems in estimating vapor transports from a "leaky" operational radiosonde network than in verifying the transport estimates determined from the satellite algorithm system. Total columnar atmospheric water budget results are presented for an extended annual cycle consisting of the months of October-97, January-98, April-98, July-98,October-98, and January- 1999. These results are used to emphasize the changing relationship in E-P, as well as in the varying roles of storage and advection in balancing E-P both on daily and monthly time scales and on localized and basin space scales. Results from the algorithm-to-algorithm intercomparisons are also presented in the context of sensitivity testing to help understand the intrinsic uncertainties in evaluating the water budget terms by an all-satellite algorithm approach.

Radiative transfer to space through a precipitating cloud at multiple microwave frequencies. I - Model description. II - Results and analysis

NASA Technical Reports Server (NTRS)

Mugnai, Alberto; Smith, Eric A.

1988-01-01

The impact of time-dependent cloud microphysical structure on the transfer to space of passive microwave radiation is studied at several frequencies across the EHF and lower SHF portions of the microwave spectrum. The feasibility of using multichannel passive-microwave retrieval techniques to estimate precipitation from space-based platforms is examined. The model is described, and the results are assessed in conjunction with a Nimbus-7 SMMR case study of precipitation in an intense tropical Pacific storm. It is concluded that the effects of cloud liquid water content must be considered to obtain a realistic estimation and distribution of rainrates.

The 4-8 GHz Microwave Active and Passive Spectrometer (MAPS). Volume 1: Radar section

NASA Technical Reports Server (NTRS)

Ulaby, F. T.

1973-01-01

The performance characteristics of the radar section of the prototype 4-8 GHz Microwave Active and Passive Spectrometer system are reported. Active and passive spectral responses were measured of natural, cultivated, and human-made surfaces over the 4-18 GHz region of frequencies for look angles between zero and 70 degrees and for all possible linear polarization combinations. Soil and plant samples were collected to measure their dielectric properties and moisture content. The FORTRAN program for area calculation is provided.

Variational Assimilation of Global Microwave Rainfall Retrievals: Physical and Dynamical Impact on GEOS Analyses and Forecasts

NASA Technical Reports Server (NTRS)

Lin, Xin; Zhang, Sara Q.; Hou, Arthur Y.

2006-01-01

Global microwave rainfall retrievals from a 5-satellite constellation, including TMI from TRMM, SSWI from DMSP F13, F14 and F15, and AMSR-E from EOS-AQUA, are assimilated into the NASA Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) using a 1-D variational continuous assimilation (VCA) algorithm. The physical and dynamical impact of rainfall assimilation on GEOS analyses and forecasts is examined at various temporal and spatial scales. This study demonstrates that the 1-D VCA algorithm, which was originally developed and evaluated for rainfall assimilations over tropical oceans, can effectively assimilate satellite microwave rainfall retrievals and improve GEOS analyses over both the Tropics and the extratropics where the atmospheric processes are dominated by different large-scale dynamics and moist physics, and also over the land, where rainfall estimates from passive microwave radiometers are believed to be less accurate. Results show that rainfall assimilation renders the GEOS analysis physically and dynamically more consistent with the observed precipitation at the monthly-mean and 6-hour time scales. Over regions where the model precipitation tends to misbehave in distinctly different rainy regimes, the 1-D VCA algorithm, by compensating for errors in the model s moist time-tendency in a 6-h analysis window, is able to bring the rainfall analysis closer to the observed. The radiation and cloud fields also tend to be in better agreement with independent satellite observations in the rainfall-assimilation m especially over regions where rainfall analyses indicate large improvements. Assimilation experiments with and without rainfall data for a midlatitude frontal system clearly indicates that the GEOS analysis is improved through changes in the thermodynamic and dynamic fields that respond to the rainfall assimilation. The synoptic structures of temperature, moisture, winds, divergence, and vertical motion, as well as vorticity are more realistically captured across the front. Short-term forecasts using initial conditions assimilated with rainfall data also show slight improvements. 1

Hurricane Imaging Radiometer Wind Speed and Rain Rate Retrievals during the 2010 GRIP Flight Experiment

NASA Technical Reports Server (NTRS)

Sahawneh, Saleem; Farrar, Spencer; Johnson, James; Jones, W. Linwood; Roberts, Jason; Biswas, Sayak; Cecil, Daniel

2014-01-01

Microwave remote sensing observations of hurricanes, from NOAA and USAF hurricane surveillance aircraft, provide vital data for hurricane research and operations, for forecasting the intensity and track of tropical storms. The current operational standard for hurricane wind speed and rain rate measurements is the Stepped Frequency Microwave Radiometer (SFMR), which is a nadir viewing passive microwave airborne remote sensor. The Hurricane Imaging Radiometer, HIRAD, will extend the nadir viewing SFMR capability to provide wide swath images of wind speed and rain rate, while flying on a high altitude aircraft. HIRAD was first flown in the Genesis and Rapid Intensification Processes, GRIP, NASA hurricane field experiment in 2010. This paper reports on geophysical retrieval results and provides hurricane images from GRIP flights. An overview of the HIRAD instrument and the radiative transfer theory based, wind speed/rain rate retrieval algorithm is included. Results are presented for hurricane wind speed and rain rate for Earl and Karl, with comparison to collocated SFMR retrievals and WP3D Fuselage Radar images for validation purposes.

A quasi-global approach to improve day-time satellite surface soil moisture anomalies through land surface temperature input

USDA-ARS?s Scientific Manuscript database

Passive microwave observations from various space borne sensors have been linked to soil moisture of the Earth’s surface layer. The new generation passive microwave sensors are dedicated to retrieving this variable and make observations in the single, theoretically optimal L-band frequency (1-2 GHz)...

A Prototype MODI- SSM/I Snow Mapping Algorithm

NASA Technical Reports Server (NTRS)

Tait, Andrew B.; Barton, Jonathan S.; Hall, Dorothy K.

1999-01-01

Data in the wavelength range 0.545 - 1.652 microns from the Moderate Resolution Imaging Spectroradiometer (MODIS), to be launched aboard the Earth Observing System (EOS) Terra in the fall of 1999, will be used to map daily global snow cover at 500m resolution. However, during darkness, or when the satellite's view of the surface is obscured by cloud, snow cover cannot be mapped using MODIS data. We show that during these conditions, it is possible to supplement the MODIS product by mapping the snow cover using passive microwave data from the Special Sensor Microwave Imager (SSM/I), albeit with much poorer resolution. For a 7-day time period in March 1999, a prototype MODIS snow-cover product was compared with a prototype MODIS-SSM/I product for the same area in the mid-western United States. The combined MODIS-SSM/I product mapped 9% more snow cover than the MODIS-only product.

MAPIR: An Airborne Polarmetric Imaging Radiometer in Support of Hydrologic Satellite Observations

NASA Technical Reports Server (NTRS)

Laymon, C.; Al-Hamdan, M.; Crosson, W.; Limaye, A.; McCracken, J.; Meyer, P.; Richeson, J.; Sims, W.; Srinivasan, K.; Varnevas, K.

2010-01-01

In this age of dwindling water resources and increasing demands, accurate estimation of water balance components at every scale is more critical to end users than ever before. Several near-term Earth science satellite missions are aimed at global hydrologic observations. The Marshall Airborne Polarimetric Imaging Radiometer (MAPIR) is a dual beam, dual angle polarimetric, scanning L band passive microwave radiometer system developed by the Observing Microwave Emissions for Geophysical Applications (OMEGA) team at MSFC to support algorithm development and validation efforts in support of these missions. MAPIR observes naturally-emitted radiation from the ground primarily for remote sensing of land surface brightness temperature from which we can retrieve soil moisture and possibly surface or water temperature and ocean salinity. MAPIR has achieved Technical Readiness Level 6 with flight heritage on two very different aircraft, the NASA P-3B, and a Piper Navajo.

The utilization of Nimbus-7 SMMR measurements to delineate rainfall over land

NASA Technical Reports Server (NTRS)

Rogers, E.; Siddalingaiah, H.

1982-01-01

Based on previous theoretical calculations, an empirical statistical approach to use satellite multifrequency dual polarized passive microwave data to detect rainfall areas over land was initiated. The addition of information from a lower frequency channel (18.0 or 10.7 GHz) was shown to improve the discrimination of rain from wet ground achieved by using a single frequency dual polarized (37 GHz) channel alone. The algorithm was developed and independently tested using data from the Nimbus-7 Scanning Multichannel Microwave Radiometer. Horizontally and vertically polarized brightness temperature pairs at 37, 18, 10.7 GHz were sampled for raining areas over land (determined from ground base radar), wet ground areas (adjacent and upwind from rain areas determined from radar), and dry land regions (areas where rain had not fallen in a 24h period) over the central and eastern United States. Surface thermodynamic temperatures were both above and below 15 deg C.

International Symposium on Remote Sensing of Environment, 9th, University of Michigan, Ann Arbor, Mich., April 15-19, 1974, Proceedings. Volumes 1, 2 & 3

NASA Technical Reports Server (NTRS)

1974-01-01

The present work gathers together numerous papers describing the use of remote sensing technology for mapping, monitoring, and management of earth resources and man's environment. Studies using various types of sensing equipment are described, including multispectral scanners, radar imagery, spectrometers, lidar, and aerial photography, and both manual and computer-aided data processing techniques are described. Some of the topics covered include: estimation of population density in Tokyo districts from ERTS-1 data, a clustering algorithm for unsupervised crop classification, passive microwave sensing of moist soils, interactive computer processing for land use planning, the use of remote sensing to delineate floodplains, moisture detection from Skylab, scanning thermal plumes, electrically scanning microwave radiometers, oil slick detection by X-band synthetic aperture radar, and the use of space photos for search of oil and gas fields. Individual items are announced in this issue.

On the characterization of subpixel effects for passive microwave remote sensing of snow in montane environments

NASA Astrophysics Data System (ADS)

Vander Jagt, Benjamin John

Snow and its water equivalent plays a vital role in global water and energy balances, with particular relevance in mountainous areas with arid and semi-arid climate regimes. Spaceborne passive microwave (PM) remote sensing measurements are attractive for snowpack characterization due to their continuous global coverage and historical record; over 30 years of research has been invested in the development of methods to characterize large-scale snow water resources from PM-based measurements. Historically, use of PM data for snowpack characterization in montane enviroments has been obstructed by the complex subpixel variability of snow properties within the PM measurement footprint. The main subpixel effects can be grouped as: the effect of snow microstructure (e.g. snow grain size) and stratigraphy on snow microwave emission, vegetation attenuation of PM measurements, and the sensitivity PM brightness temperature (Tb) observation to the variability of different subpixel properties at spaceborne measurement scales. This dissertation is focused on a systematic examination of these issues, which thus far have prevented the widespread integration of snow water equivalent (SWE) retrieval methods. It is meant to further our comprehension of the underlying processes at work in these rugged, remote, a hydrologically important areas. The role that snow microstructure plays in the PM retrievals of SWE is examined first. Traditional estimates of grain size are subjective and prone to error. Objective techniques to characterize grain size are described and implemented, including near infrared (NIR), stereology, and autocorrelation based approaches. Results from an intensive Colorado field study in which independent estimates of grain size and their modeled brightness temperature (Tb) emission are evaluated against PM Tb observations are included. The coarse resolution of the passive microwave measurements provides additional challenges when trying to resolve snow states via remote sensing observations. The natural heterogeneity of snowpack (e.g. depth, stratigraphy, etc) and vegetative states within the PM footprint occurs at spatial scales smaller than PM observation scales. The sensitivity to changes in snow depth given sub-pixel variability in snow and vegetation is explored and quantified using the comprehensive dataset acquired during the Cold Land Processes experiment (CLPX). Lastly, vegetation has long been an obstacle in efforts to derive snow depth and mass estimates from passive microwave (PM) measurements of brightness temperature (Tb). We introduce a vegetation transmissivity model that is derived entirely from multi-scale and multi-temporal PM Tb observations and a globally available vegetation dataset, specifically the Leaf Area Index (LAI). This newly constructed model characterizes the attenuation of PM Tb observations at frequencies typically employed for snow retrieval algorithms, as a function of LAI. Additionally, the model is used to predict how much SWE is observable within the major river basins of Colorado and the central Rockies.

Passive microwave remote sensing of rainfall with SSM/I: Algorithm development and implementation

NASA Technical Reports Server (NTRS)

Ferriday, James G.; Avery, Susan K.

1994-01-01

A physically based algorithm sensitive to emission and scattering is used to estimate rainfall using the Special Sensor Microwave/Imager (SSM/I). The algorithm is derived from radiative transfer calculations through an atmospheric cloud model specifying vertical distributions of ice and liquid hydrometeors as a function of rain rate. The algorithm is structured in two parts: SSM/I brightness temperatures are screened to detect rainfall and are then used in rain-rate calculation. The screening process distinguishes between nonraining background conditions and emission and scattering associated with hydrometeors. Thermometric temperature and polarization thresholds determined from the radiative transfer calculations are used to detect rain, whereas the rain-rate calculation is based on a linear function fit to a linear combination of channels. Separate calculations for ocean and land account for different background conditions. The rain-rate calculation is constructed to respond to both emission and scattering, reduce extraneous atmospheric and surface effects, and to correct for beam filling. The resulting SSM/I rain-rate estimates are compared to three precipitation radars as well as to a dynamically simulated rainfall event. Global estimates from the SSM/I algorithm are also compared to continental and shipboard measurements over a 4-month period. The algorithm is found to accurately describe both localized instantaneous rainfall events and global monthly patterns over both land and ovean. Over land the 4-month mean difference between SSM/I and the Global Precipitation Climatology Center continental rain gauge database is less than 10%. Over the ocean, the mean difference between SSM/I and the Legates and Willmott global shipboard rain gauge climatology is less than 20%.

Microwave Remote Sensing Modeling of Ocean Surface Salinity and Winds Using an Empirical Sea Surface Spectrum

NASA Technical Reports Server (NTRS)

Yueh, Simon H.

2004-01-01

Active and passive microwave remote sensing techniques have been investigated for the remote sensing of ocean surface wind and salinity. We revised an ocean surface spectrum using the CMOD-5 geophysical model function (GMF) for the European Remote Sensing (ERS) C-band scatterometer and the Ku-band GMF for the NASA SeaWinds scatterometer. The predictions of microwave brightness temperatures from this model agree well with satellite, aircraft and tower-based microwave radiometer data. This suggests that the impact of surface roughness on microwave brightness temperatures and radar scattering coefficients of sea surfaces can be consistently characterized by a roughness spectrum, providing physical basis for using combined active and passive remote sensing techniques for ocean surface wind and salinity remote sensing.

A multifrequency evaluation of active and passive microwave sensors for oil spill detection and assessment

NASA Technical Reports Server (NTRS)

Fenner, R. G.; Reid, S. C.; Solie, C. H.

1980-01-01

An evaluation is given of how active and passive microwave sensors can best be used in oil spill detection and assessment. Radar backscatter curves taken over oil spills are presented and their effect on synthetic aperture radar (SAR) imagery are discussed. Plots of microwave radiometric brightness variations over oil spills are presented and discussed. Recommendations as to how to select the best combination of frequency, viewing angle, and sensor type for evaluation of various aspects of oil spills are also discussed.

The GPM Common Calibrated Brightness Temperature Product

NASA Technical Reports Server (NTRS)

Stout, John; Berg, Wesley; Huffman, George; Kummerow, Chris; Stocker, Erich

2005-01-01

The Global Precipitation Measurement (GPM) project will provide a core satellite carrying the GPM Microwave Imager (GMI) and will use microwave observations from a constellation of other satellites. Each partner with a satellite in the constellation will have a calibration that meets their own requirements and will decide on the format to archive their brightness temperature (Tb) record in GPM. However, GPM multi-sensor precipitation algorithms need to input intercalibrated Tb's in order to avoid differences among sensors introducing artifacts into the longer term climate record of precipitation. The GPM Common Calibrated Brightness Temperature Product is intended to address this problem by providing intercalibrated Tb data, called "Tc" data, where the "c" stands for common. The precipitation algorithms require a Tc file format that is both generic and flexible enough to accommodate the different passive microwave instruments. The format will provide detailed information on the processing history in order to allow future researchers to have a record of what was done. The format will be simple, including the main items of scan time, latitude, longitude, and Tc. It will also provide spacecraft orientation, spacecraft location, orbit, and instrument scan type (cross-track or conical). Another simplification is to store data in real numbers, avoiding the ambiguity of scaled data. Finally, units and descriptions will be provided in the product. The format is built on the concept of a swath, which is a series of scans that have common geolocation and common scan geometry. Scan geometry includes pixels per scan, sensor orientation, scan type, and incidence angles. The Tc algorithm and data format are being tested using the pre-GPM Precipitation Processing System (PPS) software to generate formats and 1/0 routines. In the test, data from SSM/I, TMI, AMSR-E, and WindSat are being processed and written as Tc products.

Convective climatology over the southwest U.S. and Mexico from passive microwave and infrared data

NASA Technical Reports Server (NTRS)

Negri, Andrew J.; Howard, Kenneth W.; Keehn, Peter R.; Maddox, Robert A.; Adler, Robert F.

1992-01-01

Passive microwave data from the Special Sensor Microwave Imager (SSM/I) were used to estimate the amount of rainfall in the June-August season for the regions of the southwest U.S. and Mexico, and the results are compared to rain-gauge observations and to IR climatologies of Maddox et al. (1992), using both the hourly IR data and IR data sampled at the time of the overpass of the SSM/I. A comparison of the microwave climatology with monthly rainfall measured by the climatological gage network over several states of western Mexico resulted in a 0.63 correlation and a large (482 mm) bias, due to sampling and the incongruity of rain gages and satellite estimates. A comparison between the IR and microwave data showed that the IR tended toward higher percentages along the coast compared to the microwave.

Estimation of Snow Parameters Based on Passive Microwave Remote Sensing and Meteorological Information

NASA Technical Reports Server (NTRS)

Tsang, Leung; Hwang, Jenq-Neng

1996-01-01

A method to incorporate passive microwave remote sensing measurements within a spatially distributed snow hydrology model to provide estimates of the spatial distribution of Snow Water Equivalent (SWE) as a function of time is implemented. The passive microwave remote sensing measurements are at 25 km resolution. However, in mountain regions the spatial variability of SWE over a 25 km footprint is large due to topographic influences. On the other hand, the snow hydrology model has built-in topographic information and the capability to estimate SWE at a 1 km resolution. In our work, the snow hydrology SWE estimates are updated and corrected using SSM/I passive microwave remote sensing measurements. The method is applied to the Upper Rio Grande River Basin in the mountains of Colorado. The change in prediction of SWE from hydrology modeling with and without updating is compared with measurements from two SNOTEL sites in and near the basin. The results indicate that the method incorporating the remote sensing measurements into the hydrology model is able to more closely estimate the temporal evolution of the measured values of SWE as a function of time.

Detecting ice lenses and melt-refreeze crusts using satellite passive microwaves (Invited)

NASA Astrophysics Data System (ADS)

Montpetit, B.; Royer, A.; Roy, A.

2013-12-01

With recent winter climate warming in high latitude regions, rain-on-snow and melt-refreeze events are more frequent creating ice lenses or ice crusts at the surface or even within the snowpack through drainage. These ice layers create an impermeable ice barrier that reduces vegetation respiration and modifies snow properties due to the weak thermal diffusivity of ice. Winter mean soil temperatures increase due to latent heat being released during the freezing process. When ice layers freeze at the snow-soil interface, they can also affect the feeding habits of the northern wild life. Ice layers also significantly affect satellite passive microwave signals that are widely used to monitor the spatial and temporal evolution of snow. Here we present a method using satellite passive microwave brightness temperatures (Tb) to detect ice lenses and/or ice crusts within a snowpack. First the Microwave Emission Model for Layered Snowpacks (MEMLS) was validated to model Tb at 10.7, 19 and 37 GHz using in situ measurements taken in multiple sub-arctic environments where ice layers where observed. Through validated modeling, the effects of ice layer insertion were studied and an ice layer index was developed using the polarization ratio (PR) at all three frequencies. The developed ice index was then applied to satellite passive microwave signals for reported ice layer events.

Analysis of passive microwave signatures over snow-covered mountainous area

NASA Astrophysics Data System (ADS)

Kim, R. S.; Durand, M. T.

2015-12-01

Accurate knowledge of snow distribution over mountainous area is critical for climate studies and the passive microwave(PM) measurements have been widely used and invested in order to obtain information about snowpack properties. Understanding and analyzing the signatures for the explicit inversion of the remote sensing data from land surfaces is required for successful using of passive microwave sensors but this task is often ambiguous due to the large variability of physical conditions and object types. In this paper, we discuss the pattern of measured brightness temperatures and emissivities at vertical and horizontal polarization over the frequency range of 10.7 to 89 GHz of land surfaces under various snow and vegetation conditions. The Multiband polarimetric Scanning Radiometer(PSR) imagery is used over NASA Cold Land Processes Field Experiment(CLPX) study area with ground-based measurements of snow depth and snow properties. Classification of snow under various conditions in mountainous area is implemented based on different patterns of microwave signatures.

Concurrent remote sensing of Arctic sea ice from submarine and aircraft

NASA Technical Reports Server (NTRS)

Wadhams, P.; Davis, N. R.; Comiso, J. C.; Kutz, R.; Crawford, J.; Jackson, G.; Krabill, W.; Sear, C. B.; Swift, R.; Tucker, W. B., III

1991-01-01

In May 1987 a concurrent remote sensing study of Arctic sea ice from above and below was carried out. A submarine equipped with sidescan and upward looking sonar collaborated with two remote sensing aircraft equipped with passive microwave, synthetic aperture radar (SAR), a laser profilometer and an infrared radiometer. By careful registration of the three tracks it has been possible to find relationships between ice type, ice morphology and thickness, SAR backscatter and microwave brightness temperatures. The key to the process has been the sidescan sonar's ability to identify ice type through differences in characteristic topography. Over a heavily ridged area of mainly multiyear ice there is a strong positive correlation between SAR backscatter and ice draft or elevation. It was also found that passive and active microwave complement each other in that SAR has a high contrast between open water and multiyear ice, while passive microwave has a high contrast between open water and first-year ice.

Combining Satellite Microwave Radiometer and Radar Observations to Estimate Atmospheric Latent Heating Profiles

NASA Technical Reports Server (NTRS)

Grecu, Mircea; Olson, William S.; Shie, Chung-Lin; L'Ecuyer, Tristan S.; Tao, Wei-Kuo

2009-01-01

In this study, satellite passive microwave sensor observations from the TRMM Microwave Imager (TMI) are utilized to make estimates of latent + eddy sensible heating rates (Q1-QR) in regions of precipitation. The TMI heating algorithm (TRAIN) is calibrated, or "trained" using relatively accurate estimates of heating based upon spaceborne Precipitation Radar (PR) observations collocated with the TMI observations over a one-month period. The heating estimation technique is based upon a previously described Bayesian methodology, but with improvements in supporting cloud-resolving model simulations, an adjustment of precipitation echo tops to compensate for model biases, and a separate scaling of convective and stratiform heating components that leads to an approximate balance between estimated vertically-integrated condensation and surface precipitation. Estimates of Q1-QR from TMI compare favorably with the PR training estimates and show only modest sensitivity to the cloud-resolving model simulations of heating used to construct the training data. Moreover, the net condensation in the corresponding annual mean satellite latent heating profile is within a few percent of the annual mean surface precipitation rate over the tropical and subtropical oceans where the algorithm is applied. Comparisons of Q1 produced by combining TMI Q1-QR with independently derived estimates of QR show reasonable agreement with rawinsonde-based analyses of Q1 from two field campaigns, although the satellite estimates exhibit heating profile structure with sharper and more intense heating peaks than the rawinsonde estimates. 2

Addendum to Site Assessment and Feasibility of a New Operations Base on the Greenland Ice Sheet: Addendum to Preliminary Report

DTIC Science & Technology

2015-11-01

National Guard PLR Division of Polar Programs SMM /I Special Sensor Microwave/Imager SMMR Scanning Multi-channel Microwave Radiometer ERDC/CRREL...and the Special Sensor Microwave/Imager ( SMM /I). The satellite-based technique uses a difference in the passive microwave brightness temperatures

On-Chip Microwave Quantum Hall Circulator

NASA Astrophysics Data System (ADS)

Mahoney, A. C.; Colless, J. I.; Pauka, S. J.; Hornibrook, J. M.; Watson, J. D.; Gardner, G. C.; Manfra, M. J.; Doherty, A. C.; Reilly, D. J.

2017-01-01

Circulators are nonreciprocal circuit elements that are integral to technologies including radar systems, microwave communication transceivers, and the readout of quantum information devices. Their nonreciprocity arises from the interference of microwaves over the centimeter scale of the signal wavelength, in the presence of bulky magnetic media that breaks time-reversal symmetry. Here, we realize a completely passive on-chip microwave circulator with size 1 /1000 th the wavelength by exploiting the chiral, "slow-light" response of a two-dimensional electron gas in the quantum Hall regime. For an integrated GaAs device with 330 μ m diameter and about 1-GHz center frequency, a nonreciprocity of 25 dB is observed over a 50-MHz bandwidth. Furthermore, the nonreciprocity can be dynamically tuned by varying the voltage at the port, an aspect that may enable reconfigurable passive routing of microwave signals on chip.

AMISS - Active and passive MIcrowaves for Security and Subsurface imaging

NASA Astrophysics Data System (ADS)

Soldovieri, Francesco; Slob, Evert; Turk, Ahmet Serdar; Crocco, Lorenzo; Catapano, Ilaria; Di Matteo, Francesca

2013-04-01

The FP7-IRSES project AMISS - Active and passive MIcrowaves for Security and Subsurface imaging is based on a well-combined network among research institutions of EU, Associate and Third Countries (National Research Council of Italy - Italy, Technische Universiteit Delft - The Netherlands, Yildiz Technical University - Turkey, Bauman Moscow State Technical University - Russia, Usikov Institute for Radio-physics and Electronics and State Research Centre of Superconductive Radioelectronics "Iceberg" - Ukraine and University of Sao Paulo - Brazil) with the aims of achieving scientific advances in the framework of microwave and millimeter imaging systems and techniques for security and safety social issues. In particular, the involved partners are leaders in the scientific areas of passive and active imaging and are sharing their complementary knowledge to address two main research lines. The first one regards the design, characterization and performance evaluation of new passive and active microwave devices, sensors and measurement set-ups able to mitigate clutter and increase information content. The second line faces the requirements to make State-of-the-Art processing tools compliant with the instrumentations developed in the first line, suitable to work in electromagnetically complex scenarios and able to exploit the unexplored possibilities offered by new instrumentations. The main goals of the project are: 1) Development/improvement and characterization of new sensors and systems for active and passive microwave imaging; 2) Set up, analysis and validation of state of art/novel data processing approach for GPR in critical infrastructure and subsurface imaging; 3) Integration of state of art and novel imaging hardware and characterization approaches to tackle realistic situations in security, safety and subsurface prospecting applications; 4) Development and feasibility study of bio-radar technology (system and data processing) for vital signs detection and detection/characterization of human beings in complex scenarios. These goals are planned to be reached following a plan of research activities and researchers secondments which cover a period of three years. ACKNOWLEDGMENTS This research has been performed in the framework of the "Active and Passive Microwaves for Security and Subsurface imaging (AMISS)" EU 7th Framework Marie Curie Actions IRSES project (PIRSES-GA-2010-269157).

Microwave responses of the western North Atlantic

NASA Technical Reports Server (NTRS)

Stacey, J. M.; Girard, M. A.

1985-01-01

Features and objects in the Western North Atlantic Ocean - the Eastern Seaboard of the United States - are observed from Earth orbit by passive microwaves. The intensities of their radiated flux signatures are measured and displayed in color as a microwave flux image. The features of flux emitting objects such as the course of the Gulf Stream and the occurrence of cold eddies near the Gulf Stream are identified by contoured patterns of relative flux intensities. The flux signatures of ships and their wakes are displayed and discussed. Metal data buoys and aircraft are detected. Signal to clutter ratios and probabilities of detection are computed from their measured irradiances. Theoretical models and the range equations that explain passive microwave detection using the irradiances of natural sources are summarized.

Comparison of NASA Team2 and AES-York Ice Concentration Algorithms Against Operational Ice Charts From the Canadian Ice Service

NASA Technical Reports Server (NTRS)

Shokr, Mohammed; Markus, Thorsten

2006-01-01

Ice concentration retrieved from spaceborne passive-microwave observations is a prime input to operational sea-ice-monitoring programs, numerical weather prediction models, and global climate models. Atmospheric Environment Service (AES)- York and the Enhanced National Aeronautics and Space Administration Team (NT2) are two algorithms that calculate ice concentration from Special Sensor Microwave/Imager observations. This paper furnishes a comparison between ice concentrations (total, thin, and thick types) output from NT2 and AES-York algorithms against the corresponding estimates from the operational analysis of Radarsat images in the Canadian Ice Service (CIS). A new data fusion technique, which incorporates the actual sensor's footprint, was developed to facilitate this study. Results have shown that the NT2 and AES-York algorithms underestimate total ice concentration by 18.35% and 9.66% concentration counts on average, with 16.8% and 15.35% standard deviation, respectively. However, the retrieved concentrations of thin and thick ice are in much more discrepancy with the operational CIS estimates when either one of these two types dominates the viewing area. This is more likely to occur when the total ice concentration approaches 100%. If thin and thick ice types coexist in comparable concentrations, the algorithms' estimates agree with CIS'S estimates. In terms of ice concentration retrieval, thin ice is more problematic than thick ice. The concept of using a single tie point to represent a thin ice surface is not realistic and provides the largest error source for retrieval accuracy. While AES-York provides total ice concentration in slightly more agreement with CIS'S estimates, NT2 provides better agreement in retrieving thin and thick ice concentrations.

Performance Analysis of a Hardware Implemented Complex Signal Kurtosis Radio-Frequency Interference Detector

NASA Technical Reports Server (NTRS)

Schoenwald, Adam J.; Bradley, Damon C.; Mohammed, Priscilla N.; Piepmeier, Jeffrey R.; Wong, Mark

2016-01-01

Radio-frequency interference (RFI) is a known problem for passive remote sensing as evidenced in the L-band radiometers SMOS, Aquarius and more recently, SMAP. Various algorithms have been developed and implemented on SMAP to improve science measurements. This was achieved by the use of a digital microwave radiometer. RFI mitigation becomes more challenging for microwave radiometers operating at higher frequencies in shared allocations. At higher frequencies larger bandwidths are also desirable for lower measurement noise further adding to processing challenges. This work focuses on finding improved RFI mitigation techniques that will be effective at additional frequencies and at higher bandwidths. To aid the development and testing of applicable detection and mitigation techniques, a wide-band RFI algorithm testing environment has been developed using the Reconfigurable Open Architecture Computing Hardware System (ROACH) built by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) Group. The testing environment also consists of various test equipment used to reproduce typical signals that a radiometer may see including those with and without RFI. The testing environment permits quick evaluations of RFI mitigation algorithms as well as show that they are implementable in hardware. The algorithm implemented is a complex signal kurtosis detector which was modeled and simulated. The complex signal kurtosis detector showed improved performance over the real kurtosis detector under certain conditions. The real kurtosis is implemented on SMAP at 24 MHz bandwidth. The complex signal kurtosis algorithm was then implemented in hardware at 200 MHz bandwidth using the ROACH. In this work, performance of the complex signal kurtosis and the real signal kurtosis are compared. Performance evaluations and comparisons in both simulation as well as experimental hardware implementations were done with the use of receiver operating characteristic (ROC) curves.

A scattering-based over-land rainfall retrieval algorithm for South Korea using GCOM-W1/AMSR-2 data

NASA Astrophysics Data System (ADS)

Kwon, Young-Joo; Shin, Hayan; Ban, Hyunju; Lee, Yang-Won; Park, Kyung-Ae; Cho, Jaeil; Park, No-Wook; Hong, Sungwook

2017-08-01

Heavy summer rainfall is a primary natural disaster affecting lives and properties in the Korean Peninsula. This study presents a satellite-based rainfall rate retrieval algorithm for the South Korea combining polarization-corrected temperature ( PCT) and scattering index ( SI) data from the 36.5 and 89.0 GHz channels of the Advanced microwave Scanning Radiometer 2 (AMSR-2) onboard the Global Change Observation Mission (GCOM)-W1 satellite. The coefficients for the algorithm were obtained from spatial and temporal collocation data from the AMSR-2 and groundbased automatic weather station rain gauges from 1 July - 30 August during the years, 2012-2015. There were time delays of about 25 minutes between the AMSR-2 observations and the ground raingauge measurements. A new linearly-combined rainfall retrieval algorithm focused on heavy rain for the PCT and SI was validated using ground-based rainfall observations for the South Korea from 1 July - 30 August, 2016. The validation presented PCT and SI methods showed slightly improved results for rainfall > 5 mm h-1 compared to the current ASMR-2 level 2 data. The best bias and root mean square error (RMSE) for the PCT method at AMSR-2 36.5 GHz were 2.09 mm h-1 and 7.29 mm h-1, respectively, while the current official AMSR-2 rainfall rates show a larger bias and RMSE (4.80 mm h-1 and 9.35 mm h-1, respectively). This study provides a scatteringbased over-land rainfall retrieval algorithm for South Korea affected by stationary front rain and typhoons with the advantages of the previous PCT and SI methods to be applied to a variety of spaceborne passive microwave radiometers.

Satellite remote sensing of the ocean

NASA Technical Reports Server (NTRS)

Fu, Lee-Lueng; Liu, W. T.; Abbott, Mark R.

1990-01-01

A concise description of the principles and applications of several selected instruments that have been utilized most frequently in remote sensing of the ocean from satellites is presented. Emphasis is placed on the current progress in oceanographic applications and the outlook of the instruments in future oceanographic satellite missions is discussed. The instruments under discussion are placed into three groups: active microwave sensors, passive ocean color and infrared sensors, and passive microwave sensors.

GPM and TRMM Radar Vertical Profiles and Impact on Large-scale Variations of Surface Rain

NASA Astrophysics Data System (ADS)

Wang, J. J.; Adler, R. F.

2017-12-01

Previous studies by the authors using Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) data have shown that TRMM Precipitation Radar (PR) and GPM Dual-Frequency Precipitation Radar (DPR) surface rain estimates do not have corresponding amplitudes of inter-annual variations over the tropical oceans as do passive microwave observations by TRMM Microwave Imager (TMI) and GPM Microwave Imager (GMI). This includes differences in surface temperature-rainfall variations. We re-investigate these relations with the new GPM Version 5 data with an emphasis on understanding these differences with respect to the DPR vertical profiles of reflectivity and rainfall and the associated convective and stratiform proportions. For the inter-annual variation of ocean rainfall from both passive microwave (TMI and GMI) and active microwave (PR and DPR) estimates, it is found that for stratiform rainfall both TMI-PR and GMI-DPR show very good correlation. However, the correlation of GMI-DPR is much higher than TMI-PR in convective rainfall. The analysis of vertical profile of PR and DPR rainfall during the TRMM and GPM overlap period (March-August, 2014) reveals that PR and DPR have about the same rainrate at 4km and above, but PR rainrate is more than 10% lower that of DPR at the surface. In other words, it seems that convective rainfall is better defined with DPR near surface. However, even though the DPR results agree better with the passive microwave results, there still is a significant difference, which may be a result of DPR retrieval error, or inherent passive/active retrieval differences. Monthly and instantaneous GMI and DPR data need to be analyzed in details to better understand the differences.

Microwave radiative transfer studies of precipitation

NASA Technical Reports Server (NTRS)

Bringi, V. N.; Vivekanandan, J.; Turk, F. Joseph

1993-01-01

Since the deployment of the DMSP SSM/I microwave imagers in 1987, increased utilization of passive microwave radiometry throughout the 10 - 100 GHz spectrum has occurred for measurement of atmospheric constituents and terrestrial surfaces. Our efforts have focused on observations and analysis of the microwave radiative transfer behavior of precipitating clouds. We have focused particular attention on combining both aircraft and SSM/I radiometer imagery with ground-based multiparameter radar observations. As part of this and the past NASA contract, we have developed a multi-stream, polarized radiative transfer model which incorporates scattering. The model has the capability to be initialized with cloud model output or multiparameter radar products. This model provides the necessary 'link' between the passive microwave radiometer and active microwave radar observations. This unique arrangement has allowed the brightness temperatures (TB) to be compared against quantities such as rainfall, liquid/ice water paths, and the vertical structure of the cloud. Quantification of the amounts of ice and water in precipitating clouds is required for understanding of the global energy balance.

Snow Crystal Orientation Effects on the Scattering of Passive Microwave Radiation

NASA Technical Reports Server (NTRS)

Foster, J. L.; Barton, J. S.; Chang, A. T. C.; Hall, D. K.

1999-01-01

For this study, consideration is given to the role crystal orientation plays in scattering and absorbing microwave radiation. A discrete dipole scattering model is used to measure the passive microwave radiation, at two polarizations (horizontal and vertical), scattered by snow crystals oriented in random and non random positions, having various sizes (ranging between 1 micrometers to 10,000 micrometers in radius), and shapes (including spheroids, cylinders, hexagons). The model results demonstrate that for the crystal sizes typically found in a snowpack, crystal orientation is insignificant compared to crystal size in terms of scattering microwave energy in the 8,100 gm (37 GHz) region of the spectrum. Therefore, the assumption used in radiative transfer approaches, where snow crystals are modeled as randomly oriented spheres, is adequate to account for the transfer of microwave energy emanating from the ground and passing through a snowpack.

Laboratory for Atmospheres: Instrument Systems Report

NASA Technical Reports Server (NTRS)

2011-01-01

Studies of the atmospheres of our solar system's planets including our own require a comprehensive set of observations, relying on instruments on spacecraft, aircraft, balloons, and on the surface. Laboratory personnel define requirements, conceive concepts, and develop instrument systems for spaceflight missions, and for balloon, aircraft, and ground-based observations. Laboratory scientists also participate in the design of data processing algorithms, calibration techniques, and data processing systems. The instrument sections of this report are organized by measurement technique: lidar, passive, in situ and microwave. A number of instruments in various stages of development or modification are also described. This report will be updated as instruments evolve.

Glacier Melt Detection in Complex Terrain Using New AMSR-E Calibrated Enhanced Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record

NASA Astrophysics Data System (ADS)

Ramage, J. M.; Brodzik, M. J.; Hardman, M.

2016-12-01

Passive microwave (PM) 18 GHz and 36 GHz horizontally- and vertically-polarized brightness temperatures (Tb) channels from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) have been important sources of information about snow melt status in glacial environments, particularly at high latitudes. PM data are sensitive to the changes in near-surface liquid water that accompany melt onset, melt intensification, and refreezing. Overpasses are frequent enough that in most areas multiple (2-8) observations per day are possible, yielding the potential for determining the dynamic state of the snow pack during transition seasons. AMSR-E Tb data have been used effectively to determine melt onset and melt intensification using daily Tb and diurnal amplitude variation (DAV) thresholds. Due to mixed pixels in historically coarse spatial resolution Tb data, melt analysis has been impractical in ice-marginal zones where pixels may be only fractionally snow/ice covered, and in areas where the glacier is near large bodies of water: even small regions of open water in a pixel severely impact the microwave signal. We use the new enhanced-resolution Calibrated Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record product's twice daily obserations to test and update existing snow melt algorithms by determining appropriate melt thresholds for both Tb and DAV for the CETB 18 and 36 GHz channels. We use the enhanced resolution data to evaluate melt characteristics along glacier margins and melt transition zones during the melt seasons in locations spanning a wide range of melt scenarios, including the Patagonian Andes, the Alaskan Coast Range, and the Russian High Arctic icecaps. We quantify how improvement of spatial resolution from the original 12.5 - 25 km-scale pixels to the enhanced resolution of 3.125 - 6.25 km improves the ability to evaluate melt timing across boundaries and transition zones in diverse glacial environments.

Land Surface Microwave Emissivities Derived from AMSR-E and MODIS Measurements with Advanced Quality Control

NASA Technical Reports Server (NTRS)

Moncet, Jean-Luc; Liang, Pan; Galantowicz, John F.; Lipton, Alan E.; Uymin, Gennady; Prigent, Catherine; Grassotti, Christopher

2011-01-01

A microwave emissivity database has been developed with data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and with ancillary land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same Aqua spacecraft. The primary intended application of the database is to provide surface emissivity constraints in atmospheric and surface property retrieval or assimilation. An additional application is to serve as a dynamic indicator of land surface properties relevant to climate change monitoring. The precision of the emissivity data is estimated to be significantly better than in prior databases from other sensors due to the precise collocation with high-quality MODIS LST data and due to the quality control features of our data analysis system. The accuracy of the emissivities in deserts and semi-arid regions is enhanced by applying, in those regions, a version of the emissivity retrieval algorithm that accounts for the penetration of microwave radiation through dry soil with diurnally varying vertical temperature gradients. These results suggest that this penetration effect is more widespread and more significant to interpretation of passive microwave measurements than had been previously established. Emissivity coverage in areas where persistent cloudiness interferes with the availability of MODIS LST data is achieved using a classification-based method to spread emissivity data from less-cloudy areas that have similar microwave surface properties. Evaluations and analyses of the emissivity products over homogeneous snow-free areas are presented, including application to retrieval of soil temperature profiles. Spatial inhomogeneities are the largest in the vicinity of large water bodies due to the large water/land emissivity contrast and give rise to large apparent temporal variability in the retrieved emissivities when satellite footprint locations vary over time. This issue will be dealt with in the future by including a water fraction correction. Also note that current reliance on the MODIS day-night algorithm as a source of LST limits the coverage of the database in the Polar Regions. We will consider relaxing the current restriction as part of future development.

Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China

PubMed Central

Shao, Zhenfeng; Zhang, Linjing

2016-01-01

Estimation of forest aboveground biomass is critical for regional carbon policies and sustainable forest management. Passive optical remote sensing and active microwave remote sensing both play an important role in the monitoring of forest biomass. However, optical spectral reflectance is saturated in relatively dense vegetation areas, and microwave backscattering is significantly influenced by the underlying soil when the vegetation coverage is low. Both of these conditions decrease the estimation accuracy of forest biomass. A new optical and microwave integrated vegetation index (VI) was proposed based on observations from both field experiments and satellite (Landsat 8 Operational Land Imager (OLI) and RADARSAT-2) data. According to the difference in interaction between the multispectral reflectance and microwave backscattering signatures with biomass, the combined VI (COVI) was designed using the weighted optical optimized soil-adjusted vegetation index (OSAVI) and microwave horizontally transmitted and vertically received signal (HV) to overcome the disadvantages of both data types. The performance of the COVI was evaluated by comparison with those of the sole optical data, Synthetic Aperture Radar (SAR) data, and the simple combination of independent optical and SAR variables. The most accurate performance was obtained by the models based on the COVI and optical and microwave optimal variables excluding OSAVI and HV, in combination with a random forest algorithm and the largest number of reference samples. The results also revealed that the predictive accuracy depended highly on the statistical method and the number of sample units. The validation indicated that this integrated method of determining the new VI is a good synergistic way to combine both optical and microwave information for the accurate estimation of forest biomass. PMID:27338378

Multivariate Statistical Inference of Lightning Occurrence, and Using Lightning Observations

NASA Technical Reports Server (NTRS)

Boccippio, Dennis

2004-01-01

Two classes of multivariate statistical inference using TRMM Lightning Imaging Sensor, Precipitation Radar, and Microwave Imager observation are studied, using nonlinear classification neural networks as inferential tools. The very large and globally representative data sample provided by TRMM allows both training and validation (without overfitting) of neural networks with many degrees of freedom. In the first study, the flashing / or flashing condition of storm complexes is diagnosed using radar, passive microwave and/or environmental observations as neural network inputs. The diagnostic skill of these simple lightning/no-lightning classifiers can be quite high, over land (above 80% Probability of Detection; below 20% False Alarm Rate). In the second, passive microwave and lightning observations are used to diagnose radar reflectivity vertical structure. A priori diagnosis of hydrometeor vertical structure is highly important for improved rainfall retrieval from either orbital radars (e.g., the future Global Precipitation Mission "mothership") or radiometers (e.g., operational SSM/I and future Global Precipitation Mission passive microwave constellation platforms), we explore the incremental benefit to such diagnosis provided by lightning observations.

Aircraft and satellite passive microwave observations of the Bering Sea ice cover during MIZEX West

NASA Technical Reports Server (NTRS)

Cavalieri, D. J.; Gloersen, P.; Wilheit, T. T., Jr.

1986-01-01

Passive microwave measurements of the Bering Sea were made with the NASA CV-990 airborne laboratory during February. Microwave data were obtained with imaging and dual-polarized, fixed-beam radiometers in a range of frequencies from 10 to 183 GHz. The high resolution imagery at 92 GHz provides a particularly good description of the marginal ice zone delineating regions of open water, ice compactness, and ice-edge structure. Analysis of the fixed-beam data shows that spectral differences increase with a decrease in ice thickness. Polarization at 18 and 37 GHz distinguishes among new, young, and first-year ice types.

Passive microwave characteristics of the Bering Sea ice cover during Marginal Ice Zone Experiment (MIZEX) West

NASA Technical Reports Server (NTRS)

Cavalieri, D. J.; Gloersen, P.; Wilheit, T. T.; Calhoon, C.

1984-01-01

Passive microwave measurements of the Bering Sea were made with the NASA CV-990 airborne laboratory during February. Microwave data were obtained with imaging and dual-polarized, fixed-beam radiometers in a range of frequencies from 10 to 183 GHz. The high resolution imagery at 92 GHz provides a particularly good description of the marginal ice zone delineating regions of open water, ice compactness, and ice-edge structure. Analysis of the fixed-beam data shows that spectral differences increase with a decrease in ice thickness. Polarization at 18 and 37 GHz distinguishes among new, young, and first-year sea ice types.

Passive On-Chip Superconducting Circulator Using a Ring of Tunnel Junctions

NASA Astrophysics Data System (ADS)

Müller, Clemens; Guan, Shengwei; Vogt, Nicolas; Cole, Jared H.; Stace, Thomas M.

2018-05-01

We present the design of a passive, on-chip microwave circulator based on a ring of superconducting tunnel junctions. We investigate two distinct physical realizations, based on Josephson junctions (JJs) or quantum phase slip elements (QPS), with microwave ports coupled either capacitively (JJ) or inductively (QPS) to the ring structure. A constant bias applied to the center of the ring provides an effective symmetry breaking field, and no microwave or rf bias is required. We show that this design offers high isolation, robustness against fabrication imperfections and bias fluctuations, and a bandwidth in excess of 500 MHz for realistic device parameters.

Investigation of antenna pattern constraints for passive geosynchronous microwave imaging radiometers

NASA Technical Reports Server (NTRS)

Gasiewski, A. J.; Skofronick, G. M.

1992-01-01

Progress by investigators at Georgia Tech in defining the requirements for large space antennas for passive microwave Earth imaging systems is reviewed. In order to determine antenna constraints (e.g., the aperture size, illumination taper, and gain uncertainty limits) necessary for the retrieval of geophysical parameters (e.g., rain rate) with adequate spatial resolution and accuracy, a numerical simulation of the passive microwave observation and retrieval process is being developed. Due to the small spatial scale of precipitation and the nonlinear relationships between precipitation parameters (e.g., rain rate, water density profile) and observed brightness temperatures, the retrieval of precipitation parameters are of primary interest in the simulation studies. Major components of the simulation are described as well as progress and plans for completion. The overall goal of providing quantitative assessments of the accuracy of candidate geosynchronous and low-Earth orbiting imaging systems will continue under a separate grant.

The Integration of SMOS Soil Moisture in a Consistent Soil Moisture Climate Record

NASA Astrophysics Data System (ADS)

de Jeu, Richard; Kerr, Yann; Wigneron, Jean Pierre; Rodriguez-Fernandez, Nemesio; Al-Yaari, Amen; van der Schalie, Robin; Dolman, Han; Drusch, Matthias; Mecklenburg, Susanne

2015-04-01

Recently, a study funded by the European Space Agency (ESA) was set up to provide guidelines for the development of a global soil moisture climate record with a special emphasis on the integration of SMOS. Three different data fusion approaches were designed and implemented on 10 year passive microwave data (2003-2013) from two different satellite sensors; the ESA Soil Moisture Ocean Salinity Mission (SMOS) and the NASA/JAXA Advanced Scanning Microwave Radiometer (AMSR-E). The AMSR-E data covered the period from January 2003 until Oct 2011 and SMOS data covered the period from June 2010 until the end of 2013. The fusion approaches included a neural network approach (Rodriguez-Fernandez et al., this conference session HS6.4), a regression approach (Wigneron et al., 2004), and an approach based on the baseline algorithm of ESAs current Climate Change Initiative soil moisture program, the Land Parameter Retrieval Model (Van der Schalie et al., this conference session HS6.4). With this presentation we will show the first results from this study including a description of the different approaches and the validation activities using both globally covered modeled datasets and ground observations from the international soil moisture network. The statistical validation analyses will give us information on the temporal and spatial performance of the three different approaches. Based on these results we will then discuss the next steps towards a seamless integration of SMOS in a consistent soil moisture climate record. References Wigneron J.-P., J.-C. Calvet, P. de Rosnay, Y. Kerr, P. Waldteufel, K. Saleh, M. J. Escorihuela, A. Kruszewski, 'Soil Moisture Retrievals from Bi-Angular L-band Passive Microwave Observations', IEEE Trans. Geosc. Remote Sens. Let., vol 1, no. 4, 277-281, 2004.

Effects of Atmospheric Water and Surface Wind on Passive Microwave Retrievals of Sea Ice Concentration: a Simulation Study

NASA Astrophysics Data System (ADS)

Shin, D.; Chiu, L. S.; Clemente-Colon, P.

2006-05-01

The atmospheric effects on the retrieval of sea ice concentration from passive microwave sensors are examined using simulated data typical for the Arctic summer. The simulation includes atmospheric contributions of cloud liquid water, water vapor and surface wind on the microwave signatures. A plane parallel radiative transfer model is used to compute brightness temperatures at SSM/I frequencies over surfaces that contain open water, first-year (FY) ice and multi-year (MY) ice and their combinations. Synthetic retrievals in this study use the NASA Team (NT) algorithm for the estimation of sea ice concentrations. This study shows that if the satellite sensor's field of view is filled with only FY ice the retrieval is not much affected by the atmospheric conditions due to the high contrast between emission signals from FY ice surface and the signals from the atmosphere. Pure MY ice concentration is generally underestimated due to the low MY ice surface emissivity that results in the enhancement of emission signals from the atmospheric parameters. Simulation results in marginal ice areas also show that the atmospheric effects from cloud liquid water, water vapor and surface wind tend to degrade the accuracy at low sea ice concentration. FY ice concentration is overestimated and MY ice concentration is underestimated in the presence of atmospheric water and surface wind at low ice concentration. This compensating effect reduces the retrieval uncertainties of total (FY and MY) ice concentration. Over marginal ice zones, our results suggest that strong surface wind is more important than atmospheric water in contributing to the retrieval errors of total ice concentrations in the normal ranges of these variables.

Comparison of AMSR-E and SSM/I snow parameter retrievals over the Ob river basin

USGS Publications Warehouse

Mognard, N.M.; Grippa, M.; LeToan, T.; Kelly, R.E.J.; Chang, A.T.C.; Josberger, E.G.

2004-01-01

Passive microwave observations from the Advanced Microwave Scanning Radiometer - EOS (AMSR-E) and from the Special Sensor Microwave Imager (SSM/I) are used to analyse the evolution of the snow pack in the Ob river basin during the snow season of 2002-03. The Ob river is the biggest Russian river with respect to its watershed area (2 975 000 km2). The Ob originates in the Altai mountains and flows northward across the vast West Siberian lowland towards the Arctic Ocean. The majority of snow cover is contained in the lowlands rather than in mountainous regions and persists for six months or more. During the snow season, surface air temperatures are very cold. Therefore, the combination of cold dry snow and large areas of uniform topography is ideal for snowpack extent and water equivalent retrievals from passive microwave observations. The thermal gradient through the snow pack is estimated and used to model the growth of the snow grain size and to compute the evolution of the passive microwave derived snow depth over the region. A comparison between the AMSR-E and SSM/I estimates is performed and the differences between the snow parameters from the two satellite instruments are analysed.

Greenland 1979 microwave remote sensing data catalog report, 14-15 October 1979

NASA Technical Reports Server (NTRS)

Hennigar, H. F.; Hirstein, W. S.; Schaffner, S. K.; Delnore, V. E.; Grantham, W. L.

1983-01-01

Microwave remote sensing measurements were cataloged for active and passive instruments in support of the 1979 Greenland Remote Sensing Experiment. Instruments used in this field experiment include the stepped frequency microwave radiometer (4 to 8 GHz) and the airborne microwave scatterometer (14.6 GHz). The microwave signature data are inventoried and cataloged in a user friendly format and are available on 9 track computer compatible tapes upon request.

Water vapor profiling using microwave radiometry

NASA Technical Reports Server (NTRS)

Wang, J. R.; Wilheit, T. T.

1988-01-01

Water vapor is one of the most important constituents in the Earth's atmosphere. Its spatial and temporal variations affect a wide spectrum of meteorological phenomena ranging from the formation of clouds to the development of severe storms. The passive microwave technique offers an excellent means for water vapor measurements. It can provide both day and night coverage under most cloud conditions. Two water vapor absorption features, at 22 and 183 GHz, were explored in the past years. The line strengths of these features differ by nearly two orders of magnitude. As a consequence, the techniques and the final products of water vapor measurements are also quite different. The research effort in the past few years was to improve and extend the retrieval algorithm to the measurements of water vapor profiles under cloudy conditions. In addition, the retrieval of total precipitable water using 183 GHz measurements, but in a manner analogous to the use of 22 GHz measurements, to increase measurement sensitivity for atmospheres of very low moisture content was also explored.

The effect of severe storms on the ice cover of the northern Tatarskiy Strait

NASA Technical Reports Server (NTRS)

Martin, Seelye; Munoz, Esther; Drucker, Robert

1992-01-01

Passive microwave images from the Special Sensor Microwave Imager are used to study the volume of ice and sea-bottom water in the Japan Sea as affected by winds and severe storms. The data set comprises brightness temperatures gridded on a polar stereographic projection, and the processing is accomplished with a linear algorithm by Cavalieri et al. (1983) based on the vertically polarized 37-GHz channel. The expressions for calculating heat fluxes and downwelling radiation are given, and ice-cover fluctuations are correlated with severe storm events. The storms generate large transient polynya that occur simultaneously with the strongest heat fluxes, and severe storms are found to contribute about 25 percent of the annual introduction of 25 cu km of ice in the region. The ice production could lead to the renewal of enough sea-bottom water to account for the C-14 data provided, and the generation of Japan Sea bottom water is found to vary directly with storm activity.

Consistent radiative transfer modeling of active and passive observations of precipitation

NASA Astrophysics Data System (ADS)

Adams, Ian

2016-04-01

Spaceborne platforms such as the Tropical Rainfall Measurement Mission (TRMM) and the Global Precipitation Measurement (GPM) mission exploit a combination of active and passive sensors to provide a greater understanding of the three-dimensional structure of precipitation. While "operationalized" retrieval algorithms require fast forward models, the ability to perform higher fidelity simulations is necessary in order to understand the physics of remote sensing problems by testing assumptions and developing parameterizations for the fast models. To ensure proper synergy between active and passive modeling, forward models must be consistent when modeling the responses of radars and radiometers. This work presents a self-consistent transfer model for simulating radar reflectivities and millimeter wave brightness temperatures for precipitating scenes. To accomplish this, we extended the Atmospheric Radiative Transfer Simulator (ARTS) version 2.3 to solve the radiative transfer equation for active sensors and multiple scattering conditions. Early versions of ARTS (1.1) included a passive Monte Carlo solver, and ARTS is capable of handling atmospheres of up to three dimensions with ellipsoidal planetary geometries. The modular nature of ARTS facilitates extensibility, and the well-developed ray-tracing tools are suited for implementation of Monte Carlo algorithms. Finally, since ARTS handles the full Stokes vector, co- and cross-polarized reflectivity products are possible for scenarios that include nonspherical particles, with or without preferential alignment. The accuracy of the forward model will be demonstrated with precipitation events observed by TRMM and GPM, and the effects of multiple scattering will be detailed. The three-dimensional nature of the radiative transfer model will be useful for understanding the effects of nonuniform beamfill and multiple scattering for spatially heterogeneous precipitation events. The targets of this forward model are GPM (the Dual-wavelength Precipitation Radar (DPR) and GPM Microwave Imager (GMI)).

An extended global Earth system data record on daily landscape freeze-thaw status determined from satellite passive microwave remote sensing

NASA Astrophysics Data System (ADS)

Kim, Youngwook; Kimball, John S.; Glassy, Joseph; Du, Jinyang

2017-02-01

The landscape freeze-thaw (FT) signal determined from satellite microwave brightness temperature (Tb) observations has been widely used to define frozen temperature controls on land surface water mobility and ecological processes. Calibrated 37 GHz Tb retrievals from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and SSM/I Sounder (SSMIS) were used to produce a consistent and continuous global daily data record of landscape FT status at 25 km grid cell resolution. The resulting FT Earth system data record (FT-ESDR) is derived from a refined classification algorithm and extends over a larger domain and longer period (1979-2014) than prior FT-ESDR releases. The global domain encompasses all land areas affected by seasonal frozen temperatures, including urban, snow- and ice-dominant and barren land, which were not represented by prior FT-ESDR versions. The FT retrieval is obtained using a modified seasonal threshold algorithm (MSTA) that classifies daily Tb variations in relation to grid-cell-wise FT thresholds calibrated using surface air temperature data from model reanalysis. The resulting FT record shows respective mean annual spatial classification accuracies of 90.3 and 84.3 % for evening (PM) and morning (AM) overpass retrievals relative to global weather station measurements. Detailed data quality metrics are derived characterizing the effects of sub-grid-scale open water and terrain heterogeneity, as well as algorithm uncertainties on FT classification accuracy. The FT-ESDR results are also verified against other independent cryospheric data, including in situ lake and river ice phenology, and satellite observations of Greenland surface melt. The expanded FT-ESDR enables new investigations encompassing snow- and ice-dominant land areas, while the longer record and favorable accuracy allow for refined global change assessments that can better distinguish transient weather extremes, landscape phenological shifts, and climate anomalies from longer-term trends extending over multiple decades. The dataset is freely available online (doi:10.5067/MEASURES/CRYOSPHERE/nsidc-0477.003).

An Integrated Retrieval Framework for AMSR2: Implications for Light Precipitation and Sea Ice Edge Detectability

NASA Astrophysics Data System (ADS)

Duncan, D.; Kummerow, C. D.; Meier, W.

2016-12-01

Over the lifetime of AMSR-E, operational retrieval algorithms were developed and run for precipitation, ocean suite (SST, wind speed, cloud liquid water path, and column water vapor over ocean), sea ice, snow water equivalent, and soil moisture. With a separate algorithm for each group, the retrievals were never interactive or integrated in any way despite many co-sensitivities. AMSR2, the follow-on mission to AMSR-E, retrieves the same parameters at a slightly higher spatial resolution. We have combined the operational algorithms for AMSR2 in a way that facilitates sharing information between the retrievals. Difficulties that arose were mainly related to calibration, spatial resolution, coastlines, and order of processing. The integration of all algorithms for AMSR2 has numerous benefits, including better detection of light precipitation and sea ice, fewer screened out pixels, and better quality flags. Integrating the algorithms opens up avenues for investigating the limits of detectability for precipitation from a passive microwave radiometer and the impact of spatial resolution on sea ice edge detection; these are investigated using CloudSat and MODIS coincident observations from the A-Train constellation.

A Combined Infrared and Microwave Technique for Studying the Diurnal Variation of Rainfall over Amazonia

NASA Technical Reports Server (NTRS)

Negri, Andrew J.; Anagnostou, Emmanouil; Adler, Robert F.

1999-01-01

Over 10 years of continuous data from the Special Sensor microwave Imager (SSM/I) aboard a series of Defense Department satellites has made it possible to construct regional rainfall climatologies at high spatial resolution. Using the Goddard Profiling Algorithm (GPROF), monthly estimates of precipitation were made over the region of northern Brazil, including the Amazon Basin, for 1987 to 1998. GPROF is a physical approach to passive microwave precipitation retrieval, which uses the Goddard Cumulus Ensemble (cloud) model to establish prior probability densities of precipitation structures. Precipitation fields from GPROF were stratified into morning and evening satellite overpasses, and accumulated at monthly intervals at 0.5 degree spatial resolution. Important diurnal effects were noted in the analysis, the most pronounced being a land/sea breeze circulation along the northern coast of Brazil and a mountain/valley circulation along the Andes. There were also indications of morning rainfall maxima along the major rivers, and evening maxima between the rivers. The addition of simultaneous geosynchronous infrared (IR) data leads to the current technique, which takes advantage of the 30 minute sampling and 4 km spatial resolution of the infrared channel and the better physics of the microwave retrieval. The resultant IR method is subsequently used to derive the diurnal variability of rainfall over the Amazon basin, and further, to investigate the relative contribution from its convective and stratiform components.

Calibrated, Enhanced-Resolution Brightness Temperature Earth System Data Record: A New Era for Gridded Passive Microwave Data

NASA Astrophysics Data System (ADS)

Hardman, M.; Brodzik, M. J.; Long, D. G.

2017-12-01

Since 1978, the satellite passive microwave data record has been a mainstay of remote sensing of the cryosphere, providing twice-daily, near-global spatial coverage for monitoring changes in hydrologic and cryospheric parameters that include precipitation, soil moisture, surface water, vegetation, snow water equivalent, sea ice concentration and sea ice motion. Up until recently, the available global gridded passive microwave data sets have not been produced consistently. Various projections (equal-area, polar stereographic), a number of different gridding techniques were used, along with various temporal sampling as well as a mix of Level 2 source data versions. In addition, not all data from all sensors have been processed completely and they have not been processed in any one consistent way. Furthermore, the original gridding techniques were relatively primitive and were produced on 25 km grids using the original EASE-Grid definition that is not easily accommodated in modern software packages. As part of NASA MEaSUREs, we have re-processed all data from SMMR, all SSM/I-SSMIS and AMSR-E instruments, using the most mature Level 2 data. The Calibrated, Enhanced-Resolution Brightness Temperature (CETB) Earth System Data Record (ESDR) gridded data are now available from the NSIDC DAAC. The data are distributed as netCDF files that comply with CF-1.6 and ACDD-1.3 conventions. The data have been produced on EASE 2.0 projections at smoothed, 25 kilometer resolution and spatially-enhanced resolutions, up to 3.125 km depending on channel frequency, using the radiometer version of the Scatterometer Image Reconstruction (rSIR) method. We expect this newly produced data set to enable scientists to better analyze trends in coastal regions, marginal ice zones and in mountainous terrain that were not possible with the previous gridded passive microwave data. The use of the EASE-Grid 2.0 definition and netCDF-CF formatting allows users to extract compliant geotiff images and provides for easy importing and correct reprojection interoperability in many standard packages. As a consistently-processed, high-quality satellite passive microwave ESDR, we expect this data set to replace earlier gridded passive microwave data sets, and to pave the way for new insights from higher-resolution derived geophysical products.

Precipitation Retrievals in typhoon domain combining of FY3C MWHTS Observations and WRF Predicted Models

NASA Astrophysics Data System (ADS)

Jieying, HE; Shengwei, ZHANG; Na, LI

2017-02-01

A passive sub-millimeter precipitation retrievals algorithm is provided based on Microwave Humidity and Temperature Sounder (MWHTS) onboard the Chinese Feng Yun 3C (FY-3C) satellite. Using the validated global reference physical model NCEP/WRF/VDISORT), NCEP data per 6 hours are downloaded to run the Weather Research and Forecast model WRF, and derive the typical precipitation data from the whole world. The precipitation retrieval algorithm can operate either on land or on seawater for global. To simply the calculation procedure and save the training time, principle component analysis (PCA) was adapted to filter out the redundancy caused by scanning angle and surface effects, as well as system noise. According to the comparison and validation combing with other precipitation sources, it is demonstrated that the retrievals are reliable for surface precipitation rate higher than 0.1 mm/h at 15km resolution.

Estimating surface soil moisture from SMAP observations using a Neural Network technique.

PubMed

Kolassa, J; Reichle, R H; Liu, Q; Alemohammad, S H; Gentine, P; Aida, K; Asanuma, J; Bircher, S; Caldwell, T; Colliander, A; Cosh, M; Collins, C Holifield; Jackson, T J; Martínez-Fernández, J; McNairn, H; Pacheco, A; Thibeault, M; Walker, J P

2018-01-01

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m -3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m -3 , 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Satellite Remote Sensing: Passive-Microwave Measurements of Sea Ice

NASA Technical Reports Server (NTRS)

Parkinson, Claire L.; Zukor, Dorothy J. (Technical Monitor)

2001-01-01

Satellite passive-microwave measurements of sea ice have provided global or near-global sea ice data for most of the period since the launch of the Nimbus 5 satellite in December 1972, and have done so with horizontal resolutions on the order of 25-50 km and a frequency of every few days. These data have been used to calculate sea ice concentrations (percent areal coverages), sea ice extents, the length of the sea ice season, sea ice temperatures, and sea ice velocities, and to determine the timing of the seasonal onset of melt as well as aspects of the ice-type composition of the sea ice cover. In each case, the calculations are based on the microwave emission characteristics of sea ice and the important contrasts between the microwave emissions of sea ice and those of the surrounding liquid-water medium.

An intercomparison of remotely sensed soil moisture products at various spatial scales over the Iberian penisula

USDA-ARS?s Scientific Manuscript database

Soil moisture (SM) can be retrieved from active microwave (AM)-, passive microwave (PM)- and thermal infrared (TIR)-observations, each having their unique spatial- and temporal-coverage. A limitation of TIR-based SM retrievals is its dependency on cloud-free conditions, while microwave retrievals ar...

GCOM-W AMSR2 soil moisture product validation using core validation sites

USDA-ARS?s Scientific Manuscript database

The Advanced Microwave Scanning Radiometer 2 (AMSR2) is part of the Global Change Observation Mission-Water (GCOM-W). AMSR2 has filled the gap in passive microwave observations left by the loss of the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) after almost 10 years of obs...

The Norwegian remote sensing experiment (Norsex) in a marginal ice zone

NASA Technical Reports Server (NTRS)

Farrelly, B.; Johannessen, J.; Johannessen, O. M.; Svendson, E.; Kloster, K.; Horjen, I.; Campbell, W. J.; Crawford, J.; Harrington, R.; Jones, L.

1981-01-01

Passive and active microwave measurements from surface based, airborne, and satellite instruments were obtained together with surface observations northwest of Svalbard. Emissivities of different ice patches in the ice edge region over the spectral range from 4.9 to 94 GHz are presented. The combination of a 6.6 GHz microwave radiometer with a 14.6 GHz scatterometer demonstrates the usefulness of an active/passive system in ice classification. A variety of mesoscale features under different meteorological conditions is revealed by a 1.36 GHz synthetic aperture radar. Ice edge location by Nimbus 7 scanning multifrequency microwave radiometer is shown accurate to 10 km when the 37 GHz horizontal polarized channel is used.

Shaping complex microwave fields in reverberating media with binary tunable metasurfaces

PubMed Central

Kaina, Nadège; Dupré, Matthieu; Lerosey, Geoffroy; Fink, Mathias

2014-01-01

In this article we propose to use electronically tunable metasurfaces as spatial microwave modulators. We demonstrate that like spatial light modulators, which have been recently proved to be ideal tools for controlling light propagation through multiple scattering media, spatial microwave modulators can efficiently shape in a passive way complex existing microwave fields in reverberating environments with a non-coherent energy feedback. Unlike in free space, we establish that a binary-only phase state tunable metasurface allows a very good control over the waves, owing to the random nature of the electromagnetic fields in these complex media. We prove in an everyday reverberating medium, that is, a typical office room, that a small spatial microwave modulator placed on the walls can passively increase the wireless transmission between two antennas by an order of magnitude, or on the contrary completely cancel it. Interestingly and contrary to free space, we show that this results in an isotropic shaped microwave field around the receiving antenna, which we attribute again to the reverberant nature of the propagation medium. We expect that spatial microwave modulators will be interesting tools for fundamental physics and will have applications in the field of wireless communications. PMID:25331498

Performance Analysis of a Hardware Implemented Complex Signal Kurtosis Radio-Frequency Interference Detector

NASA Technical Reports Server (NTRS)

Schoenwald, Adam J.; Bradley, Damon C.; Mohammed, Priscilla N.; Piepmeier, Jeffrey R.; Wong, Mark

2016-01-01

Radio-frequency interference (RFI) is a known problem for passive remote sensing as evidenced in the L-band radiometers SMOS, Aquarius and more recently, SMAP. Various algorithms have been developed and implemented on SMAP to improve science measurements. This was achieved by the use of a digital microwave radiometer. RFI mitigation becomes more challenging for microwave radiometers operating at higher frequencies in shared allocations. At higher frequencies larger bandwidths are also desirable for lower measurement noise further adding to processing challenges. This work focuses on finding improved RFI mitigation techniques that will be effective at additional frequencies and at higher bandwidths. To aid the development and testing of applicable detection and mitigation techniques, a wide-band RFI algorithm testing environment has been developed using the Reconfigurable Open Architecture Computing Hardware System (ROACH) built by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) Group. The testing environment also consists of various test equipment used to reproduce typical signals that a radiometer may see including those with and without RFI. The testing environment permits quick evaluations of RFI mitigation algorithms as well as show that they are implementable in hardware. The algorithm implemented is a complex signal kurtosis detector which was modeled and simulated. The complex signal kurtosis detector showed improved performance over the real kurtosis detector under certain conditions. The real kurtosis is implemented on SMAP at 24 MHz bandwidth. The complex signal kurtosis algorithm was then implemented in hardware at 200 MHz bandwidth using the ROACH. In this work, performance of the complex signal kurtosis and the real signal kurtosis are compared. Performance evaluations and comparisons in both simulation as well as experimental hardware implementations were done with the use of receiver operating characteristic (ROC) curves. The complex kurtosis algorithm has the potential to reduce data rate due to onboard processing in addition to improving RFI detection performance.

Snow Cover Mapping at the Continental to Global Scale Using Combined Visible and Passive Microwave Satellite Data

NASA Astrophysics Data System (ADS)

Armstrong, R. L.; Brodzik, M.; Savoie, M. H.

2007-12-01

Over the past several decades both visible and passive microwave satellite data have been utilized for snow mapping at the continental to global scale. Snow mapping using visible data has been based primarily on the magnitude of the surface reflectance, and in more recent cases on specific spectral signatures, while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Both passive microwave and visible data sets indicate a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible satellite data and the visible data typically show higher monthly variability. We describe the respective problems as well as the advantages and disadvantages of these two types of satellite data for snow cover mapping and demonstrate how a multi-sensor approach is optimal. For the period 1978 to present we combine data from the NOAA weekly snow charts with snow cover derived from the SMMR and SSM/I brightness temperature data. For the period since 2002 we blend NASA EOS MODIS and AMSR-E data sets. Our current product incorporates MODIS data from the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) with microwave-derived snow water equivalent (SWE) at 25 km, resulting in a blended product that includes percent snow cover in the larger grid cell whenever the microwave SWE signal is absent. Validation of AMSR-E at the brightness temperature level is provided through the comparison with data from the well-calibrated heritage SSM/I sensor over large homogeneous snow-covered surfaces (e.g. Dome C region, Antarctica). We also describe how the application of the higher frequency microwave channels (85 and 89 GHz)enhances accurate mapping of shallow and intermittent snow cover.

Remote Sensing of the Arctic Seas.

ERIC Educational Resources Information Center

Weeks, W. F.; And Others

1986-01-01

Examines remote sensing of the arctic seas by discussing: (1) passive microwave sensors; (2) active microwave sensors; (3) other types of sensors; (4) the future deployment of sensors; (5) data buoys; and (6) future endeavors. (JN)

A passive microwave technique for estimating rainfall and vertical structure information from space. Part 2: Applications to SSM/I data

NASA Technical Reports Server (NTRS)

Kummerow, Christian; Giglio, Louis

1994-01-01

A multi channel physical approach for retrieving rainfall and its vertical structure from Special Sensor Microwave/Imager (SSM/I) observations is examined. While a companion paper was devoted exclusively to the description of the algorithm, its strengths, and its limitations, the main focus of this paper is to report on the results, applicability, and expected accuraciesfrom this algorithm. Some examples are given that compare retrieved results with ground-based radar data from different geographical regions to illustrate the performance and utility of the algorithm under distinct rainfall conditions. More quantitative validation is accomplished using two months of radar data from Darwin, Australia, and the radar network over Japan. Instantaneous comparisons at Darwin indicate that root-mean-square errors for 1.25 deg areas over water are 0.09 mm/h compared to the mean rainfall value of 0.224 mm/h while the correlation exceeds 0.9. Similar results are obtained over the Japanese validation site with rms errors of 0.615 mm/h compared to the mean of 0.0880 mm/h and a correlation of 0.9. Results are less encouraging over land with root-mean-square errors somewhat larger than the mean rain rates and correlations of only 0.71 and 0.62 for Darwin and Japan, respectively. These validation studies are further used in combination with the theoretical treatment of expected accuracies developed in the companion paper to define error estimates on a broader scale than individual radar sites from which the errors may be analyzed. Comparisons with simpler techniques that are based on either emission or scattering measurements are used to illustrate the fact that the current algorithm, while better correlated with the emission methods over water, cannot be reduced to either of these simpler methods.

History of Satellite Observations of East Pacific Atmospheric Rivers

NASA Astrophysics Data System (ADS)

Vonder Haar, T. H.; Forsythe, J. M.; Seaman, C.

2017-12-01

The terms "Atmospheric River" or "Tropospheric River" were not used in refereed literature until the 1990's, although earlier works hinted at the existence of narrow corridors of moisture transport. With the advent of satellite observations in the 1960's, meteorologists began to discover the fingerprints of these phenomena via cloud observations. Early geostationary satellites depicted "cloud rivers" or "pipeline cirrus" impacting the U.S. west coast, with only indirect evidence of large water vapor transport. Routine use of passive microwave imagery to retrieve total column water vapor began in the late 1980's with the launch of the Special Sensor Microwave / Imager instrument, whose descendants continue to provide realtime monitoring of atmospheric rivers today. Passive microwave data opened the door to quantitative studies of atmospheric rivers, by providing the water vapor measurements needed to compute integrated moisture flux. Atmospheric rivers are detected in near-realtime from passive microwave water vapor products. In recent years, dedicated coastal observatories, multidecadal global water vapor data sets, cloud radars, and satellite sounding systems have begun to probe the 4-dimensional moisture structure of atmospheric rivers. The timeline of our understanding of atmospheric rivers will be presented from the standpoint of evolving satellite observing systems.

Crop moisture estimation over the southern Great Plains with dual polarization 1.66 centimeter passive microwave data from Nimbus 7

NASA Technical Reports Server (NTRS)

Mcfarland, M. J.; Harder, P. H., II; Wilke, G. D.; Huebner, G. L., Jr.

1984-01-01

Moisture content of snow-free, unfrozen soil is inferred using passive microwave brightness temperatures from the scanning multichannel microwave radiometer (SMMR) on Nimbus-7. Investigation is restricted to the two polarizations of the 1.66 cm wavelength sensor. Passive microwave estimates of soil moisture are of two basic categories; those based upon soil emissivity and those based upon the polarization of soil emission. The two methods are compared and contrasted through the investigation of 54 potential functions of polarized brightness temperatures and, in some cases, ground-based temperature measurements. Of these indices, three are selected for the estimated emissivity, the difference between polarized brightness temperatures, and the normalized polarization difference. Each of these indices is about equally effective for monitoring soil moisture. Using an antecedent precipitation index (API) as ground control data, temporal and spatial analyses show that emissivity data consistently give slightly better soil moisture estimates than depolarization data. The difference, however, is not statistically significant. It is concluded that polarization data alone can provide estimates of soil moisture in areas where the emissivity cannot be inferred due to nonavailability of surface temperature data.

Polar research from satellites

NASA Technical Reports Server (NTRS)

Thomas, Robert H.

1991-01-01

In the polar regions and climate change section, the topics of ocean/atmosphere heat transfer, trace gases, surface albedo, and response to climate warming are discussed. The satellite instruments section is divided into three parts. Part one is about basic principles and covers, choice of frequencies, algorithms, orbits, and remote sensing techniques. Part two is about passive sensors and covers microwave radiometers, medium-resolution visible and infrared sensors, advanced very high resolution radiometers, optical line scanners, earth radiation budget experiment, coastal zone color scanner, high-resolution imagers, and atmospheric sounding. Part three is about active sensors and covers synthetic aperture radar, radar altimeters, scatterometers, and lidar. There is also a next decade section that is followed by a summary and recommendations section.

Two-Level Hierarchical FEM Method for Modeling Passive Microwave Devices

NASA Astrophysics Data System (ADS)

Polstyanko, Sergey V.; Lee, Jin-Fa

1998-03-01

In recent years multigrid methods have been proven to be very efficient for solving large systems of linear equations resulting from the discretization of positive definite differential equations by either the finite difference method or theh-version of the finite element method. In this paper an iterative method of the multiple level type is proposed for solving systems of algebraic equations which arise from thep-version of the finite element analysis applied to indefinite problems. A two-levelV-cycle algorithm has been implemented and studied with a Gauss-Seidel iterative scheme used as a smoother. The convergence of the method has been investigated, and numerical results for a number of numerical examples are presented.

Multisensor Observation and Simulation of Snowfall During the 2003 Wakasa Bay Field Experiment

NASA Technical Reports Server (NTRS)

Johnson, Benjamin T.; Petty, Grant W.; Skofronick-Jackson, Gail; Wang, James W.

2005-01-01

This research seeks to assess and improve the accuracy of microphysical assumptions used in satellite passive microwave radiative transfer models and retrieval algorithms by exploiting complementary observations from satellite radiometers, such as TRMM/AMSR-E/GPM, and coincident aircraft instruments, such as the next generation precipitation radar (PR-2). We focus in particular on aircraft data obtained during the Wakasa Bay field experiment, Japan 2003, pertaining to surface snowfall events. The observations of vertical profiles of reflectivity and Doppler-derived fall speeds are used in conjunction with the radiometric measurements to identify 1-D profiles of precipitation particle types, sizes, and concentrations that are consistent with the observations.

Active and Passive Remote Sensing of Ice.

DTIC Science & Technology

1984-09-01

This is a report on the progress that has been made in the study of active and passive remote sensing of ice during the period of February 1, 1984...the emissivities as functions of viewing angles and polarizations. They are used to interpret the passive microwave remote sensing data from

Active and Passive Remote Sensing of Ice.

DTIC Science & Technology

1985-01-01

This is a report on the progress that has been made in the study of active and passive remote sensing of ice during the period of August 1, 1984...active and passive microwave remote sensing , (2) used the strong fluctuation theory and the fluctuation-dissipation theorem to calculate the brightness

Soil moisture retrieval by active/passive microwave remote sensing data

NASA Astrophysics Data System (ADS)

Wu, Shengli; Yang, Lijuan

2012-09-01

This study develops a new algorithm for estimating bare surface soil moisture using combined active / passive microwave remote sensing on the basis of TRMM (Tropical Rainfall Measuring Mission). Tropical Rainfall Measurement Mission was jointly launched by NASA and NASDA in 1997, whose main task was to observe the precipitation of the area in 40 ° N-40 ° S. It was equipped with active microwave radar sensors (PR) and passive sensor microwave imager (TMI). To accurately estimate bare surface soil moisture, precipitation radar (PR) and microwave imager (TMI) are simultaneously used for observation. According to the frequency and incident angle setting of PR and TMI, we first need to establish a database which includes a large range of surface conditions; and then we use Advanced Integral Equation Model (AIEM) to calculate the backscattering coefficient and emissivity. Meanwhile, under the accuracy of resolution, we use a simplified theoretical model (GO model) and the semi-empirical physical model (Qp Model) to redescribe the process of scattering and radiation. There are quite a lot of parameters effecting backscattering coefficient and emissivity, including soil moisture, surface root mean square height, correlation length, and the correlation function etc. Radar backscattering is strongly affected by the surface roughness, which includes the surface root mean square roughness height, surface correlation length and the correlation function we use. And emissivity is differently affected by the root mean square slope under different polarizations. In general, emissivity decreases with the root mean square slope increases in V polarization, and increases with the root mean square slope increases in H polarization. For the GO model, we found that the backscattering coefficient is only related to the root mean square slope and soil moisture when the incident angle is fixed. And for Qp Model, through the analysis, we found that there is a quite good relationship between Qpparameter and root mean square slope. So here, root mean square slope is a parameter that both models shared. Because of its big influence to backscattering and emissivity, we need to throw it out during the process of the combination of GO model and Qp model. The result we obtain from the combined model is the Fresnel reflection coefficient in the normal direction gama(0). It has a good relationship with the soil dielectric constant. In Dobson Model, there is a detailed description about Fresnel reflection coefficient and soil moisture. With the help of Dobson model and gama(0) that we have obtained, we can get the soil moisture that we want. The backscattering coefficient and emissivity data used in combined model is from TRMM/PR, TMI; with this data, we can obtain gama(0); further, we get the soil moisture by the relationship of the two parameters-- gama(0) and soil moisture. To validate the accuracy of the retrieval soil moisture, there is an experiment conducted in Tibet. The soil moisture data which is used to validate the retrieval algorithm is from GAME-Tibet IOP98 Soil Moisture and Temperature Measuring System (SMTMS). There are 9 observing sites in SMTMS to validate soil moisture. Meanwhile, we use the SMTMS soil moisture data obtained by Time Domain Reflectometer (TDR) to do the validation. And the result shows the comparison of retrieval and measured results is very good. Through the analysis, we can see that the retrieval and measured results in D66 is nearly close; and in MS3608, the measured result is a little higher than retrieval result; in MS3637, the retrieval result is a little higher than measured result. According to the analysis of the simulation results, we found that this combined active and passive approach to retrieve the soil moisture improves the retrieval accuracy.

New calibration algorithms for dielectric-based microwave moisture sensors

USDA-ARS?s Scientific Manuscript database

New calibration algorithms for determining moisture content in granular and particulate materials from measurement of the dielectric properties at a single microwave frequency are proposed. The algorithms are based on identifying empirically correlations between the dielectric properties and the par...

Australian Soil Moisture Field Experiments in Support of Soil Moisture Satellite Observations

NASA Technical Reports Server (NTRS)

Kim, Edward; Walker, Jeff; Rudiger, Christopher; Panciera, Rocco

2010-01-01

Large-scale field campaigns provide the critical fink between our understanding retrieval algorithms developed at the point scale, and algorithms suitable for satellite applications at vastly larger pixel scales. Retrievals of land parameters must deal with the substantial sub-pixel heterogeneity that is present in most regions. This is particularly the case for soil moisture remote sensing, because of the long microwave wavelengths (L-band) that are optimal. Yet, airborne L-band imagers have generally been large, heavy, and required heavy-lift aircraft resources that are expensive and difficult to schedule. Indeed, US soil moisture campaigns, have been constrained by these factors, and European campaigns have used non-imagers due to instrument and aircraft size constraints. Despite these factors, these campaigns established that large-scale soil moisture remote sensing was possible, laying the groundwork for satellite missions. Starting in 2005, a series of airborne field campaigns have been conducted in Australia: to improve our understanding of soil moisture remote sensing at large scales over heterogeneous areas. These field data have been used to test and refine retrieval algorithms for soil moisture satellite missions, and most recently with the launch of the European Space Agency's Soil Moisture Ocean Salinity (SMOS) mission, to provide validation measurements over a multi-pixel area. The campaigns to date have included a preparatory campaign in 2005, two National Airborne Field Experiments (NAFE), (2005 and 2006), two campaigns to the Simpson Desert (2008 and 2009), and one Australian Airborne Cal/val Experiment for SMOS (AACES), just concluded in the austral spring of 2010. The primary airborne sensor for each campaign has been the Polarimetric L-band Microwave Radiometer (PLMR), a 6-beam pushbroom imager that is small enough to be compatible with light aircraft, greatly facilitating the execution of the series of campaigns, and a key to their success. An L-band imaging radar is being added to the complement to provide simultaneous active-passive L-band observations, for algorithm development activities in support of NASA's upcoming Soil Moisture Active Passive (.S"M) mission. This paper will describe the campaigns, their objectives, their datasets, and some of the unique advantages of working with small/light sensors and aircraft. We will also review the main scientific findings, including improvements to the SMOS retrieval algorithm enabled by NAFE observations and the evaluation of the Simpson Desert as a calibration target for L-band satellite missions. Plans for upcoming campaigns will also be discussed.

The 20-22 January 2007 Snow Events over Canada: Microphysical Properties

NASA Technical Reports Server (NTRS)

Tao. W.K.; Shi, J.J.; Matsui, T.; Hao, A.; Lang, S.; Peters-Lidard, C.; Skofronick-Jackson, G.; Petersen, W.; Cifelli, R.; Rutledge, S.

2009-01-01

One of the grand challenges of the Global Precipitation Measurement (GPM) mission is to improve precipitation measurements in mid- and high-latitudes during cold seasons through the use of high-frequency passive microwave radiometry. Toward this end, the Weather Research and Forecasting (WRF) model with the Goddard microphysics scheme is coupled with a Satellite Data Simulation Unit (WRF-SDSU) that has been developed to facilitate over-land snowfall retrieval algorithms by providing a virtual cloud library and microwave brightness temperature (Tb) measurements consistent with the GPM Microwave Imager (GMI). This study tested the Goddard cloud microphysics scheme in WRF for snowstorm events (January 20-22, 2007) that took place over the Canadian CloudSAT/CALIPSO Validation Project (C3VP) ground site (Centre for Atmospheric Research Experiments - CARE) in Ontario, Canada. In this paper, the performance of the Goddard cloud microphysics scheme both with 2ice (ice and snow) and 3ice (ice, snow and graupel) as well as other WRF microphysics schemes will be presented. The results are compared with data from the Environment Canada (EC) King Radar, an operational C-band radar located near the CARE site. In addition, the WRF model output is used to drive the Goddard SDSU to calculate radiances and backscattering signals consistent with direct satellite observations for evaluating the model results.

Neural Network-Based Retrieval of Surface and Root Zone Soil Moisture using Multi-Frequency Remotely-Sensed Observations

NASA Astrophysics Data System (ADS)

Hamed Alemohammad, Seyed; Kolassa, Jana; Prigent, Catherine; Aires, Filipe; Gentine, Pierre

2017-04-01

Knowledge of root zone soil moisture is essential in studying plant's response to different stress conditions since plant photosynthetic activity and transpiration rate are constrained by the water available through their roots. Current global root zone soil moisture estimates are based on either outputs from physical models constrained by observations, or assimilation of remotely-sensed microwave-based surface soil moisture estimates with physical model outputs. However, quality of these estimates are limited by the accuracy of the model representations of physical processes (such as radiative transfer, infiltration, percolation, and evapotranspiration) as well as errors in the estimates of the surface parameters. Additionally, statistical approaches provide an alternative efficient platform to develop root zone soil moisture retrieval algorithms from remotely-sensed observations. In this study, we present a new neural network based retrieval algorithm to estimate surface and root zone soil moisture from passive microwave observations of SMAP satellite (L-band) and AMSR2 instrument (X-band). SMAP early morning observations are ideal for surface soil moisture retrieval. AMSR2 mid-night observations are used here as an indicator of plant hydraulic properties that are related to root zone soil moisture. The combined observations from SMAP and AMSR2 together with other ancillary observations including the Solar-Induced Fluorescence (SIF) estimates from GOME-2 instrument provide necessary information to estimate surface and root zone soil moisture. The algorithm is applied to observations from the first 18 months of SMAP mission and retrievals are validated against in-situ observations and other global datasets.

Assessing the utility of passive microwave data for Snow Water Equivalent (SWE) estimation in the Sutlej River Basin of the northwestern Himalaya

NASA Astrophysics Data System (ADS)

Brandt, T.; Bookhagen, B.; Dozier, J.

2014-12-01

Since 1978, space based passive microwave (PM) radiometers have been used to comprehensively measure Snow Water Equivalent (SWE) on a global basis. The ability of PM radiometers to directly measure SWE at high temporal frequencies offers some distinct advantages over optical remote sensors. Nevertheless, in mountainous terrain PM radiometers often struggle to accurately measure SWE because of wet snow, saturation in deep snow, forests, depth hoar and stratigraphy, variable relief, and subpixel heterogeneity inherent in large pixel sizes. The Himalaya, because of their high elevation and high relief—much above tree line—offer an opportunity to examine PM products in the mountains without the added complication of trees. The upper Sutlej River basin— the third largest Himalayan catchment—lies in the western Himalaya. The river is a tributary of the Indus River and seasonal snow constitutes a substantial part of the basin's hydrologic budget. The basin has a few surface stations and river gauges, which is unique for the region. As such, the Sutlej River basin is a good location to analyze the accuracy and effectiveness of the current National Snow and Ice Data Center's (NSIDC) standard AMSR-E/Aqua Daily SWE product in mountainous terrain. So far, we have observed that individual pixels can "flicker", i.e. fluctuate from day to day, over large parts of the basin. We consider whether this is an artifact of the algorithm or whether this is embedded in the raw brightness temperatures themselves. In addition, we examine how well the standard product registers winter storms, and how it varies over heavily glaciated pixels. Finally, we use a few common measures of algorithm performance (precision, recall and accuracy) to test how well the standard product detects the presence of snow, using optical imagery for validation. An improved understanding of the effectiveness of PM imagery in the mountains will help to clarify the technology's limits.

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 ground-based sensors is being deployed during the 2017 monsoon season to better understand possible reasons for this discrepancy.

Sea ice motion from low-resolution satellite sensors: An alternative method and its validation in the Arctic

NASA Astrophysics Data System (ADS)

Lavergne, T.; Eastwood, S.; Teffah, Z.; Schyberg, H.; Breivik, L.-A.

2010-10-01

The retrieval of sea ice motion with the Maximum Cross-Correlation (MCC) method from low-resolution (10-15 km) spaceborne imaging sensors is challenged by a dominating quantization noise as the time span of displacement vectors is shortened. To allow investigating shorter displacements from these instruments, we introduce an alternative sea ice motion tracking algorithm that builds on the MCC method but relies on a continuous optimization step for computing the motion vector. The prime effect of this method is to effectively dampen the quantization noise, an artifact of the MCC. It allows for retrieving spatially smooth 48 h sea ice motion vector fields in the Arctic. Strategies to detect and correct erroneous vectors as well as to optimally merge several polarization channels of a given instrument are also described. A test processing chain is implemented and run with several active and passive microwave imagers (Advanced Microwave Scanning Radiometer-EOS (AMSR-E), Special Sensor Microwave Imager, and Advanced Scatterometer) during three Arctic autumn, winter, and spring seasons. Ice motion vectors are collocated to and compared with GPS positions of in situ drifters. Error statistics are shown to be ranging from 2.5 to 4.5 km (standard deviation for components of the vectors) depending on the sensor, without significant bias. We discuss the relative contribution of measurement and representativeness errors by analyzing monthly validation statistics. The 37 GHz channels of the AMSR-E instrument allow for the best validation statistics. The operational low-resolution sea ice drift product of the EUMETSAT OSI SAF (European Organisation for the Exploitation of Meteorological Satellites Ocean and Sea Ice Satellite Application Facility) is based on the algorithms presented in this paper.

Foreword to the Special Issue on the 11th Specialist Meeting on Microwave Radiometry and Remote Sensing Applications (MicroRad 2010)

NASA Technical Reports Server (NTRS)

Le Vine, David M; Jackson, Thomas J.; Kim, Edward J.; Lang, Roger H.

2011-01-01

The Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad 2010) was held in Washington, DC from March 1 to 4, 2010. The objective of MicroRad 2010 was to provide an open forum to report and discuss recent advances in the field of microwave radiometry, particularly with application to remote sensing of the environment. The meeting was highly successful, with more than 200 registrations representing 48 countries. There were 80 oral presentations and more than 100 posters. MicroRad has become a venue for the microwave radiometry community to present new research results, instrument designs, and applications to an audience that is conversant in these issues. The meeting was divided into 16 sessions (listed in order of presentation): 1) SMOS Mission; 2) Future Passive Microwave Remote Sensing Missions; 3) Theory and Physical Principles of Electromagnetic Models; 4) Field Experiment Results; 5) Soil Moisture and Vegetation; 6) Snow and Cryosphere; 7) Passive/Active Microwave Remote Sensing Synergy; 8) Oceans; 9) Atmospheric Sounding and Assimilation; 10) Clouds and Precipitation; 11) Instruments and Advanced Techniques I; 12) Instruments and Advanced Techniques II; 13) Cross Calibration of Satellite Radiometers; 14) Calibration Theory and Methodology; 15) New Technologies for Microwave Radiometry; 16) Radio Frequency Interference.

High-Q microwave photonic filter with a tuned modulator.

PubMed

Capmany, J; Mora, J; Ortega, B; Pastor, D

2005-09-01

We propose the use of tuned electro-optic or electroabsorption external modulators to implement high-quality (high-Q) factor, single-bandpass photonic filters for microwave signals. Using this approach, we experimentally demonstrate a transversal finite impulse response with a Q factor of 237. This is to our knowledge the highest value ever reported for a passive finite impulse-response microwave photonic filter.

Estimating snow depth of alpine snowpack via airborne multifrequency passive microwave radiance observations: Colorado, USA

NASA Astrophysics Data System (ADS)

Kim, R. S.; Durand, M. T.; Li, D.; Baldo, E.; Margulis, S. A.; Dumont, M.; Morin, S.

2017-12-01

This paper presents a newly-proposed snow depth retrieval approach for mountainous deep snow using airborne multifrequency passive microwave (PM) radiance observation. In contrast to previous snow depth estimations using satellite PM radiance assimilation, the newly-proposed method utilized single flight observation and deployed the snow hydrologic models. This method is promising since the satellite-based retrieval methods have difficulties to estimate snow depth due to their coarse resolution and computational effort. Indeed, this approach consists of particle filter using combinations of multiple PM frequencies and multi-layer snow physical model (i.e., Crocus) to resolve melt-refreeze crusts. The method was performed over NASA Cold Land Processes Experiment (CLPX) area in Colorado during 2002 and 2003. Results showed that there was a significant improvement over the prior snow depth estimates and the capability to reduce the prior snow depth biases. When applying our snow depth retrieval algorithm using a combination of four PM frequencies (10.7,18.7, 37.0 and 89.0 GHz), the RMSE values were reduced by 48 % at the snow depth transects sites where forest density was less than 5% despite deep snow conditions. This method displayed a sensitivity to different combinations of frequencies, model stratigraphy (i.e. different number of layering scheme for snow physical model) and estimation methods (particle filter and Kalman filter). The prior RMSE values at the forest-covered areas were reduced by 37 - 42 % even in the presence of forest cover.

Surface Soil Moisture Retrieval Using SSM/I and Its Comparison with ESTAR: A Case Study Over a Grassland Region

NASA Technical Reports Server (NTRS)

Jackson, T.; Hsu, A. Y.; ONeill, P. E.

1999-01-01

This study extends a previous investigation on estimating surface soil moisture using the Special Sensor Microwave/Imager (SSM/I) over a grassland region. Although SSM/I is not optimal for soil moisture retrieval, it can under some conditions provide information. Rigorous analyses over land have been difficult due to the lack of good validation data sets. A scientific objective of the Southern Great Plains 1997 (SGP97) Hydrology Experiment was to investigate whether the retrieval algorithms for surface soil moisture developed at higher spatial resolution using truck-and aircraft-based passive microwave sensors can be extended to the coarser resolutions expected from satellite platform. With the data collected for the SGP97, the objective of this study is to compare the surface soil moisture estimated from the SSM/I data with those retrieved from the L-band Electronically Scanned Thinned Array Radiometer (ESTAR) data, the core sensor for the experiment, using the same retrieval algorithm. The results indicated that an error of estimate of 7.81% could be achieved with SSM/I data as contrasted to 2.82% with ESTAR data over three intensive sampling areas of different vegetation regimes. It confirms the results of previous study that SSM/I data can be used to retrieve surface soil moisture information at a regional scale under certain conditions.

Radio Frequency Survey of the 21-cm Wavelength(l.4 GHz) Allocation for Passive Microwave Observing

NASA Technical Reports Server (NTRS)

Piepmeier, J. R.; Midon, M.; Caroglanian, A.; Ugweje, O. C.

2003-01-01

Because of the need to develop 1.4-GHz radiometers, a set of RF surveys was conducted in and around our laboratories. In this paper, a measurement campaign and analysis of radio frequency interference (RFI) in the 21 cm wavelength allocation for passive microwave observing, was undertaken. The experimental setup and measurement procedure are outlined and measured data are interpreted. Significant signals were discovered within and surrounding the allocated spectrum at 1.4 GHz. Some implications for remote sensing are discussed.

Orbiting passive microwave sensor simulation applied to soil moisture estimation

NASA Technical Reports Server (NTRS)

Newton, R. W. (Principal Investigator); Clark, B. V.; Pitchford, W. M.; Paris, J. F.

1979-01-01

A sensor/scene simulation program was developed and used to determine the effects of scene heterogeneity, resolution, frequency, look angle, and surface and temperature relations on the performance of a spaceborne passive microwave system designed to estimate soil water information. The ground scene is based on classified LANDSAT images which provide realistic ground classes, as well as geometries. It was determined that the average sensitivity of antenna temperature to soil moisture improves as the antenna footprint size increased. Also, the precision (or variability) of the sensitivity changes as a function of resolution.

Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987-2016

NASA Astrophysics Data System (ADS)

Smith, Taylor; Bookhagen, Bodo; Rheinwalt, Aljoscha

2017-10-01

High Mountain Asia (HMA) - encompassing the Tibetan Plateau and surrounding mountain ranges - is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications - such as agriculture, drinking-water generation, and hydropower - rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season - defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade-1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002-2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers - such as the Karakoram and Kunlun Shan - see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.

High-Power, High-Temperature Superconductor Technology Development

NASA Technical Reports Server (NTRS)

Bhasin, Kul B.

2005-01-01

Since the first discovery of high-temperature superconductors (HTS) 10 years ago, the most promising areas for their applications in microwave systems have been as passive components for communication systems. Soon after the discovery, experiments showed that passive microwave circuits made from HTS material exceeded the performance of conventional devices for low-power applications and could be 10 times as small or smaller. However, for superconducting microwave components, high-power microwave applications have remained elusive until now. In 1996, DuPont and Com Dev Ltd. developed high-power superconducting materials and components for communication applications under a NASA Lewis Research Center cooperative agreement, NCC3-344 "High Power High Temperature Superconductor (HTS) Technology Development." The agreement was cost shared between the Defense Advanced Research Projects Agency's (DARPA) Technology Reinvestment Program Office and the two industrial partners. It has the following objectives: 1) Material development and characterization for high-power HTS applications; 2) Development and validation of generic high-power microwave components; 3) Development of a proof-of-concept model for a high-power six-channel HTS output multiplexer.

Signatures of Hydrometeor Species from Airborne Passive Microwave Data for Frequencies 10-183 GHz

NASA Technical Reports Server (NTRS)

Cecil, Daniel J.; Leppert, Kenneth, II

2014-01-01

There are 2 basic precipitation retrieval methods using passive microwave measurements: (1) Emission-based: Based on the tendency of liquid precipitation to cause an increase in brightness temperature (BT) primarily at frequencies below 22 GHz over a radiometrically cold background, often an ocean background (e.g., Spencer et al. 1989; Adler et al. 1991; McGaughey et al. 1996); and (2) Scattering-based: Based on the tendency of precipitation-sized ice to scatter upwelling radiation, thereby reducing the measured BT over a relatively warmer (usually land) background at frequencies generally 37 GHz (e.g., Spencer et al. 1989; Smith et al. 1992; Ferraro and Marks 1995). Passive microwave measurements have also been used to detect intense convection (e.g., Spencer and Santek 1985) and for the detection of hail (e.g., Cecil 2009; Cecil and Blankenship 2012; Ferraro et al. 2014). The Global Precipitation Measurement (GPM) mission expands upon the successful Tropical Rainfall Measurement Mission program to provide global rainfall and snowfall observations every 3 hours (Hou et al. 2014). One of the instruments on board the GPM Core Observatory is the GPM Microwave Imager (GMI) which is a conically-scanning microwave radiometer with 13 channels ranging from 10-183 GHz. Goal of this study: Determine the signatures of various hydrometeor species in terms of BTs measured at frequencies used by GMI by using data collected on 3 case days (all having intense/severe convection) during the Mid-latitude Continental Convective Clouds Experiment conducted over Oklahoma in 2011.

Seasonal Snow Extent and Snow Mass in South America Using SMMR and SSM/I Passive Microwave Data (1979-2003)

NASA Technical Reports Server (NTRS)

Foster, J. L.; Hall, D. K.; Chiu, L.; Kelly, R. E.; Powell, H.; Chiu, L.

2007-01-01

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-satellite and the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2003, both snow cover extent and snow depth (snow mass) were investigated during coldest months (May-September), primarily in the Patagonia area of Argentina and in Chile. Most of the seasonal snow in South America is in the Patagonia region of Argentina. Since winter temperatures in this region are often above freezing, the coldest winter month was found to be the month having the most extensive snow cover and also usually the month having the deepest snow cover as well. Sharp year-to-year differences were recorded using the passive microwave observations. The average snow cover extent for July, the month with the greatest average snow extent during the 25-year period of record, is 320,700 km(exp 2). In July of 1984, the average monthly snow cover was 701,250 km(exp 2) - the most extensive coverage observed between 1979 and 2003. However, in July of 1989, snow cover extent was only 120 km(exp 2). The 25-year period of record shows a sinusoidal like pattern, though there appears to be no obvious trend in either increasing or decreasing snow extent or snow mass between 1979 and 2003.

Combining Passive Microwave Sounders with CYGNSS information for improved retrievals: Observations during Hurricane Harvey

NASA Astrophysics Data System (ADS)

Schreier, M. M.

2017-12-01

The launch of CYGNSS (Cyclone Global Navigation Satellite System) has added an interesting component to satellite observations: it can provide wind speeds in the tropical area with a high repetition rate. Passive microwave sounders that are overpassing the same region can benefit from this information, when it comes to the retrieval of temperature or water profiles: the uncertainty about wind speeds has a strong impact on emissivity and reflectivity calculations with respect to surface temperature. This has strong influences on the uncertainty of retrieval of temperature and water content, especially under extreme weather conditions. Adding CYGNSS information to the retrieval can help to reduce errors and provide a significantly better sounder retrieval. Based on observations during Hurricane Harvey, we want to show the impact of CYGNSS data on the retrieval of passive microwave sensors. We will show examples on the impact on the retrieval from polar orbiting instruments, like the Advanced Technology Microwave Sounder (ATMS) and AMSU-A/B on NOAA-18 and 19. In addition we will also show the impact on retrievals from HAMSR (High Altitude MMIC Sounding Radiometer), which was flying on the Global Hawk during the EPOCH campaign. We will compare the results with other observations and estimate the impact of additional CYGNSS information on the microwave retrieval, especially on the impact in error and uncertainty reduction. We think, that a synergetic use of these different data sources could significantly help to produce better assimilation products for forecast assimilation.

Seasonal Snow Extent and Snow Mass in South America using SMMR and SSM/I Passive Microwave Data (1979-2006)

NASA Technical Reports Server (NTRS)

Foster, J. L.; Hall, D. K.; Kelly, R. E. J.; Chiu, L.

2008-01-01

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite and the Special Sensor Microwave Imagers (SSM/I) onboard Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2006, both snow cover extent and snow water equivalent (snow mass) were investigated during the coldest months (May-September), primarily in the Patagonia area of Argentina and in the Andes of Chile, Argentina and Bolivia, where most of the seasonal snow is found. Since winter temperatures in this region are often above freezing, the coldest winter month was found to be the month having the most extensive snow cover and usually the month having the deepest snow cover as well. Sharp year-to-year differences were recorded using the passive microwave observations. The average snow cover extent for July, the month with the greatest average extent during the 28-year period of record, is 321,674 km(exp 2). In July of 1984, the average monthly snow cover extent was 701,250 km(exp 2) the most extensive coverage observed between 1979 and 2006. However, in July of 1989, snow cover extent was only 120,000 km(exp 2). The 28-year period of record shows a sinusoidal like pattern for both snow cover and snow mass, though neither trend is significant at the 95% level.

NSIDC's passive microwave satellite record: The story behind 30 years of data across multiple satellite platforms

NASA Astrophysics Data System (ADS)

Scott, D. J.; Brandt, M.; Savoie, M. H.; Stewart, J. S.

2016-12-01

The National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) has been producing and distributing passive microwave snow and ice data sets from the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) for over two decades. Aboard the Defense Meteorological Satellite Program (DMSP) platforms, SSM/I and SSMIS have been operating across eight different orbiting DMSP satellites since 1987, providing an invaluable 30 year record for snow and ice climate data studies. Each sensor has performed within or beyond its expected life cycle, ultimately resulting in a transition across platforms to continue the data record. On occasion the satellites have failed unexpectedly, requiring an unplanned need for science and data management to come together and adjust production code and services to get the data back online in a timely fashion. In recent years, this has become a greater importance as climate blogging sites have increased the visibility of near-real-time passive microwave products to communicate the current changes in the Polar Regions. This presentation summarizes the history and most recent activities surrounding satellite transitions, including the scientific assessment and development required in maintaining a streamlined data record across multiple sensors. In addition, we examine challenges in long-term provenance as well as the considerations and decisions made based on value added products utilizing these data, as well as cryospheric research and general public needs.

An Intercomparison of Vegetation Products from Satellite-based Observations used for Soil Moisture Retrievals

NASA Astrophysics Data System (ADS)

Vreugdenhil, Mariette; de Jeu, Richard; Wagner, Wolfgang; Dorigo, Wouter; Hahn, Sebastian; Bloeschl, Guenter

2013-04-01

Vegetation and its water content affect active and passive microwave soil moisture retrievals and need to be taken into account in such retrieval methodologies. This study compares the vegetation parameterisation that is used in the TU-Wien soil moisture retrieval algorithm to other vegetation products, such as the Vegetation Optical Depth (VOD), Net Primary Production (NPP) and Leaf Area Index (LAI). When only considering the retrieval algorithm for active microwaves, which was developed by the TU-Wien, the effect of vegetation on the backscattering coefficient is described by the so-called slope [1]. The slope is the first derivative of the backscattering coefficient in relation to the incidence angle. Soil surface backscatter normally decreases quite rapidly with the incidence angle over bare or sparsely vegetated soils, whereas the contribution of dense vegetation is fairly uniform over a large range of incidence angles. Consequently, the slope becomes less steep with increasing vegetation. Because the slope is a derivate of noisy backscatter measurements, it is characterised by an even higher level of noise. Therefore, it is averaged over several years assuming that the state of the vegetation doesn't change inter-annually. The slope is compared to three dynamic vegetation products over Australia, the VOD, NPP and LAI. The VOD was retrieved from AMSR-E passive microwave data using the VUA-NASA retrieval algorithm and provides information on vegetation with a global coverage of approximately every two days [2]. LAI is defined as half the developed area of photosynthetically active elements of the vegetation per unit horizontal ground area. In this study LAI is used from the Geoland2 products derived from SPOT Vegetation*. The NPP is the net rate at which plants build up carbon through photosynthesis and is a model-based estimate from the BiosEquil model [3, 4]. Results show that VOD and slope correspond reasonably well over vegetated areas, whereas in arid areas, where the microwave signals mostly stem from the soil surface and deeper soil layers, they are negatively correlated. A second comparison of monthly values of both vegetation parameters to modelled NPP data shows that particularly over dry areas the VOD corresponds better to the NPP, with r=0.79 for VOD-NPP and r=-0.09 for slope-NPP. 1. Wagner, W., et al., A Study of Vegetation Cover Effects on ERS Scatterometer Data. IEEE Transactions on Geoscience and Remote Sensing, 1999. 37(2): p. 938-948. 2. Owe, M., R. de Jeu, and J. Walker, A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. Geoscience and Remote Sensing, IEEE Transactions on, 2001. 39(8): p. 1643-1654. 3. Raupach, M.R., et al., Balances of Water, Carbon, Nitrogen and Phosphorus in Australian Landscapes: (1) Project Description and Results, 2001, Sustainable Minerals Institute, CSIRO Land and Water. 4. Raupach, M.R., et al., Balances of Water, Carbon, Nitrogen and Phosporus in Australian Landscapes: (2) Model Formulation and Testing, 2001, Sustainable Minerals Institute, CSIRO Land and Water. * These products are the joint property of INRA, CNES and VITO under copyright of Geoland2. They are generated from the SPOT VEGETATION data under copyright CNES and distribution by VITO.

Microwave signatures of ice hydrometeors from ground-based observations above Summit, Greenland

DOE PAGES

Pettersen, Claire; Bennartz, Ralf; Kulie, Mark S.; ...

2016-04-15

Multi-instrument, ground-based measurements provide unique and comprehensive data sets of the atmosphere for a specific location over long periods of time and resulting data compliment past and existing global satellite observations. Our paper explores the effect of ice hydrometeors on ground-based, high-frequency passive microwave measurements and attempts to isolate an ice signature for summer seasons at Summit, Greenland, from 2010 to 2013. Furthermore, data from a combination of passive microwave, cloud radar, radiosonde, and ceilometer were examined to isolate the ice signature at microwave wavelengths. By limiting the study to a cloud liquid water path of 40 g m -2more » or less, the cloud radar can identify cases where the precipitation was dominated by ice. These cases were examined using liquid water and gas microwave absorption models, and brightness temperatures were calculated for the high-frequency microwave channels: 90, 150, and 225GHz. By comparing the measured brightness temperatures from the microwave radiometers and the calculated brightness temperature using only gas and liquid contributions, any residual brightness temperature difference is due to emission and scattering of microwave radiation from the ice hydrometeors in the column. The ice signature in the 90, 150, and 225 GHz channels for the Summit Station summer months was isolated. Then, this measured ice signature was compared to an equivalent brightness temperature difference calculated with a radiative transfer model including microwave single-scattering properties for several ice habits. Furthermore, initial model results compare well against the 4 years of summer season isolated ice signature in the high-frequency microwave channels.« less

Investigation of remote sensing techniques of measuring soil moisture

NASA Technical Reports Server (NTRS)

Newton, R. W. (Principal Investigator); Blanchard, A. J.; Nieber, J. L.; Lascano, R.; Tsang, L.; Vanbavel, C. H. M.

1981-01-01

Major activities described include development and evaluation of theoretical models that describe both active and passive microwave sensing of soil moisture, the evaluation of these models for their applicability, the execution of a controlled field experiment during which passive microwave measurements were acquired to validate these models, and evaluation of previously acquired aircraft microwave measurements. The development of a root zone soil water and soil temperature profile model and the calibration and evaluation of gamma ray attenuation probes for measuring soil moisture profiles are considered. The analysis of spatial variability of soil information as related to remote sensing is discussed as well as the implementation of an instrumented field site for acquisition of soil moisture and meteorologic information for use in validating the soil water profile and soil temperature profile models.

Passive microwave (SSM/I) satellite predictions of valley glacier hydrology, Matanuska Glacier, Alaska

USGS Publications Warehouse

Kopczynski, S.E.; Ramage, J.; Lawson, D.; Goetz, S.; Evenson, E.; Denner, J.; Larson, G.

2008-01-01

We advance an approach to use satellite passive microwave observations to track valley glacier snowmelt and predict timing of spring snowmelt-induced floods at the terminus. Using 37 V GHz brightness temperatures (Tb) from the Special Sensor Microwave hnager (SSM/I), we monitor snowmelt onset when both Tb and the difference between the ascending and descending overpasses exceed fixed thresholds established for Matanuska Glacier. Melt is confirmed by ground-measured air temperature and snow-wetness, while glacier hydrologic responses are monitored by a stream gauge, suspended-sediment sensors and terminus ice velocity measurements. Accumulation area snowmelt timing is correlated (R2 = 0.61) to timing of the annual snowmelt flood peak and can be predicted within ??5 days. Copyright 2008 by the American Geophysical Union.

ASPECTS OF ARCTIC SEA ICE OBSERVABLE BY SEQUENTIAL PASSIVE MICROWAVE OBSERVATIONS FROM THE NIMBUS-5 SATELLITE.

USGS Publications Warehouse

Campbell, William J.; Gloersen, Per; Zwally, H. Jay; ,

1984-01-01

Observations made from 1972 to 1976 with the Electrically Scanning Microwave Radiometer on board the Nimbus-5 satellite provide sequential synoptic information of the Arctic sea ice cover. This four-year data set was used to construct a fairly continuous series of three-day average 19-GHz passive microwave images which has become a valuable source of polar information, yielding many anticipated and unanticipated discoveries of the sea ice canopy observed in its entirety through the clouds and during the polar night. Short-term, seasonal, and annual variations of key sea ice parameters, such as ice edge position, ice types, mixtures of ice types, ice concentrations, and snow melt on the ice, are presented for various parts of the Arctic.

High Resolution Freeze and Thaw States Detection Using Combination of Sentinel 1A SAR and Passive Microwave Measurements

NASA Astrophysics Data System (ADS)

Azarderakhsh, M.; McDonald, K. C.; Norouzi, H.; Rebolledo, M. A.; Prakash, S.

2017-12-01

The freeze and thaw (FT) cycles in high-latitude regions have great impact on many biogeochemical transitions, hydrology and ecosystem especially in wetland areas. Passive and active microwave remote sensing data from satellite observations have been deployed in the past to define the status of the surface in terms of freeze and thaw. While many progresses have been made in this field, the limitations attached to such observations have hindered our ability to fully predict the change of surface state in the scale that is appropriate for the aforementioned applications. The transition between freeze and thaw states may occur frequently (even within a day) especially during shifts from cold to warm seasons and vice versa. Passive microwave sensors have different acquisition times, and data fusion of these sensors may provide a complete diurnal variation estimate of FT states. However, the coarse spatial resolution of these measurements may undermine their applicability. However, active microwave backscatter measurements from sensors such as Sentinel 1A and the Advanced Land Observing Satellite Phased Array L-Band SAR (ALOS PALSAR) can deliver high resolution information about wetlands and FT status. In this project, Synthetic Aperture Radar (SAR) c-band backscatter data from Sentinel 1 from April 2014 to June 2017 are deployed to detect high resolution freeze/thaw states and wetland areas. The contrasts between frozen and thawed seasons are used to define FT states after performing required radiometric corrections and calibrations. A method based on phase changes in polarized images is developed for different land cover types to maximize the accuracy of the detections. The aggregated (up-scaled) estimates from active measurements are compared to passive microwave-based FT product. The results of this method reveal that the estimates are relatively in good agreement with SNOw TELemetry (SNOTEL) ground measurements. Finally, a downscaling method is tried to link passive emissivity-based FT product to high resolution active FT estimates to increase the temporal frequency of the high-resolution Sentinel data. The results of this study contribute to better understanding sources of positive carbon and methane (CH4) feedback to the atmosphere.

Dual-mode microwave system to enhance early detection of cancer

NASA Technical Reports Server (NTRS)

Carr, K. L.; El-Mahdi, A. M.; Shaeffer, J.

1981-01-01

A dual-mode microwave system has been developed that will permit early detection of cancer. The system combines the use of the passive microwave radiometer with an active transmitter. The active transmitter will provide localized heating to enhance early detection by taking advantage of the differential heating (i.e., tumor temperature with respect to surrounding tissue) associated with the thermal characteristics of tumors.

Microwave remote sensing of snow experiment description and preliminary results

NASA Technical Reports Server (NTRS)

Ulaby, F. T. (Principal Investigator); Stiles, W. H.; Hanson, B. C.

1977-01-01

The active and passive microwave responses to snow were investigated at a site near Steamboat Springs, Colorado during the February and March winter months. The microwave equipment was mounted atop truck-mounted booms. Data were acquired at numerous frequencies, polarizations, and angles of incidence for a variety of snow conditions. The experiment description, the characteristics of the microwave and ground truth instruments, and the results of a preliminary analysis of a small portion of the total data volume acquired in Colorado are documented.

An Evaluation of Antarctica as a Calibration Target for Passive Microwave Satellite Missions with Climate Data Record Applications

NASA Technical Reports Server (NTRS)

Kim, Edward

2011-01-01

Passive microwave remote sensing at L-band (1.4 GHz) is sensitive to soil moisture and sea surface salinity, both important climate variables. Science studies involving these variables can now take advantage of new satellite L-band observations. The first mission with regular global passive microwave observations at L-band is the European Space Agency's Soil Moisture and Ocean Salinity (SMOS), launched November, 2009. A second mission, NASA's Aquarius, was launched June, 201 I. A third mission, NASA's Soil Moisture Active Passive (SMAP) is scheduled to launch in 2014. Together, these three missions may provide a decade-long data record-provided that they are intercalibrated. The intercalibration is best performed at the radiance (brightness temperature) level, and Antarctica is proving to be a key calibration target. However, Antarctica has thus far not been fully characterized as a potential target. This paper will present evaluations of Antarctica as a microwave calibration target for the above satellite missions. Preliminary analyses have identified likely target areas, such as the vicinity of Dome-C and larger areas within East Antarctica. Physical sources of temporal and spatial variability of polar firn are key to assessing calibration uncertainty. These sources include spatial variability of accumulation rate, compaction, surface characteristics (dunes, micro-topography), wind patterns, and vertical profiles of density and temperature. Using primarily SMOS data, variability is being empirically characterized and attempts are being made to attribute observed variability to physical sources. One expected outcome of these studies is the potential discovery of techniques for remotely sensing--over all of Antarctica-parameters such as surface temperature.

An Evaluation of Antarctica as a Calibration Target for Passive Microwave Satellite Missions

NASA Technical Reports Server (NTRS)

Kim, Edward

2012-01-01

Passive microwave remote sensing at L-band (1.4 GHz) is sensitive to soil moisture and sea surface salinity, both important climate variables. Science studies involving these variables can now take advantage of new satellite L-band observations. The first mission with regular global passive microwave observations at L-band is the European Space Agency's Soil Moisture and Ocean Salinity (SMOS), launched November, 2009. A second mission, NASA's Aquarius, was launched June, 201l. A third mission, NASA's Soil Moisture Active Passive (SMAP) is scheduled to launch in 2014. Together, these three missions may provide a decade-long data record -- provided that they are intercalibrated. The intercalibration is best performed at the radiance (brightness temperature) level, and Antarctica is proving to be a key calibration target. However, Antarctica has thus far not been fully characterized as a potential target. This paper will present evaluations of Antarctica as a microwave calibration target for the above satellite missions. Preliminary analyses have identified likely target areas, such as the vicinity of Dome-C and larger areas within East Antarctica. Physical sources of temporal and spatial variability of polar firn are key to assessing calibration uncertainty. These sources include spatial variability of accumulation rate, compaction, surface characteristics (dunes, micro-topography), wind patterns, and vertical profiles of density and temperature. Using primarily SMOS data, variability is being empirically characterized and attempts are being made to attribute observed variability to physical sources. One expected outcome of these studies is the potential discovery of techniques for remotely sensing--over all of Antarctica--parameters such as surface temperature.

Investigation of radio astronomy image processing techniques for use in the passive millimetre-wave security screening environment

NASA Astrophysics Data System (ADS)

Taylor, Christopher T.; Hutchinson, Simon; Salmon, Neil A.; Wilkinson, Peter N.; Cameron, Colin D.

2014-06-01

Image processing techniques can be used to improve the cost-effectiveness of future interferometric Passive MilliMetre Wave (PMMW) imagers. The implementation of such techniques will allow for a reduction in the number of collecting elements whilst ensuring adequate image fidelity is maintained. Various techniques have been developed by the radio astronomy community to enhance the imaging capability of sparse interferometric arrays. The most prominent are Multi- Frequency Synthesis (MFS) and non-linear deconvolution algorithms, such as the Maximum Entropy Method (MEM) and variations of the CLEAN algorithm. This investigation focuses on the implementation of these methods in the defacto standard for radio astronomy image processing, the Common Astronomy Software Applications (CASA) package, building upon the discussion presented in Taylor et al., SPIE 8362-0F. We describe the image conversion process into a CASA suitable format, followed by a series of simulations that exploit the highlighted deconvolution and MFS algorithms assuming far-field imagery. The primary target application used for this investigation is an outdoor security scanner for soft-sided Heavy Goods Vehicles. A quantitative analysis of the effectiveness of the aforementioned image processing techniques is presented, with thoughts on the potential cost-savings such an approach could yield. Consideration is also given to how the implementation of these techniques in CASA might be adapted to operate in a near-field target environment. This may enable a much wider usability by the imaging community outside of radio astronomy and thus would be directly relevant to portal screening security systems in the microwave and millimetre wave bands.

Global Precipitation Measurement, Validation, and Applications Integrated Hydrologic Validation to Improve Physical Precipitation Retrievals for GPM

NASA Technical Reports Server (NTRS)

Peters-Lidar, Christa D.; Tian, Yudong; Kenneth, Tian; Harrison, Kenneth; Kumar, Sujay

2011-01-01

Land surface modeling and data assimilation can provide dynamic land surface state variables necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in the Global Precipitation Measurement Mission (GPM), is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. In order to investigate the robustness of both the land surface model states and the microwave emissivity and forward radiative transfer models, we have undertaken a multi-site investigation as part of the NASA Precipitation Measurement Missions (PMM) Land Surface Characterization Working Group. Specifically, we will demonstrate the performance of the Land Information System (LIS; http://lis.gsfc.nasa.gov; Peters-Lidard et aI., 2007; Kumar et al., 2006) coupled to the Joint Center for Satellite Data Assimilation (JCSDA's) Community Radiative Transfer Model (CRTM; Weng, 2007; van Deist, 2009). The land surface is characterized by complex physical/chemical constituents and creates temporally and spatially heterogeneous surface properties in response to microwave radiation scattering. The uncertainties in surface microwave emission (both surface radiative temperature and emissivity) and very low polarization ratio are linked to difficulties in rainfall detection using low-frequency passive microwave sensors (e.g.,Kummerow et al. 2001). Therefore, addressing these issues is of utmost importance for the GPM mission. There are many approaches to parameterizing land surface emission and radiative transfer, some of which have been customized for snow (e.g., the Helsinki University of Technology or HUT radiative transfer model;) and soil moisture (e.g., the Land Surface Microwave Emission Model or LSMEM).

Monitoring Freeze-Thaw States in the Pan-Arctic: Application of Microwave Remote Sensing to Monitoring Hydrologic and Ecological Processes

NASA Astrophysics Data System (ADS)

McDonald, K. C.; Kimball, J. S.

2004-12-01

The transition of the landscape between predominantly frozen and non-frozen conditions in seasonally frozen environments impacts climate, hydrological, ecological and biogeochemical processes profoundly. Satellite microwave remote sensing is uniquely capable of detecting and monitoring a range of related biophysical processes associated with the measurement of landscape freeze/thaw status. We present the development, physical basis, current techniques and selected hydrological applications of satellite-borne microwave remote sensing of landscape freeze/thaw states for the terrestrial cryosphere. Major landscape hydrological processes embracing the remotely-sensed freeze/thaw signal include timing and spatial dynamics of seasonal snowmelt and associated soil thaw, runoff generation and flooding, ice breakup in large rivers and lakes, and timing and length of vegetation growing seasons and associated productivity and trace gas exchange. Employing both active and passive microwave sensors, we apply a selection of temporal change classification algorithms to examine a variety of hydrologic processes. We investigate contemporaneous and retrospective applications of the QuikSCAT scatterometer, and the SSM/I and SMMR radiometers to this end. Results illustrate the strong correspondence between regional thawing, seasonal ice break up for rivers, and the springtime pulse in river flow. We present the physical principles of microwave sensitivity to landscape freeze/thaw state, recent progress in applying these principles toward satellite remote sensing of freeze/thaw processes over broad regions, and potential for future global monitoring of this significant phenomenon of the global cryosphere. This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, and at the University of Montana, Missoula, under contract to the National Aeronautics and Space Administration.

Impact of Uncertainty in the Drop Size Distribution on Oceanic Rainfall Retrievals From Passive Microwave Observations

NASA Technical Reports Server (NTRS)

Wilheit, Thomas T.; Chandrasekar, V.; Li, Wanyu

2007-01-01

The variability of the drop size distribution (DSD) is one of the factors that must be considered in understanding the uncertainties in the retrieval of oceanic precipitation from passive microwave observations. Here, we have used observations from the Precipitation Radar on the Tropical Rainfall Measuring Mission spacecraft to infer the relationship between the DSD and the rain rate and the variability in this relationship. The impact on passive microwave rain rate retrievals varies with the frequency and rain rate. The total uncertainty for a given pixel can be slightly larger than 10% at the low end (ca. 10 GHz) of frequencies commonly used for this purpose and smaller at higher frequencies (up to 37 GHz). Since the error is not totally random, averaging many pixels, as in a monthly rainfall total, should roughly halve this uncertainty. The uncertainty may be lower at rain rates less than about 30 mm/h, but the lack of sensitivity of the surface reference technique to low rain rates makes it impossible to tell from the present data set.

Lunar Heat Flux Measurements Enabled by a Microwave Radiometer Aboard the Deep Space Gateway

NASA Astrophysics Data System (ADS)

Siegler, M.; Ruf, C.; Putzig, N.; Morgan, G.; Hayne, P.; Paige, D.; Nagihara, S.; Weber, R.

2018-02-01

We would like to present a concept to use the Deep Space Gateway as a platform for constraining the geothermal heat production, surface, and near-surface rocks, and dielectric properties of the Moon from orbit with passive microwave radiometery.

IRIS - A concept for microwave sensing of soil moisture and ocean salinity

NASA Technical Reports Server (NTRS)

Moghaddam, M.; Njoku, E.

1997-01-01

A concept is described for passive microwave sensing of soil moisture and ocean salinity from space. The Inflatable Radiometric Imaging System (IRIS) makes use of a large-diameter, offset-fed, parabolic-torus antenna with multiple feeds, in a conical pushbroom configuration.

Passive microwave structure of severe tornadic storms on 16 November 1987

NASA Technical Reports Server (NTRS)

Heymsfield, Gerald M.; Fulton, Richard

1994-01-01

Passive microwave observations using the Special Sensor Microwave/Imager (SSM/I) are presented for severe tornadic storms in the lower midwestern United States on 16 November 1987. These measurements are compared with Geostationary Operational Environmental Satellite infrared (IR) measurements for the same case. The IR observations had a classic 'V' cold feature commonly associated with severe Midwest thunderstorms. The minimum microwave brightness temperatures at 86 GHz, which primarily respond to ice scattering by larger ice particles, were located in the convective region and the warm interior of the anvil top, between the arms of the IR V feature. The interior warm region was the only portion of the entire anvil region that had high 86-GHz polarization difference temperatures. Microphysical implications of these multispectral observations are discussed. The observations suggest that there are large variations of ice microphysical characteristics spatially and vertically in the anvil region. These observations are discussed in the context of previous dynamical and microphysical hypotheses on the IR V feature.

Determination of effective droplet radius and optical depth of liquid water clouds over a tropical site in northern Thailand using passive microwave soundings, aircraft measurements and spectral irradiance data

NASA Astrophysics Data System (ADS)

Nimnuan, P.; Janjai, S.; Nunez, M.; Pratummasoot, N.; Buntoung, S.; Charuchittipan, D.; Chanyatham, T.; Chantraket, P.; Tantiplubthong, N.

2017-08-01

This paper presents an algorithm for deriving the effective droplet radius and optical depth of liquid water clouds using ground-based measurements, aircraft observations and an adiabatic model of cloud liquid water. The algorithm derives cloud effective radius and cloud optical depth over a tropical site at Omkoi (17.80°N, 98.43°E), Thailand. Monthly averages of cloud optical depth are highest in April (54.5), which is the month with the lowest average cloud effective radius (4.2 μm), both occurring before the start of the rainy season and at the end of the high contamination period. By contrast, the monsoon period extending from May to October brings higher cloud effective radius and lower cloud optical depth to the region on average. At the diurnal scale there is a gradual increase in average cloud optical depth and decrease in cloud effective radius as the day progresses.

Assessing the relationship between microwave vegetation optical depth and gross primary production

NASA Astrophysics Data System (ADS)

Teubner, Irene E.; Forkel, Matthias; Jung, Martin; Liu, Yi Y.; Miralles, Diego G.; Parinussa, Robert; van der Schalie, Robin; Vreugdenhil, Mariette; Schwalm, Christopher R.; Tramontana, Gianluca; Camps-Valls, Gustau; Dorigo, Wouter A.

2018-03-01

At the global scale, the uptake of atmospheric carbon dioxide by terrestrial ecosystems through photosynthesis is commonly estimated through vegetation indices or biophysical properties derived from optical remote sensing data. Microwave observations of vegetated areas are sensitive to different components of the vegetation layer than observations in the optical domain and may therefore provide complementary information on the vegetation state, which may be used in the estimation of Gross Primary Production (GPP). However, the relation between GPP and Vegetation Optical Depth (VOD), a biophysical quantity derived from microwave observations, is not yet known. This study aims to explore the relationship between VOD and GPP. VOD data were taken from different frequencies (L-, C-, and X-band) and from both active and passive microwave sensors, including the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS) mission, the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E) and a merged VOD data set from various passive microwave sensors. VOD data were compared against FLUXCOM GPP and Solar-Induced chlorophyll Fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). FLUXCOM GPP estimates are based on the upscaling of flux tower GPP observations using optical satellite data, while SIF observations present a measure of photosynthetic activity and are often used as a proxy for GPP. For relating VOD to GPP, three variables were analyzed: original VOD time series, temporal changes in VOD (ΔVOD), and positive changes in VOD (ΔVOD≥0). Results show widespread positive correlations between VOD and GPP with some negative correlations mainly occurring in dry and wet regions for active and passive VOD, respectively. Correlations between VOD and GPP were similar or higher than between VOD and SIF. When comparing the three variables for relating VOD to GPP, correlations with GPP were higher for the original VOD time series than for ΔVOD or ΔVOD≥0 in case of sparsely to moderately vegetated areas and evergreen forests, while the opposite was true for deciduous forests. Results suggest that original VOD time series should be used jointly with changes in VOD for the estimation of GPP across biomes, which may further benefit from combining active and passive VOD data.

Classification of Active Microwave and Passive Optical Data Based on Bayesian Theory and Mrf

NASA Astrophysics Data System (ADS)

Yu, F.; Li, H. T.; Han, Y. S.; Gu, H. Y.

2012-08-01

A classifier based on Bayesian theory and Markov random field (MRF) is presented to classify the active microwave and passive optical remote sensing data, which have demonstrated their respective advantages in inversion of surface soil moisture content. In the method, the VV, VH polarization of ASAR and all the 7 TM bands are taken as the input of the classifier to get the class labels of each pixel of the images. And the model is validated for the necessities of integration of TM and ASAR, it shows that, the total precision of classification in this paper is 89.4%. Comparing with the classification with single TM, the accuracy increase 11.5%, illustrating that synthesis of active and passive optical remote sensing data is efficient and potential in classification.

Mapping global surface water inundation dynamics using synergistic information from SMAP, AMSR2 and Landsat

NASA Astrophysics Data System (ADS)

Du, J.; Kimball, J. S.; Galantowicz, J. F.; Kim, S.; Chan, S.; Reichle, R. H.; Jones, L. A.; Watts, J. D.

2017-12-01

A method to monitor global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fwLBand) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency Tb observations from AMSR2. The resulting fwLBand global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The fwLBand annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly fwLBand averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable fwLBand performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) fwLBand results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m fwLBand retrievals showed favourable classification accuracy for water (commission error 31.84%; omission error 28.08%) and land (commission error 0.82%; omission error 0.99%) and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new fwLBand algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics, potentially benefiting hydrological monitoring, flood assessments, and global climate and carbon modeling.

Air-sea interaction with SSM/I and altimeter

NASA Technical Reports Server (NTRS)

1985-01-01

A number of important developments in satellite remote sensing techniques have occurred recently which offer the possibility of studying over vast areas of the ocean the temporally evolving energy exchange between the ocean and the atmosphere. Commencing in spring of 1985, passive and active microwave sensors that can provide valuable data for scientific utilization will start to become operational on Department of Defense (DOD) missions. The passive microwave radiometer can be used to estimate surface wind speed, total air column humidity, and rain rate. The active radar, or altimeter, senses surface gravity wave height and surface wind speed.

A micro-Doppler sonar for acoustic surveillance in sensor networks

NASA Astrophysics Data System (ADS)

Zhang, Zhaonian

Wireless sensor networks have been employed in a wide variety of applications, despite the limited energy and communication resources at each sensor node. Low power custom VLSI chips implementing passive acoustic sensing algorithms have been successfully integrated into an acoustic surveillance unit and demonstrated for detection and location of sound sources. In this dissertation, I explore active and passive acoustic sensing techniques, signal processing and classification algorithms for detection and classification in a multinodal sensor network environment. I will present the design and characterization of a continuous-wave micro-Doppler sonar to image objects with articulated moving components. As an example application for this system, we use it to image gaits of humans and four-legged animals. I will present the micro-Doppler gait signatures of a walking person, a dog and a horse. I will discuss the resolution and range of this micro-Doppler sonar and use experimental results to support the theoretical analyses. In order to reduce the data rate and make the system amenable to wireless sensor networks, I will present a second micro-Doppler sonar that uses bandpass sampling for data acquisition. Speech recognition algorithms are explored for biometric identifications from one's gait, and I will present and compare the classification performance of the two systems. The acoustic micro-Doppler sonar design and biometric identification results are the first in the field as the previous work used either video camera or microwave technology. I will also review bearing estimation algorithms and present results of applying these algorithms for bearing estimation and tracking of moving vehicles. Another major source of the power consumption at each sensor node is the wireless interface. To address the need of low power communications in a wireless sensor network, I will also discuss the design and implementation of ultra wideband transmitters in a three dimensional silicon on insulator process. Lastly, a prototype of neuromorphic interconnects using ultra wideband radio will be presented.

Tropical Cyclone Intensity and Position Analysis Using Passive Microwave Imager and Sounder Data

DTIC Science & Technology

2015-03-26

NPP) Advanced Technology Microwave Sounder (ATMS) for a sample of 28 North Atlantic storms from the 2011 through 2013 TC seasons . Using a stepwise...58 27. NOAA NHC 2011 TC Season Tracks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 28...per Season and TCs with Aircraft Reconnaissance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

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

Pettersen, Claire; Bennartz, Ralf; Kulie, Mark S.

Multi-instrument, ground-based measurements provide unique and comprehensive data sets of the atmosphere for a specific location over long periods of time and resulting data compliment past and existing global satellite observations. Our paper explores the effect of ice hydrometeors on ground-based, high-frequency passive microwave measurements and attempts to isolate an ice signature for summer seasons at Summit, Greenland, from 2010 to 2013. Furthermore, data from a combination of passive microwave, cloud radar, radiosonde, and ceilometer were examined to isolate the ice signature at microwave wavelengths. By limiting the study to a cloud liquid water path of 40 g m -2more » or less, the cloud radar can identify cases where the precipitation was dominated by ice. These cases were examined using liquid water and gas microwave absorption models, and brightness temperatures were calculated for the high-frequency microwave channels: 90, 150, and 225GHz. By comparing the measured brightness temperatures from the microwave radiometers and the calculated brightness temperature using only gas and liquid contributions, any residual brightness temperature difference is due to emission and scattering of microwave radiation from the ice hydrometeors in the column. The ice signature in the 90, 150, and 225 GHz channels for the Summit Station summer months was isolated. Then, this measured ice signature was compared to an equivalent brightness temperature difference calculated with a radiative transfer model including microwave single-scattering properties for several ice habits. Furthermore, initial model results compare well against the 4 years of summer season isolated ice signature in the high-frequency microwave channels.« less

Passive microwave studies of frozen lakes

NASA Technical Reports Server (NTRS)

Hall, D. K.; Foster, J. L.; Rango, A.; Chang, A. T. C.

1978-01-01

Lakes of various sizes, depths and ice thicknesses in Alaska, Utah and Colorado were overflown with passive microwave sensors providing observations at several wavelengths. A layer model is used to calculate the microwave brightness temperature, T sub B (a function of the emissivity and physical temperatures of the object), of snowcovered ice underlain with water. Calculated T sub B's are comparable to measured T sub B's. At short wavelengths, e.g., 0.8 cm, T sub B data provide information on the near surface properties of ice covered lakes where the long wavelength, 21.0 cm, observations sense the entire thickness of ice including underlying water. Additionally, T sub B is found to increase with ice thickness. 1.55 cm observations on Chandalar Lake in Alaska show a T sub B increase of 38 K with an approximate 124 cm increase in ice thickness.

Determination of precipitation profiles from airborne passive microwave radiometric measurements

NASA Technical Reports Server (NTRS)

Kummerow, Christian; Hakkarinen, Ida M.; Pierce, Harold F.; Weinman, James A.

1991-01-01

This study presents the first quantitative retrievals of vertical profiles of precipitation derived from multispectral passive microwave radiometry. Measurements of microwave brightness temperature (Tb) obtained by a NASA high-altitude research aircraft are related to profiles of rainfall rate through a multichannel piecewise-linear statistical regression procedure. Statistics for Tb are obtained from a set of cloud radiative models representing a wide variety of convective, stratiform, and anvil structures. The retrieval scheme itself determines which cloud model best fits the observed meteorological conditions. Retrieved rainfall rate profiles are converted to equivalent radar reflectivity for comparison with observed reflectivities from a ground-based research radar. Results for two case studies, a stratiform rain situation and an intense convective thunderstorm, show that the radiometrically derived profiles capture the major features of the observed vertical structure of hydrometer density.

Ice water path estimation and characterization using passive microwave radiometry

NASA Technical Reports Server (NTRS)

Vivekanandan, J.; Turk, J.; Bringi, V. N.

1991-01-01

Model computations of top-of-atmospheric microwave brightness temperatures T(B) from layers of precipitation-sized ice of variable bulk density and ice water content (IWC) are presented. It is shown that the 85-GHz T(B) depends essentially on the ice optical thickness. The results demonstrate the potential usefulness of scattering-based channels for characterizing the ice phase and suggest a top-down methodology for retrieval of cloud vertical structure and precipitation estimation from multifrequency passive microwave measurements. Attention is also given to radiative transfer model results based on the multiparameter radar data initialization from the Cooperative Huntsville Meteorological Experiment (COHMEX) in northern Alabama. It is shown that brightness temperature warming effects due to the inclusion of a cloud liquid water profile are especially significant at 85 GHz during later stages of cloud evolution.

Round Robin evaluation of soil moisture retrieval models for the MetOp-A ASCAT Instrument

NASA Astrophysics Data System (ADS)

Gruber, Alexander; Paloscia, Simonetta; Santi, Emanuele; Notarnicola, Claudia; Pasolli, Luca; Smolander, Tuomo; Pulliainen, Jouni; Mittelbach, Heidi; Dorigo, Wouter; Wagner, Wolfgang

2014-05-01

Global soil moisture observations are crucial to understand hydrologic processes, earth-atmosphere interactions and climate variability. ESA's Climate Change Initiative (CCI) project aims to create a global consistent long-term soil moisture data set based on the merging of the best available active and passive satellite-based microwave sensors and retrieval algorithms. Within the CCI, a Round Robin evaluation of existing retrieval algorithms for both active and passive instruments was carried out. In this study we present the comparison of five different retrieval algorithms covering three different modelling principles applied to active MetOp-A ASCAT L1 backscatter data. These models include statistical models (Bayesian Regression and Support Vector Regression, provided by the Institute for Applied Remote Sensing, Eurac Research Viale Druso, Italy, and an Artificial Neural Network, provided by the Institute of Applied Physics, CNR-IFAC, Italy), a semi-empirical model (provided by the Finnish Meteorological Institute), and a change detection model (provided by the Vienna University of Technology). The algorithms were applied on L1 backscatter data within the period of 2007-2011, resampled to a 12.5 km grid. The evaluation was performed over 75 globally distributed, quality controlled in situ stations drawn from the International Soil Moisture Network (ISMN) using surface soil moisture data from the Global Land Data Assimilation System (GLDAS-) Noah land surface model as second independent reference. The temporal correlation between the data sets was analyzed and random errors of the the different algorithms were estimated using the triple collocation method. Absolute soil moisture values as well as soil moisture anomalies were considered including both long-term anomalies from the mean seasonal cycle and short-term anomalies from a five weeks moving average window. Results show a very high agreement between all five algorithms for most stations. A slight vegetation dependency of the errors and a spatial decorrelation of the performance patterns of the different algorithms was found. We conclude that future research should focus on understanding, combining and exploiting the advantages of all available modelling approaches rather than trying to optimize one approach to fit every possible condition.

Towards Understanding the Timing and Frequency of Rain-on-Snow (ROS) Events in Alaska

NASA Astrophysics Data System (ADS)

Pan, C.; Kirchner, P. B.; Kimball, J. S.; Kim, Y.; Kamp, U.

2017-12-01

Rain-on-snow (ROS) events affect ecosystem processes at multiple spatial and temporal scales including hydrology, carbon cycling, wildlife movement and human transportation and result in marked changes to snowpack processes including enhanced snow melt, surface albedo and energy balance. Changes in the surface structure of the snowpack are visible through optical remote sensing and changes in the relative content and distribution of water, air and ice in the snowpack are detectable using passive microwave remote sensing. This project aims to develop ROS products to elucidate changes in frequency and distribution in ROS events using satellite data products derived from both optical and passive microwave satellite records. To detect ROS events, we use downscaled brightness temperature measurements derived from vertical and horizontal polarizations at 19 and 37 GHz from the Advanced Microwave Scanning Radiometer (AMSR-E/2) passive microwave satellites. Preliminary results indicate an overall classification accuracy of 77.6% relative to in situ weather observations including surface air temperature, precipitation, and snow depth. ROS events are spatially distributed largely to elevations below 500 m and occur most frequently on northern to western aspects in addition to southeastern. Regional ROS hot spots occur in southwest Alaska characterized by warmer climates and transient snowcover. The seasonal timing of ROS events indicates increasing frequency during the fall and spring months.

Thin Sea-Ice Thickness as Inferred from Passive Microwave and In Situ Observations

NASA Technical Reports Server (NTRS)

Naoki, Kazuhiro; Ukita, Jinro; Nishio, Fumihiko; Nakayama, Masashige; Comiso, Josefino C.; Gasiewski, Al

2007-01-01

Since microwave radiometric signals from sea-ice strongly reflect physical conditions of a layer near the ice surface, a relationship of brightness temperature with thickness is possible especially during the early stages of ice growth. Sea ice is most saline during formation stage and as the salinity decreases with time while at the same time the thickness of the sea ice increases, a corresponding change in the dielectric properties and hence the brightness temperature may occur. This study examines the extent to which the relationships of thickness with brightness temperature (and with emissivity) hold for thin sea-ice, approximately less than 0.2 -0.3 m, using near concurrent measurements of sea-ice thickness in the Sea of Okhotsk from a ship and passive microwave brightness temperature data from an over-flying aircraft. The results show that the brightness temperature and emissivity increase with ice thickness for the frequency range of 10-37 GHz. The relationship is more pronounced at lower frequencies and at the horizontal polarization. We also established an empirical relationship between ice thickness and salinity in the layer near the ice surface from a field experiment, which qualitatively support the idea that changes in the near-surface brine characteristics contribute to the observed thickness-brightness temperature/emissivity relationship. Our results suggest that for thin ice, passive microwave radiometric signals contain, ice thickness information which can be utilized in polar process studies.

High Resolution UAV-based Passive Microwave L-band Imaging of Soil Moisture

NASA Astrophysics Data System (ADS)

Gasiewski, A. J.; Stachura, M.; Elston, J.; McIntyre, E. M.

2013-12-01

Due to long electrical wavelengths and aperture size limitations the scaling of passive microwave remote sensing of soil moisture from spaceborne low-resolution applications to high resolution applications suitable for precision agriculture requires use of low flying aerial vehicles. This presentation summarizes a project to develop a commercial Unmanned Aerial Vehicle (UAV) hosting a precision microwave radiometer for mapping of soil moisture in high-value shallow root-zone crops. The project is based on the use of the Tempest electric-powered UAV and a compact digital L-band (1400-1427 MHz) passive microwave radiometer developed specifically for extremely small and lightweight aerial platforms or man-portable, tractor, or tower-based applications. Notable in this combination are a highly integrated UAV/radiometer antenna design and use of both the upwelling emitted signal from the surface and downwelling cold space signal for precise calibration using a lobe-correlating radiometer architecture. The system achieves a spatial resolution comparable to the altitude of the UAV above the ground while referencing upwelling measurements to the constant and well-known background temperature of cold space. The radiometer incorporates digital sampling and radio frequency interference mitigation along with infrared, near-infrared, and visible (red) sensors for surface temperature and vegetation biomass correction. This NASA-sponsored project is being developed both for commercial application in cropland water management, L-band satellite validation, and estuarian plume studies.

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.

Merging thermal and microwave satellite observations for a high-resolution soil moisture data product

USDA-ARS?s Scientific Manuscript database

Many societal applications of soil moisture data products require high spatial resolution and numerical accuracy. Current thermal geostationary satellite sensors (GOES Imager and GOES-R ABI) could produce 2-16km resolution soil moisture proxy data. Passive microwave satellite radiometers (e.g. AMSR...

The potential of 2D Kalman filtering for soil moisture data assimilation

USDA-ARS?s Scientific Manuscript database

We examine the potential for parameterizing a two-dimensional (2D) land data assimilation system using spatial error auto-correlation statistics gleaned from a triple collocation analysis and the triplet of: (1) active microwave-, (2) passive microwave- and (3) land surface model-based surface soil ...

WRF Simulations of the 20-22 January 2007 Snow Events over Eastern Canada: Comparison with In-Situ and Satellite Observations

NASA Technical Reports Server (NTRS)

Shi, J. J.; Tao, W.-K.; Matsui, T.; Cifelli, R.; Huo, A.; Lang, S.; Tokay, A.; Peters-Lidard, C.; Jackson, G.; Rutledge, S.;

Unpowered wireless generation and sensing of ultrasound

NASA Astrophysics Data System (ADS)

Huang, Haiying

2013-04-01

This paper presents a wireless ultrasound pitch-catch system that demonstrates the wireless generation and sensing of ultrasounds based on the principle of frequency conversion. The wireless ultrasound pitch-catch system consists of a wireless interrogator and two wireless ultrasound transducers. The wireless interrogator generates an ultrasound-modulated signal and a carrier signal, both at the microwave frequency, and transmits these two signals to the wireless ultrasound actuator using a pair of antennas. Upon receiving these two signals, the wireless ultrasound actuator recovers the ultrasound excitation signal using a passive mixer and then supplies it to a piezoelectric wafer sensor for ultrasound generation in the structure. For wireless ultrasound sensing, the frequency conversion process is reversed. The ultrasound sensing signal is up-converted to a microwave signal by the wireless ultrasound sensor and is recovered at the wireless interrogator using a homodyne receiver. To differentiate the wireless actuator from the wireless sensor, each wireless transducer is equipped with a narrowband microwave filter so that it only responds to the carrier frequency that matches the filter's operation bandwidth. The principle of operation of the wireless pitch-catch system, the hardware implementation, and the associated data processing algorithm to recover the ultrasound signal from the wirelessly received signal are described. The wirelessly acquired ultrasound signal is compared with those acquired using wired connection in both time and frequency domain.

Preliminary results of passive microwave snow experiment during February and March 1978

NASA Technical Reports Server (NTRS)

Chang, A. T. C.; Shiue, J. C.; Boyne, H.; Ellerbruch, D.; Counas, G.; Wittmann, R.; Jones, R.

1979-01-01

The purpose of the experiment was to determine if remote microwave sensing of snowpack data could be used to predict runoff, thereby allowing more efficient management of the water supply. A four-frequency microwave radiometer system was attached to a truck-mounted aerial lift and was used to gather data on snowpacks at three different sites in the Colorado Rocky Mountains. Ground truth data measurements (density, temperature, grain size, hardness, and free-liquid water content) were taken at each site corresponding to each microwave scan.

Microwave remote sensing from space for earth resource surveys

NASA Technical Reports Server (NTRS)

1977-01-01

The concepts of radar remote sensing and microwave radiometry are discussed and their utility in earth resource sensing is examined. The direct relationship between the character of the remotely sensed data and the level of decision making for which the data are appropriate is considered. Applications of active and a passive microwave sensing covered include hydrology, land use, mapping, vegetation classification, environmental monitoring, coastal features and processes, geology, and ice and snow. Approved and proposed microwave sensors are described and the use of space shuttle as a development platform is evaluated.

Algorithm Estimates Microwave Water-Vapor Delay

NASA Technical Reports Server (NTRS)

Robinson, Steven E.

1989-01-01

Accuracy equals or exceeds conventional linear algorithms. "Profile" algorithm improved algorithm using water-vapor-radiometer data to produce estimates of microwave delays caused by water vapor in troposphere. Does not require site-specific and weather-dependent empirical parameters other than standard meteorological data, latitude, and altitude for use in conjunction with published standard atmospheric data. Basic premise of profile algorithm, wet-path delay approximated closely by solution to simplified version of nonlinear delay problem and generated numerically from each radiometer observation and simultaneous meteorological data.

Leveraging GeoTIFF Compatibility for Visualizing a New EASE-Grid 2.0 Global Satellite Passive Microwave Climate Record

NASA Astrophysics Data System (ADS)

Paget, A. C.; Brodzik, M. J.; Long, D. G.; Hardman, M.

2016-02-01

The historical record of satellite-derived passive microwave brightness temperatures comprises data from multiple imaging radiometers (SMMR, SSM/I-SSMIS, AMSR-E), spanning nearly 40 years of Earth observations from 1978 to the present. Passive microwave data are used to monitor time series of many climatological variables, including ocean wind speeds, cloud liquid water and sea ice concentrations and ice velocity. Gridded versions of passive microwave data have been produced using various map projections (polar stereographic, Lambert azimuthal equal-area, cylindrical equal-area, quarter-degree Platte-Carree) and data formats (flat binary, HDF). However, none of the currently available versions can be rendered in the common visualization standard, geoTIFF, without requiring cartographic reprojection. Furthermore, the reprojection details are complicated and often require expert knowledge of obscure software package options. We are producing a consistently calibrated, completely reprocessed data set of this valuable multi-sensor satellite record, using EASE-Grid 2.0, an improved equal-area projection definition that will require no reprojection for translation into geoTIFF. Our approach has been twofold: 1) define the projection ellipsoid to match the reference datum of the satellite data, and 2) include required file-level metadata for standard projection software to correctly render the data in the geoTIFF standard. The Calibrated, Enhanced Resolution Brightness Temperature (CETB) Earth System Data Record (ESDR), leverages image reconstruction techniques to enhance gridded spatial resolution to 3 km and uses newly available intersensor calibrations to improve the quality of derived geophysical products. We expect that our attention to easy geoTIFF compatibility will foster higher-quality analysis with the CETB product by enabling easy and correct intercomparison with other gridded and in situ data.

Validation of the Daily Passive Microwave Snow Depth Products Over Northern China

NASA Astrophysics Data System (ADS)

Qiao, D.; Li, Z.; Wang, N.; Zhou, J.; Zhang, P.; Gao, S.

2018-04-01

Passive microwave sensors have the capability to provide information on snow depth (SD), which is critically important for hydrological modeling and water resource management. However, the different algorithms used to produce SD products lead to discrepancies in the data. To determine which products might be most suitable for Northern China, this paper assesses the accuracy of the existing snow depth products in the period of 2002-2011. By comparing three daily snow depth products, including NSIDC, WESTDC and ESA Globsnow, with snow cover product and meteorological stations data, the accuracies of the different SD products are analyzed for different snow class and forest cover fraction. The results show that comparison between snow cover derived from snow depth of NSIDC, ESA GlobSnow and WESTDC with snow cover product shows that accuracy of WESTDC and ESA GlobSnow in snow cover detecting can reach 0.70. Compared to meteorological stations data below 20 cm, NSIDC consistently overestimate, WESTDC and ESA Globsnow underestimate, furthermore the product from WESTDC is superior to the others. The three products have the same tendency of significant undervaluation over 20 cm. The WESTDC is superior to the ESA Globsnow and NSIDC in non-forest regions, whereas the ESA GlobSnow estimate is superior to the WESTDC and NSIDC in forest regions. As for the prairie and alpine snow, WESTDC has smaller bias and RMSE, meanwhile Globsnow has advantages in the snow depth retrieval in tundra and taiga snow. Therefore, we should choose the more suitable snow depth products according to different needs.

Surface and Atmospheric Contributions to Passive Microwave Brightness Temperatures for Falling Snow Events

NASA Technical Reports Server (NTRS)

Skofronick-Jackson, Gail; Johnson, Benjamin T.

2011-01-01

Physically based passive microwave precipitation retrieval algorithms require a set of relationships between satellite -observed brightness temperatures (TBs) and the physical state of the underlying atmosphere and surface. These relationships are nonlinear, such that inversions are ill ]posed especially over variable land surfaces. In order to elucidate these relationships, this work presents a theoretical analysis using TB weighting functions to quantify the percentage influence of the TB resulting from absorption, emission, and/or reflection from the surface, as well as from frozen hydrometeors in clouds, from atmospheric water vapor, and from other contributors. The percentage analysis was also compared to Jacobians. The results are presented for frequencies from 10 to 874 GHz, for individual snow profiles, and for averages over three cloud-resolving model simulations of falling snow. The bulk structure (e.g., ice water path and cloud depth) of the underlying cloud scene was found to affect the resultant TB and percentages, producing different values for blizzard, lake effect, and synoptic snow events. The slant path at a 53 viewing angle increases the hydrometeor contributions relative to nadir viewing channels. Jacobians provide the magnitude and direction of change in the TB values due to a change in the underlying scene; however, the percentage analysis provides detailed information on how that change affected contributions to the TB from the surface, hydrometeors, and water vapor. The TB percentage information presented in this paper provides information about the relative contributions to the TB and supplies key pieces of information required to develop and improve precipitation retrievals over land surfaces.

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.

Surface and Atmospheric Contributions to Passive Microwave Brightness Temperatures

NASA Technical Reports Server (NTRS)

Jackson, Gail Skofronick; Johnson, Benjamin T.

2010-01-01

Physically-based passive microwave precipitation retrieval algorithms require a set of relationships between satellite observed brightness temperatures (TB) and the physical state of the underlying atmosphere and surface. These relationships are typically non-linear, such that inversions are ill-posed especially over variable land surfaces. In order to better understand these relationships, this work presents a theoretical analysis using brightness temperature weighting functions to quantify the percentage of the TB resulting from absorption/emission/reflection from the surface, absorption/emission/scattering by liquid and frozen hydrometeors in the cloud, the emission from atmospheric water vapor, and other contributors. The results are presented for frequencies from 10 to 874 GHz and for several individual precipitation profiles as well as for three cloud resolving model simulations of falling snow. As expected, low frequency channels (<89 GHz) respond to liquid hydrometeors and the surface, while the higher frequency channels become increasingly sensitive to ice hydrometeors and the water vapor sounding channels react to water vapor in the atmosphere. Low emissivity surfaces (water and snow-covered land) permit energy downwelling from clouds to be reflected at the surface thereby increasing the percentage of the TB resulting from the hydrometeors. The slant path at a 53deg viewing angle increases the hydrometeor contributions relative to nadir viewing channels and show sensitivity to surface polarization effects. The TB percentage information presented in this paper answers questions about the relative contributions to the brightness temperatures and provides a key piece of information required to develop and improve precipitation retrievals over land surfaces.

Wireless Passive Stimulation of Engineered Cardiac Tissues.

PubMed

Liu, Shiyi; Navaei, Ali; Meng, Xueling; Nikkhah, Mehdi; Chae, Junseok

2017-07-28

We present a battery-free radio frequency (RF) microwave activated wireless stimulator, 25 × 42 × 1.6 mm 3 on a flexible substrate, featuring high current delivery, up to 60 mA, to stimulate engineered cardiac tissues. An external antenna shines 2.4 GHz microwave, which is modulated by an inverted pulse to directly control the stimulating waveform, to the wireless passive stimulator. The stimulator is equipped with an on-board antenna, multistage diode multipliers, and a control transistor. Rat cardiomyocytes, seeded on electrically conductive gelatin-based hydrogels, demonstrate synchronous contractions and Ca 2+ transients immediately upon stimulation. Notably, the stimulator output voltage and current profiles match the tissue contraction frequency within 0.5-2 Hz. Overall, our results indicate the promising potential of the proposed wireless passive stimulator for cardiac stimulation and therapy by induction of precisely controlled and synchronous contractions.

Bridging the Past with Today's Microwave Remote Sensing: A Case Study of Long Term Inundation Patterns in Two River Deltas

NASA Astrophysics Data System (ADS)

McDonald, K. C.; Jensen, K.; Schroeder, R.; Tessler, Z. D.

2016-12-01

Surface inundation extent and its predictability vary tremendously across the globe. This dynamic is being and has been captured by three general categories of satellite imagery: a) low-spatial-resolution microwave sensors with global coverage and a long record of observations (e.g., SSM/I), b) optical sensors with high spatial and temporal resolution and global coverage as well, but with cloud contamination (e.g. MODIS), and also c) less frequently in ``snapshot'' form by high-resolution synthetic aperture radar (SAR) sensors. We explore the ability to bridge techniques that can exploit the higher spatial resolution of more recent data products back in time with the help of the temporal evolution of lower resolution products. We present a study of long term (20+ yrs) inundation patterns in two river deltas: (1) the Mekong, and (2) the Ganges-Brahmaputra. This research utilizes baseline observations from the Surface Water Microwave Product Series (SWAMPS), an inundation area fraction product derived at 25km scale from active and passive microwave instruments (ERS, QuikSCAT, ASCAT, and SSM/I) that spans from Jan 1992 to the present. Every hydrological basin has unique characteristics - such as its topography, land cover / land use, and spatio-temporal variability - thus, a downscaling algorithm needs to take into account these idiosyncrasies. We merge SWAMPS with topographical information derived from 30m SRTM DEM, river networks from USGS HydroSHEDS, and train a downscaling algorithm to learn from two sets of classified SAR data: (1) L-band imaging radar from ALOS PALSAR, 2007-2010, and (2) more recent C-band imagery from the Sentinel-1 mission (2014 to present). We present an accuracy assessment of retrospective downscaled flood extent with Landsat imagery and address potential sources of biases. With a higher spatial resolution of past flooding extent, we can improve our understanding of how delta surface hydrology has responded to climate events and human activities. This is important both in the short-term for accurate flood prediction, as well as on longer-term planning horizons.

A numerically-stable algorithm for calibrating single six-ports for national microwave reflectometry

NASA Astrophysics Data System (ADS)

Hodgetts, T. E.

1990-11-01

A full description and analysis of the numerically stable algorithm currently used for calibrating single six ports or multi states for national microwave reflectometry, employing as standards four one port devices having known voltage reflection coefficients, is given.

RFI and Remote Sensing of the Earth from Space

NASA Technical Reports Server (NTRS)

Le Vine, D. M.; Johnson, J. T.; Piepmeier, J.

2016-01-01

Passive microwave remote sensing of the Earth from space provides information essential for understanding the Earth's environment and its evolution. Parameters such as soil moisture, sea surface temperature and salinity, and profiles of atmospheric temperature and humidity are measured at frequencies determined by the physics (e.g. sensitivity to changes in desired parameters) and by the availability of suitable spectrum free from interference. Interference from manmade sources (radio frequency interference) is an impediment that in many cases limits the potential for accurate measurements from space. A review is presented here of the frequencies employed in passive microwave remote sensing of the Earth from space and the associated experience with RFI.

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.

Combining Passive Microwave and Optical Data to Estimate Snow Water Equivalent in Afghanistan's Hindu Kush

NASA Astrophysics Data System (ADS)

Dozier, J.; Bair, N.; Calfa, A. A.; Skalka, C.; Tolle, K.; Bongard, J.

2015-12-01

The task is to estimate spatiotemporally distributed estimates of snow water equivalent (SWE) in snow-dominated mountain environments, including those that lack on-the-ground measurements such as the Hindu Kush range in Afghanistan. During the snow season, we can use two measurements: (1) passive microwave estimates of SWE, which generally underestimate in the mountains; (2) fractional snow-covered area from MODIS. Once the snow has melted, we can reconstruct the accumulated SWE back to the last significant snowfall by calculating the energy used in melt. The reconstructed SWE values provide a training set for predictions from the passive microwave SWE and snow-covered area. We examine several machine learning methods—regression-boosted decision trees, bagged trees, neural networks, and genetic programming—to estimate SWE. All methods work reasonably well, with R2 values greater than 0.8. Predictors built with multiple years of data reduce the bias that usually appears if we predict one year from just one other year's training set. Genetic programming tends to produce results that additionally provide physical insight. Adding precipitation estimates from the Global Precipitation Measurements mission is in progress.

Passive microwave remote sensing for sea ice research

NASA Technical Reports Server (NTRS)

1984-01-01

Techniques for gathering data by remote sensors on satellites utilized for sea ice research are summarized. Measurement of brightness temperatures by a passive microwave imager converted to maps of total sea ice concentration and to the areal fractions covered by first year and multiyear ice are described. Several ancillary observations, especially by means of automatic data buoys and submarines equipped with upward looking sonars, are needed to improve the validation and interpretation of satellite data. The design and performance characteristics of the Navy's Special Sensor Microwave Imager, expected to be in orbit in late 1985, are described. It is recommended that data from that instrument be processed to a form suitable for research applications and archived in a readily accessible form. The sea ice data products required for research purposes are described and recommendations for their archival and distribution to the scientific community are presented.

A feasibility study of a microwave water vapor measurement from a space probe along an occultation path

NASA Technical Reports Server (NTRS)

Longbothum, R. L.

1975-01-01

Stratospheric and mesospheric water vapor measurements were taken using the microwave lines at 22 GHz (22.235 GHz) and 183 GHz (183.31 GHz). The resonant cross sections for both the 22 GHz and the 183 GHz lines were used to model the optical depth of atmospheric water vapor. The range of optical depths seen by a microwave radiometer through the earth's limb was determined from radiative transfer theory. Radiometer sensitivity, derived from signal theory, was compared with calculated optical depths to determine the maximum height to which water vapor can be measured using the following methods: passive emission, passive absorption, and active absorption. It was concluded that measurements using the 22 GHz line are limited to about 50 km whereas the 183 GHz line enables measurements up to and above 100 km for water vapor mixing ratios as low as 0.1 ppm under optimum conditions.

Results From the First 118 GHz Passive Microwave Observations Over Antarctica

NASA Astrophysics Data System (ADS)

McAllister, R.; Gallaher, D. W.; Gasiewski, A. J.; Periasamy, L.; Belter, R.; Hurowitz, M.; Hosack, W.; Sanders, B. T.

2017-12-01

Cooperation between the University of Colorado (Center for Environmental Technology, National Snow and Ice Data Center, and Colorado Space Grant Consortium) and the private corporation Orbital Micro Systems (OMS) has resulted in a highly miniturized passive microwave sensor. This sensor was successfully flown over Antarctica in onboard NASA's DC-8 in Operation Ice Bridge (OIB) in October / November of 2016. Data was collected from the "MiniRad" 8 channel miniaturized microwave sensor, which operated as both a sounder and an imager. The non-calibrated observation included both high and low altitude observations over clouds, sea, ice, ice sheets, and mountains as well as terrain around Tierra del Fuego. Sample results and their significance will be discussed. The instrument is in a form factor suitable for deployment in cubesats and will be launched into orbit next year. Commercial deployments by OMS in a constellation configuration will shortly follow.

Highly luminescent carbon nanodots by microwave-assisted pyrolysis.

PubMed

Zhai, Xinyun; Zhang, Peng; Liu, Changjun; Bai, Tao; Li, Wenchen; Dai, Liming; Liu, Wenguang

2012-08-18

Carbon nanodots (CDs) with a low cytotoxicity have been synthesized by one-step microwave-assisted pyrolysis of citric acid in the presence of various amine molecules. The primary amine molecules have been confirmed to serve dual roles as N-doping precursors and surface passivation agents, both of which considerably enhanced the fluorescence of the CDs.

Active–passive soil moisture retrievals during the SMAP validation experiment 2012

USDA-ARS?s Scientific Manuscript database

The goal of this study is to assess the performance of the active–passive algorithm for the NASA Soil Moisture Active Passive mission (SMAP) using airborne and ground observations from a field campaign. The SMAP active–passive algorithm disaggregates the coarse-resolution radiometer brightness tempe...

HMMR (High-Resolution Multifrequency Microwave Radiometer) Earth observing system, volume 2e. Instrument panel report

NASA Technical Reports Server (NTRS)

1987-01-01

Recommendations and background are provided for a passive microwave remote sensing system of the future designed to meet the observational needs of Earth scientist in the next decade. This system, called the High Resolution Multifrequency Microwave Radiometer (HMMR), is to be part of a complement of instruments in polar orbit. Working together, these instruments will form an Earth Observing System (EOS) to provide the information needed to better understand the fundamental, global scale processes which govern the Earth's environment. Measurements are identified in detail which passive observations in the microwave portion of the spectrum could contribute to an Earth Observing System in polar orbit. Requirements are established, e.g., spatial and temporal resolution, for these measurements so that, when combined with the other instruments in the Earth Observing System, they would yield a data set suitable for understanding the fundamental processes governing the Earth's environment. Existing and/or planned sensor systems are assessed in the light of these requirements, and additional sensor hardware needed to meet these observational requirements are defined.

Using satellite microwave sensors to develop climate data records

NASA Astrophysics Data System (ADS)

Ferraro, Ralph; Meng, Huan; Luo, Zhengzhao

2011-08-01

NOAA Workshop on Climate Data Records From Satellite Passive Microwave Sounders: AMSU/MHS/SSMT2; College Park, Maryland, 2-3 March 2011 ; The National Oceanic and Atmospheric Administration's (NOAA) Climate Data Record (CDR) program (http://www.ncdc.noaa.gov/cdr/index.html) is an effort to create long-term homogeneous records of satellite measurements and derived products. As part of this effort, scientists at two related projects that focus on passive microwave sensors with the goal of hydrological applications—one led by a National Environmental Satellite, Data, and Information Service/Center for Satellite Applications and Research (STAR) team and one led by the City College of New York (CCNY)—held a joint workshop with the following objectives: To allow the CDR teams to interact with satellite data and product users and other CDR developers on relevant aspects of sensor characteristics and intercalibration that will lead to mature CDRs; To provide a formal mechanism for input by subject matter experts, in particular, sensor scientists and engineers; and> To move toward a community consensus approach for NOAA microwave sounder CDRs.

Earth Observing System (EOS)/Advanced Microwave Sounding Unit-A (AMSU-A): Calibration management plan

NASA Technical Reports Server (NTRS)

1994-01-01

This is the Calibration Management Plan for the Earth Observing System/Advanced Microwave Sounding Unit-A (AMSU-A). The plan defines calibration requirements, calibration equipment, and calibration methods for the AMSU-A, a 15 channel passive microwave radiometer that will be used for measuring global atmospheric temperature profiles from the EOS polar orbiting observatory. The AMSU-A system will also provide data to verify and augment that of the Atmospheric Infrared Sounder.

Combined active and passive microwave remote sensing of vegetated surfaces at l-band

USDA-ARS?s Scientific Manuscript database

In previous work the distorted Born approximation (DBA) of volume scattering was combined with the numerical solutions of Maxwell equations (NMM3D) for a rough surface to calculate the radar backscattering coefficient for the Soil Moisture Active Passive (SMAP) mission. The model results were valida...

Microphysical and Radiative Characteristics of Convective Clouds during COHMEX.

NASA Astrophysics Data System (ADS)

Fulton, Richard; Heymsfield, Gerald M.

1991-01-01

The use of passive remote microwave radiance measurements above cloud tops for rainrate estimation is complicated by the complex nature of cloud microphysics. The knowledge of the microphysical structure of clouds, specifically the hydrometeor types, shapes, sizes, and their vertical distribution, is important because radiative emission and scattering effects are dependent upon the hydrometeor distribution. This paper has two purposes: first, to document the structure and evolution of two strong thunderstorms in Alabama using radar multiparameter data; and second, to relate the inferred microphysics to the resulting upwelling microwave radiance observed concurrently by high altitude aircraft. These measurements were collected during the COHMEX field program in the summer of 1986. The radar analysis includes a description of the parameters reflectivity Z, differential reflectivity ZDR, linear depolarization ratio LDR, and hail signal HS for two thunderstorm cases on 11 July 1986. The simultaneous aircraft data includes passive microwave brightness temperature (TB) measurements at four frequencies ranging from 18 to 183 GHz as well as visible and infrared data.The remote radar observations reveal the existence of large ice particles within the storms which is likely to have caused the observed low microwave brightness temperatures. By relating the evolution of the radar measureables to the microwave TB's it has been found that knowledge of the storm microphysics and its evolution is important to adequately understand the microwave TB's.

Estimation of polar low characteristics for the Nordic Seas for 1995-2008 using satellite data

NASA Astrophysics Data System (ADS)

Smirnova, Julia; Chapron, Bertrand; Zabolotskikh, Elizaveta; Leonid Bobylev, Mr

In recent years the scientific research confirmed the fact of the global warming. The Arctic climate is warming even more rapidly. Powerful storm polar lows having wind speeds of about 25 m/c are known to be the cause of hazardous weather. Polar lows present themselves as the atmospheric phenomena the horizontal dimensions of which do not exceed 1,000 km, appear and which exist from 12 to 24 hours. The wave fall and low temperatures can lead to increased probability of vessel icing the intensity of which increases with the high wind speed and large wave height. Study of the mesoscale processes, such as polar lows in the Arctic has become especially relevant due to the sharp sea ice decreasing in the Arctic Ocean and Arctic seas in recent years. Only the use of satellite data allows obtaining regular and spacious information about the polar lows. Early detection and evaluation of the characteristics of the polar lows is an extremely important task to ensure the safety of navigation, fishing and oil industry in the Arctic region. With new open areas dangerous polar lows can arise over them. So early detection of the polar lows, studying their characteristics, tracking their movement and prediction presents one the most important problems of the modern science. The present-day meteorological observational network has severe limitations in detecting all, especially small mesoscale cyclones, so there is a strong need for new and/or improved methods to detect and monitor polar lows. Satellite remote sensing seems to be the most feasible tool for early detection and monitoring of the polar lows. Several remote sensing sensors are capable to detect a polar low but each of them suffers from various deficiencies. In the work, satellite passive microwave data have been intensively exploited aiming at obtaining the fields of geophysical parameters inside the polar lows. DMSP Special Sensor Microwave/Imager - SSM/I data were used in the research. The polar lows have been identified on satellite passive microwave imagery by fields of means of analysis of atmospheric water vapour fields using a new approach. This approach consists of two stages. During the first stage the total atmospheric water vapor fields are calculated from passive microwave measurements using precise retrieval Neural Network Algorithms (Bobylev et al., 2010). During the second stage the vortex structures are detected in these fields, and polar lows are identified and tracked. Based on this approach, were estimated polar low characteristics in the Nordic seas for the period of 1995 - 2008. All polar lows have been identified for this period on SSM/I imagery. Other satellite data, such as QuikSCAT SeaWinds, NOAA AVHRR were used as additional information for polar low parameter retrieval and analysis.

AN ACTIVE-PASSIVE COMBINED ALGORITHM FOR HIGH SPATIAL RESOLUTION RETRIEVAL OF SOIL MOISTURE FROM SATELLITE SENSORS (Invited)

NASA Astrophysics Data System (ADS)

Lakshmi, V.; Mladenova, I. E.; Narayan, U.

2009-12-01

Soil moisture is known to be an essential factor in controlling the partitioning of rainfall into surface runoff and infiltration and solar energy into latent and sensible heat fluxes. Remote sensing has long proven its capability to obtain soil moisture in near real-time. However, at the present time we have the Advanced Scanning Microwave Radiometer (AMSR-E) on board NASA’s AQUA platform is the only satellite sensor that supplies a soil moisture product. AMSR-E coarse spatial resolution (~ 50 km at 6.9 GHz) strongly limits its applicability for small scale studies. A very promising technique for spatial disaggregation by combining radar and radiometer observations has been demonstrated by the authors using a methodology is based on the assumption that any change in measured brightness temperature and backscatter from one to the next time step is due primarily to change in soil wetness. The approach uses radiometric estimates of soil moisture at a lower resolution to compute the sensitivity of radar to soil moisture at the lower resolution. This estimate of sensitivity is then disaggregated using vegetation water content, vegetation type and soil texture information, which are the variables on which determine the radar sensitivity to soil moisture and are generally available at a scale of radar observation. This change detection algorithm is applied to several locations. We have used aircraft observed active and passive data over Walnut Creek watershed in Central Iowa in 2002; the Little Washita Watershed in Oklahoma in 2003 and the Murrumbidgee Catchment in southeastern Australia for 2006. All of these locations have different soils and land cover conditions which leads to a rigorous test of the disaggregation algorithm. Furthermore, we compare the derived high spatial resolution soil moisture to in-situ sampling and ground observation networks

SMOS/SMAP Synergy for SMAP Level 2 Soil Moisture Algorithm Evaluation

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas J.; Zhao, Tianjie; Cosh, Michael; Chan, Steven; O'Neill, Peggy; Njoku, Eni; Colliander, Andreas; Kerr, Yann

2011-01-01

Soil Moisture Active Passive (SMAP) satellite has been proposed to provide global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only soil moisture product at 40-km spatial resolution. This product and the supporting brightness temperature observations are common to both SMAP and European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are opportunities for synergies between the two missions. These include exploiting the data for calibration and validation and establishing longer term L-band brightness temperature and derived soil moisture products. In this investigation we will be using SMOS brightness temperature, ancillary data, and soil moisture products to develop and evaluate a candidate SMAP L2 passive soil moisture retrieval algorithm. This work will begin with evaluations based on the SMOS product grids and ancillary data sets and transition to those that will be used by SMAP. An important step in this analysis is reprocessing the multiple incidence angle observations provided by SMOS to a global brightness temperature product that simulates the constant 40 degree incidence angle observations that SMAP will provide. The reprocessed brightness temperature data provide a basis for evaluating different SMAP algorithm alternatives. Several algorithms are being considered for the SMAP radiometer-only soil moisture retrieval. In this first phase, we utilized only the Single Channel Algorithm (SCA), which is based on the radiative transfer equation and uses the channel that is most sensitive to soil moisture (H-pol). Brightness temperature is corrected sequentially for the effects of temperature, vegetation, roughness (dynamic ancillary data sets) and soil texture (static ancillary data set). European Centre for Medium-Range Weather Forecasts (ECMWF) estimates of soil temperature for the top layer (as provided as part of the SMOS ancillary data) were used to correct for surface temperature effects and to derive microwave emissivity. ECMWF data were also used for precipitation forecasts, presence of snow, and frozen ground. Vegetation options are described below. One year of soil moisture observations from a set of four watersheds in the U.S. were used to evaluate four different retrieval methodologies: (1) SMOS soil moisture estimates (version 400), (2) SeA soil moisture estimates using the SMOS/SMAP data with SMOS estimated vegetation optical depth, which is part of the SMOS level 2 product, (3) SeA soil moisture estimates using the SMOS/SMAP data and the MODIS-based vegetation climatology data, and (4) SeA soil moisture estimates using the SMOS/SMAP data and actual MODIS observations. The use of SMOS real-world global microwave observations and the analyses described here will help in the development and selection of different land surface parameters and ancillary observations needed for the SMAP soil moisture algorithms. These investigations will greatly improve the quality and reliability of this SMAP product at launch.

Investigating methods to estimate melting event parameters over Arctic sea- ice using SSM/I, OKEAN, and RADARSAT Data

NASA Astrophysics Data System (ADS)

Belchansky, G.; Eremeev, V.; Mordvintsev, I.; Platonov, N.; Douglas, D.

The melting events (early melt, melt onset, melt ponding, freeze-up onset) over Arctic sea-ice area are critical for climate and global change studies. They are combined with accuracy of surface energy balances estimates (due to contrasts in the short wave albedo of snow and ice, open water or melt ponds) and drives a number of important processes (onset of snow melt, thawing of boreal forest, etc). M icrowave measurements identify seasonal transition zones due to large differences in emissivity during melt onset, melt ponding and freeze-up periods. This report presents near coincident observation of backscatter cross section (0 ) and brightness temperature (Tb) from Russian OKEAN 01 satellite series, backscatter cross section (0) from RADARSAT-1, brightness temperatures (Tbs) from SSM/I sensors, and near-surface temperature derived from the International Arctic Buoy Program data (IABP) (Belchansky and Douglas, 2000, 2002). To determine the melt duration (time of freeze-up onset minus time of melt onset) passive and active microwave methods were developed. These methods used differences between SSM /I 19.3GHz,H and SSM/I 37.0 GHz, H channels (SSM/I Tb), OKEAN 0 (9.52GHz, VV) and Tb (37.47 GHz, H) channels, RADARSAT-1 0 (5.3GHz, HH), and a threshold technique. An evolution of the SSM/I Tb, OKEAN-01 0 and Tb, RADARSAT ScanSAR 0, MEAN ( 0), SD(0) and SD(0 ) / MEAN(0 ) as function of time was investigated along FY and MY dominant type ice areas during January 1996 through December 1998. The SSM/I, OKEAN and RADARSAT melt onset and freeze up onset algorithms were constructed. The SSM/I algorithm was based- on analysis of the SSM/I Tb. The OKEAN and RADARSAT ScanSAR algorithms were based, respectively, on analysis of OKEAN 0 and Tb of MY and FY sea ice at each MY and FY ice region (200 km by 200 km) determined in OKEAN imagery prior to melting period and changes in RADARSAT SD(0 ) / MEAN(0) of sea-ice during different stages of melting processes at each ice site (75 km by 75 km) determined prior to spring period in ScanSAR imagery. The averaged 12-h near surface temperatures derived from the IABP wer e used to analyze changes in the SSM/I Tb, OKEAN 0 and OKEAN Tb, RADARSAT SD(0) / MEAN(0), and to estimate respective thresholds associated with the melt onset and freeze-up onset. To highlight the sources of differences among various sensors results were compared to understand how the average the melt onset, melt duration and freeze-up onset estimates varied between different instruments and algorithms. A discrepancy in estimates resulted due to the nature of active and passive microwave measurements, frequency and polarization, number of channels, temperature and emissivity effects, and algorithm types. Higher spatial resolution of OKEAN-01 and RADARSAT-1 SAR was an important characteristic for obtaining better estimates of melting parameters. The SSM/ data provide a spatial resolution with global coverageI suitable for circulation models. Therefore OKEAN-01 and RADARSAT measurements can complement SSM/I data. These studies contribute to the growing body of documentation about the levels of disparity obtained when Arctic seasonal transition parameters are calculated using various types of satellite sensors and algorithms. ACKNOWLEDGEMENTS This work was carried out with the support from the International Arctic Research Center and Cooperative Institute for Arctic Research (IARC/CIFAR), University of Alaska Fairbanks. We would like to acknowledge the Alaska SAR Facility (Fairbanks), the National Snow and Ice Data Center (University of Colorado), and the Global Hydrology Resource Center, respectively, for providing RADARSAT images, the DMSP SSM/I Daily Polar Gridded Tb and Sea Ice Concentrations, the single-pass SSM/I brightness temperature data. REFERENCES Belchansky, G. I. and Douglas, D. C. (2000). Classification methods for monitoring Arctic sea-ice using OKEAN passive / active two-channel microwave data. J. Remote Sensing of Environment, Elsevier Science, New York. 73 (3): 307 -322. Belchansky, G. I. and Douglas, D. C. (2002). Seasonal comparisons of sea ice concentration estimates derived from SSM /I, OKEAN, and RADARSAT data. J. Remote Sensing of Environment, Elsevier Science, New York, 81 (1): 67-81.

Parameter Search Algorithms for Microwave Radar-Based Breast Imaging: Focal Quality Metrics as Fitness Functions.

PubMed

O'Loughlin, Declan; Oliveira, Bárbara L; Elahi, Muhammad Adnan; Glavin, Martin; Jones, Edward; Popović, Milica; O'Halloran, Martin

2017-12-06

Inaccurate estimation of average dielectric properties can have a tangible impact on microwave radar-based breast images. Despite this, recent patient imaging studies have used a fixed estimate although this is known to vary from patient to patient. Parameter search algorithms are a promising technique for estimating the average dielectric properties from the reconstructed microwave images themselves without additional hardware. In this work, qualities of accurately reconstructed images are identified from point spread functions. As the qualities of accurately reconstructed microwave images are similar to the qualities of focused microscopic and photographic images, this work proposes the use of focal quality metrics for average dielectric property estimation. The robustness of the parameter search is evaluated using experimental dielectrically heterogeneous phantoms on the three-dimensional volumetric image. Based on a very broad initial estimate of the average dielectric properties, this paper shows how these metrics can be used as suitable fitness functions in parameter search algorithms to reconstruct clear and focused microwave radar images.

Improving Global Analysis and Short-Range Forecast Using Rainfall and Moisture Observations Derived from TRMM and SSM/I Passive Microwave Instruments

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; daSilva, Arlindo M.; Olson, William S.; Kummerow, Christian D.; Simpson, Joanne

2000-01-01

The Global Precipitation Mission, a satellite project under consideration as a follow-on to the Tropical Rainfall Measuring Mission (TRMM) by the National Aeronautics and Space Agency (NASA) in the United States, the National Space Development Agency (NASDA) in Japan, and other international partners, comprises an improved TRMM-like satellite and a constellation of 8 satellites carrying passive microwave radiometers to provide global rainfall measurements at 3-hour intervals. The success of this concept relies on the merits of rainfall estimates derived from passive microwave radiometers. This article offers a proof-of-concept demonstration of the benefits of using, rainfall and total precipitable water (TPW) information derived from such instruments in global data assimilation with observations from the TRMM Microwave Imager (TMI) and 2 Special Sensor Microwave/Imager (SSM/I) instruments. Global analyses that optimally combine observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data analyses contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. We show that assimilating the 6-h averaged TMI and SSM/I surface rainrate and TPW retrievals improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper tropospheric moisture in the analysis produced by the Goddard Earth Observing System (GEOS) Data Assimilation System, as verified against radiation measurements by the Clouds and the Earth's Radiant Energy System (CERES) instrument and brightness temperature observations by the TIROS Operational Vertical Sounder (TOVS) instruments. Typically, rainfall assimilation improves clouds and radiation in areas of active convection, as well as the latent heating and large-scale motions in the tropics, while TPW assimilation leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Ensemble forecasts initialized with analyses that incorporate TMI and SSM/I rainfall and TPW data also yield better short-range predictions of geopotential heights, winds, and precipitation in the tropics. This study offers a compelling illustration of the potential of using rainfall and TPW information derived from passive microwave instruments to significantly improve the quality of 4-dimensional global datasets for climate analysis and weather forecasting applications.

Directional amplifier in an optomechanical system with optical gain

NASA Astrophysics Data System (ADS)

Jiang, Cheng; Song, L. N.; Li, Yong

2018-05-01

Directional amplifiers are crucial nonreciprocal devices in both classical and quantum information processing. Here we propose a scheme for realizing a directional amplifier between optical and microwave fields based on an optomechanical system with optical gain, where an active optical cavity and two passive microwave cavities are coupled to a common mechanical resonator via radiation pressure. The two passive cavities are coupled via hopping interaction to facilitate the directional amplification between the active and passive cavities. We obtain the condition of achieving optical directional amplification and find that the direction of amplification can be controlled by the phase differences between the effective optomechanical couplings. The effects of the gain rate of the active cavity and the effective coupling strengths on the maximum gain of the amplifier are discussed. We show that the noise added to this amplifier can be greatly suppressed in the large cooperativity limit.

Microwave and physical properties of sea ice in the winter marginal ice zone

NASA Technical Reports Server (NTRS)

Tucker, W. B., III; Perovich, D. K.; Gow, A. J.; Grenfell, T. C.; Onstott, R. G.

1991-01-01

Surface-based active and passive microwave measurements were made in conjunction with ice property measurements for several distinct ice types in the Fram Strait during March and April 1987. Synthesis aperture radar imagery downlinked from an aircraft was used to select study sites. The surface-based radar scattering cross section and emissivity spectra generally support previously inferred qualitative relationships between ice types, exhibiting expected separation between young, first-year and multiyear ice. Gradient ratios, calculated for both active and passive data, appear to allow clear separation of ice types when used jointly. Surface flooding of multiyear floes, resulting from excessive loading and perhaps wave action, causes both active and passive signatures to resemble those of first-year ice. This effect could possibly cause estimates of ice type percentages in the marginal ice zone to be in error when derived from aircraft- or satellite-born sensors.

Smart Phase Tuning in Microwave Photonic Integrated Circuits Toward Automated Frequency Multiplication by Design

NASA Astrophysics Data System (ADS)

Nabavi, N.

2018-07-01

The author investigates the monitoring methods for fine adjustment of the previously proposed on-chip architecture for frequency multiplication and translation of harmonics by design. Digital signal processing (DSP) algorithms are utilized to create an optimized microwave photonic integrated circuit functionality toward automated frequency multiplication. The implemented DSP algorithms are formed on discrete Fourier transform and optimization-based algorithms (Greedy and gradient-based algorithms), which are analytically derived and numerically compared based on the accuracy and speed of convergence criteria.

A multistage selective weighting method for improved microwave breast tomography.

PubMed

Shahzad, Atif; O'Halloran, Martin; Jones, Edward; Glavin, Martin

2016-12-01

Microwave tomography has shown potential to successfully reconstruct the dielectric properties of the human breast, thereby providing an alternative to other imaging modalities used in breast imaging applications. Considering the costly forward solution and complex iterative algorithms, computational complexity becomes a major bottleneck in practical applications of microwave tomography. In addition, the natural tendency of microwave inversion algorithms to reward high contrast breast tissue boundaries, such as the skin-adipose interface, usually leads to a very slow reconstruction of the internal tissue structure of human breast. This paper presents a multistage selective weighting method to improve the reconstruction quality of breast dielectric properties and minimize the computational cost of microwave breast tomography. In the proposed two stage approach, the skin layer is approximated using scaled microwave measurements in the first pass of the inversion algorithm; a numerical skin model is then constructed based on the estimated skin layer and the assumed dielectric properties of the skin tissue. In the second stage of the algorithm, the skin model is used as a priori information to reconstruct the internal tissue structure of the breast using a set of temporal scaling functions. The proposed method is evaluated on anatomically accurate MRI-derived breast phantoms and a comparison with the standard single-stage technique is presented. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Snow stratigraphic heterogeneity within ground-based passive microwave radiometer footprints: implications for emission modelling

NASA Astrophysics Data System (ADS)

Sandells, M.; Rutter, N.; Derksen, C.; Langlois, A.; Lemmetyinen, J.; Montpetit, B.; Pulliainen, J. T.; Royer, A.; Toose, P.

2012-12-01

Remote sensing of snow mass remains a challenging area of research. Scattering of electromagnetic radiation is sensitive to snow mass, but is also affected by contrasts in the dielectric properties of the snow. Although the argument that errors from simple algorithms average out at large scales has been used to justify current retrieval methods, it is not obvious why this should be the case. This hypothesis needs to be tested more rigorously. A ground-based field experiment was carried out to assess the impact of sub-footprint snow heterogeneity on microwave brightness temperature, in Churchill, Canada in winter in early 2010. Passive microwave measurements of snow were made using sled-mounted radiometers at 75cm intervals over a 5m transect. Measurements were made at horizontal and vertical polarizations at frequencies of 19 and 37 GHz. Snow beneath the radiometer footprints was subsequently excavated, creating a snow trench wall along the centrepoints of adjacent footprints. The trench wall was carefully smoothed and photographed with a near-infrared camera in order to determine the positions of stratigraphic snow layer boundaries. Three one-dimensional vertical profiles of snowpack properties (density and snow specific surface area) were taken at 75cm, 185cm and 355cm from the left hand side of the trench. These profile measurements were used to derive snow density and grain size for each of the layers identified from the NIR image. Microwave brightness temperatures for the 2-dimensional map of snow properties was simulated with the Helsinki University of Technology (HUT) model at 1cm intervals horizontally across the trench. Where each of five ice lenses was identified in the snow stratigraphy, a decrease in brightness temperature was simulated. However, the median brightness temperature simulated across the trench was substantially higher than the observations, of the order of tens of Kelvin, dependent on frequency and polarization. In order to understand and quantify possible sources of error in the simulations, a number of experiments were carried out to investigate the sensitivity of the brightness temperature to: 1) uncertainties in field observations, 2) representation of ice lenses, 3) model layering structure, and 4) near-infrared derived grain size representing snow grain size at microwave wavelengths. Field measurement error made little difference to the simulated brightness temperature, nor did the representation of ice lenses as crusts of high density snow. As the number of layers in the snow was reduced to 3, 2, or 1, the simulated brightness temperature increased slightly. However, scaling of snow grain size had a dramatic effect on the simulated brightness temperatures, reducing the median bias of the simulations to within measurement error for the statistically different brightness temperature distributions. This indicated that further investigation is required to define what is meant by the microwave grain size, and how this relates to the grain size that is used in the microwave emission model.

Detection Thresholds of Falling Snow From Satellite-Borne Active and Passive Sensors

NASA Technical Reports Server (NTRS)

Skofronick-Jackson, Gail M.; Johnson, Benjamin T.; Munchak, S. Joseph

2013-01-01

There is an increased interest in detecting and estimating the amount of falling snow reaching the Earths surface in order to fully capture the global atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms for current and future missions includes determining the thresholds of detection for various active and passive sensor channel configurations and falling snow events over land surfaces and lakes. In this paper, cloud resolving model simulations of lake effect and synoptic snow events were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W-band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR)Ku- and Ka-bands, and the GPM Microwave Imager. Eleven different nonspherical snowflake shapes were used in the analysis. Notable results include the following: 1) The W-band radar has detection thresholds more than an order of magnitude lower than the future GPM radars; 2) the cloud structure macrophysics influences the thresholds of detection for passive channels (e.g., snow events with larger ice water paths and thicker clouds are easier to detect); 3) the snowflake microphysics (mainly shape and density)plays a large role in the detection threshold for active and passive instruments; 4) with reasonable assumptions, the passive 166-GHz channel has detection threshold values comparable to those of the GPM DPR Ku- and Ka-band radars with approximately 0.05 g *m(exp -3) detected at the surface, or an approximately 0.5-1.0-mm * h(exp -1) melted snow rate. This paper provides information on the light snowfall events missed by the sensors and not captured in global estimates.

Arctic and Antarctic Sea Ice, 1978-1987: Satellite Passive-Microwave Observations and Analysis

NASA Technical Reports Server (NTRS)

Gloersen, Per; Campbell, William J.; Cavalieri, Donald J.; Comiso, Josefino C.; Parkinson, Claire L.; Zwally, H. Jay

1992-01-01

This book contains a description and analysis of the spatial and temporal variations in the Arctic and Antarctic sea ice covers from October 26, 1978 through August 20, 1987. It is based on data collected by the Scanning Multichannel Microwave Radiometer (SMMR) onboard the NASA Nimbus 7 satellite. The 8.8-year period, together with the 4 years of the Nimbus 5 Electrically Scanning Microwave Radiometer (ESMR) observations presented in two earlier volumes, comprises a sea ice record spanning almost 15 years.

Interpretation of Passive Microwave Imagery of Surface Snow and Ice: Harding Lake, Alaska

DTIC Science & Technology

1991-06-01

Circle conditions in microwave imagery depends on the char- (Fig. 1). The lake is roughly circular in shape and has a acteristics of the sensor system...local oscillator frequency 33.6 0Hz IF bandwidth Greaterthan 500 MHz cracks in the ice sheet. The incursion process is de - video bandwidth 1.7 kHz...using pas- surface snow had oct.urred on these similarly sized sive microwave sensors . IEEE/Transactions on Geo- lakes. Additional field verifications

Passive microwave derived snowmelt timing: significance, spatial and temporal variability, and potential applications

NASA Astrophysics Data System (ADS)

Semmens, Kathryn Alese

Snow accumulation and melt are dynamic features of the cryosphere indicative of a changing climate. Spring melt and refreeze timing are of particular importance due to the influence on subsequent hydrological and ecological processes, including peak runoff and green-up. To investigate the spatial and temporal variability of melt timing across a sub-arctic region (the Yukon River Basin (YRB), Alaska/Canada) dominated by snow and lacking substantial ground instrumentation, passive microwave remote sensing was utilized to provide daily brightness temperatures (Tb) regardless of clouds and darkness. Algorithms to derive the timing of melt onset and the end of melt-refreeze, a critical transition period where the snowpack melts during the day and refreezes at night, were based on thresholds for Tb and diurnal amplitude variations (day and night difference). Tb data from the Special Sensor Microwave Imager (1988 to 2011) was used for analyzing YRB terrestrial snowmelt timing and for characterizing melt regime patterns for icefields in Alaska and Patagonia. Tb data from the Advanced Microwave Scanning Radiometer for EOS (2003 to 2010) was used for determining the occurrence of early melt events (before melt onset) associated with fog or rain on snow, for investigating the correlation between melt timing and forest fires, and for driving a flux-based snowmelt runoff model. From the SSM/I analysis: the melt-refreeze period lengthened for the majority of the YRB with later end of melt-refreeze and earlier melt onset; and positive Tb anomalies were found in recent years from glacier melt dynamics. From the AMSR-E analysis: early melt events throughout the YRB were most often associated with warm air intrusions and reflect a consistent spatial distribution; years and areas of earlier melt onset and refreeze had more forest fire occurrences suggesting melt timing's effects extend to later seasons; and satellite derived melt timing served as an effective input for model simulation of discharge in remote, ungauged snow-dominated basins. The melt detection methodology and results present a new perspective on the changing cryosphere, provide an understanding of melt's influence on other earth system processes, and develop a baseline from which to assess and evaluate future change. The temporal and spatial variability conveyed through the regional context of this research may be useful to communities in climate change adaptation planning.

Evaluation of Improvements to the TRMM Microwave Rain Algorithm

NASA Technical Reports Server (NTRS)

Yang, Song; Olson, Williams S.; Smith, Eric A.; Kummerow, Christian

2002-01-01

Improvements made to the Version 5 TRMM passive microwave rain retrieval algorithm (2A-12) are evaluated using independent data. Surface rain rate estimates from the Version 5 TRMM TMI (2A-12), PR (2A-25) and TMI/PR Combined (2B-31) algorithms and ground-based radar estimates for selected coincident subset datasets in 1998 over Melbourne and Kwajalein show varying degrees of agreement. The surface rain rates are then classified into convective and stratiform rain types over ocean, land, and coastal areas for more detailed comparisons to the ground radar measurements. These comparisons lead to a better understanding of the relative performances of the current TRMM rain algorithms. For example, at Melbourne more than 80% of the radar-derived rainfall is classified as convective rain. Convective rain from the TRMM rain algorithms is less than that from ground radar measurements, while TRMM stratiform rain is much greater. Rain area coverage from 2A-12 is also in reasonable agreement with ground radar measurements, with about 25% more over ocean and 25% less over land and coastal areas. Retrieved rain rates from the improved (Version 6) 2A-12 algorithm will be compared to 2A-25, 2B-31, and ground-based radar measurements to evaluate the impact of improvements to 2A-12 in Version 6. An important improvement to the Version 6 2A-12 algorithm is the retrieval of Q1/Q2 (latent heating/drying) profiles in addition to the surface rain rate and hydrometeor profiles. In order to ascertain the credibility of the new products, retrieved Q1/Q2 profiles are compared to independent ground-based estimates. Analyses of dual-Doppler radar data in conjunction with coincident rawinsonde data yield estimates of the vertical distributions of diabatic heating/drying at high horizontal resolution for selected cases over the Kwajalein and LBA field sites. The estimated vertical heating/drying structures appear to be reasonable. Comparisons of Q1/Q2 profiles from Version 6 2A-12 and the ground-based estimates are in progress. Retrieved Q1/Q2 structures will also be compared to MM5 hurricane simulations for selected cases. The results of these intercomparisons will be presented at the conference.

The microwave radiometer spacecraft: A design study

NASA Technical Reports Server (NTRS)

Wright, R. L. (Editor)

1981-01-01

A large passive microwave radiometer spacecraft with near all weather capability of monitoring soil moisture for global crop forecasting was designed. The design, emphasizing large space structures technology, characterized the mission hardware at the conceptual level in sufficient detail to identify enabling and pacing technologies. Mission and spacecraft requirements, design and structural concepts, electromagnetic concepts, and control concepts are addressed.

Passive microwave studies of snowpack properties. [Walden and Steamboat Spring, Colorado

NASA Technical Reports Server (NTRS)

Hall, D. K.; Chang, A. T. C.; Foster, J. L.; Rango, A.; Schmugge, T.

1978-01-01

Microwave brightness temperatures were measured for the snowpacks at Walden and Steamboat Springs, Colorado during 1976 and 1977 aircraft experiments. Variations in measured brightness temperatures are attributed to snow grain and crystal sizes, liquid water content, and snowpack temperature. Results demonstrate that shorter wavelength radiation is scattered more strongly than longer wavelength radiation.

Soil Moisture Active/Passive (SMAP) L-band microwave radiometer post-launch calibration

USDA-ARS?s Scientific Manuscript database

The SMAP microwave radiometer is a fully-polarimetric L-band radiometer flown on the SMAP satellite in a 6 AM / 6 PM sun-synchronous orbit at 685-km altitude. Since April 2015, the radiometer has been under calibration and validation to assess the quality of the radiometer L1B data product. Calibrat...

Passive Microwave Rainfall Estimates from the GPM Mission

NASA Astrophysics Data System (ADS)

Kummerow, Christian; Petkovic, Veljko

2017-04-01

The Global Precipitation Measurement (GPM) mission was launched in February 2014 as a joint mission between JAXA from Japan and NASA from the United States. GPM carries a state of the art dual-frequency precipitation radar and a multi-channel passive microwave radiometer that acts not only to enhance the radar's retrieval capability, but also as a reference for a constellation of existing satellites carrying passive microwave sensors. In March of 2016, GPM released Version 4 of its precipitation products that consists of radar, radiometer, and combined radar/radiometer products. The precipitation products from these sensors or sensor combination are consistent by design and show relatively minor differences in the mean global sense. Closer examination of the biases, however, reveals regional biases between active and passive sensors that can be directly related top the nature of the convection. By looking at cloud systems instead of individual satellite pixels, the relationship between biases and the large scale environmental state become obvious. Organized convection, which occurs more readily in regimes with large Convective Available Potential Energy (CAPE) and shear tend to drive biases in different directions than isolated convection. This is true over both land and ocean. This talk will present the latest findings and explore these discrepancies from a physical perspective in order to gain some understanding between cloud structures, information content, and retrieval differences. This analysis will be used to then drive a bigger picture of how GPM's latest results inform the Global Water and Energy budgets.

SLAPex Freeze/Thaw 2015: The First Dedicated Soil Freeze/Thaw Airborne Campaign

NASA Technical Reports Server (NTRS)

Kim, Edward; Wu, Albert; DeMarco, Eugenia; Powers, Jarrett; Berg, Aaron; Rowlandson, Tracy; Freeman, Jacqueline; Gottfried, Kurt; Toose, Peter; Roy, Alexandre;

Day 1 for the Integrated Multi-Satellite Retrievals for GPM (IMERG) Data Sets

NASA Astrophysics Data System (ADS)

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

2014-12-01

The Integrated Multi-satellitE Retrievals for GPM (IMERG) is designed to compute the best time series of (nearly) global precipitation from "all" precipitation-relevant satellites and global surface precipitation gauge analyses. IMERG was developed to use GPM Core Observatory data as a reference for the international constellation of satellites of opportunity that constitute the GPM virtual constellation. Computationally, IMERG is a unified U.S. algorithm drawing on strengths in the three contributing groups, whose previous work includes: 1) the TRMM Multi-satellite Precipitation Analysis (TMPA); 2) the CPC Morphing algorithm with Kalman Filtering (K-CMORPH); and 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS). We review the IMERG design, development, testing, and current status. IMERG provides 0.1°x0.1° half-hourly data, and will be run at multiple times, providing successively more accurate estimates: 4 hours, 8 hours, and 2 months after observation time. In Day 1 the spatial extent is 60°N-S, for the period March 2014 to the present. In subsequent reprocessing the data will extend to fully global, covering the period 1998 to the present. Both the set of input data set retrievals and the IMERG system are substantially different than those used in previous U.S. products. The input passive microwave data are all being produced with GPROF2014, which is substantially upgraded compared to previous versions. For the first time, this includes microwave sounders. Accordingly, there is a strong need to carefully check the initial test data sets for performance. IMERG output will be illustrated using pre-operational test data, including the variety of supporting fields, such as the merged-microwave and infrared estimates, and the precipitation type. Finally, we will summarize the expected release of various output products, and the subsequent reprocessing sequence.

Satellite Based Soil Moisture Product Validation Using NOAA-CREST Ground and L-Band Observations

NASA Astrophysics Data System (ADS)

Norouzi, H.; Campo, C.; Temimi, M.; Lakhankar, T.; Khanbilvardi, R.

2015-12-01

Soil moisture content is among most important physical parameters in hydrology, climate, and environmental studies. Many microwave-based satellite observations have been utilized to estimate this parameter. The Advanced Microwave Scanning Radiometer 2 (AMSR2) is one of many remotely sensors that collects daily information of land surface soil moisture. However, many factors such as ancillary data and vegetation scattering can affect the signal and the estimation. Therefore, this information needs to be validated against some "ground-truth" observations. NOAA - Cooperative Remote Sensing and Technology (CREST) center at the City University of New York has a site located at Millbrook, NY with several insitu soil moisture probes and an L-Band radiometer similar to Soil Moisture Passive and Active (SMAP) one. This site is among SMAP Cal/Val sites. Soil moisture information was measured at seven different locations from 2012 to 2015. Hydra probes are used to measure six of these locations. This study utilizes the observations from insitu data and the L-Band radiometer close to ground (at 3 meters height) to validate and to compare soil moisture estimates from AMSR2. Analysis of the measurements and AMSR2 indicated a weak correlation with the hydra probes and a moderate correlation with Cosmic-ray Soil Moisture Observing System (COSMOS probes). Several differences including the differences between pixel size and point measurements can cause these discrepancies. Some interpolation techniques are used to expand point measurements from 6 locations to AMSR2 footprint. Finally, the effect of penetration depth in microwave signal and inconsistencies with other ancillary data such as skin temperature is investigated to provide a better understanding in the analysis. The results show that the retrieval algorithm of AMSR2 is appropriate under certain circumstances. This validation algorithm and similar study will be conducted for SMAP mission. Keywords: Remote Sensing, Soil Moisture, AMSR2, SMAP, L-Band.

Global-scale assessment and combination of SMAP with ASCAT (Active) and AMSR2 (Passive) soil moisture products

USDA-ARS?s Scientific Manuscript database

Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily temporal resolution. In the present study, we first inter-compared global-scale error patterns and comb...

Estimation of Rainfall Rates from Passive Microwave Remote Sensing.

NASA Astrophysics Data System (ADS)

Sharma, Awdhesh Kumar

Rainfall rates have been estimated using the passive microwave and visible/infrared remote sensing techniques. Data of September 14, 1978 from the Scanning Multichannel Microwave Radiometer (SMMR) on board SEA SAT-A and the Visible and Infrared Spin Scan Radiometer (VISSR) on board GOES-W (Geostationary Operational Environmental Satellite - West) was obtained and analyzed for rainfall rate retrieval. Microwave brightness temperatures (MBT) are simulated, using the microwave radiative transfer model (MRTM) and atmospheric scattering models. These MBT were computed as a function of rates of rainfall from precipitating clouds which are in a combined phase of ice and water. Microwave extinction due to ice and liquid water are calculated using Mie-theory and Gamma drop size distributions. Microwave absorption due to oxygen and water vapor are based on the schemes given by Rosenkranz, and Barret and Chung. The scattering phase matrix involved in the MRTM is found using Eddington's two stream approximation. The surface effects due to winds and foam are included through the ocean surface emissivity model. Rainfall rates are then inverted from MBT using the optimization technique "Leaps and Bounds" and multiple linear regression leading to a relationship between the rainfall rates and MBT. This relationship has been used to infer the oceanic rainfall rates from SMMR data. The VISSR data has been inverted for the rainfall rates using Griffith's scheme. This scheme provides an independent means of estimating rainfall rates for cross checking SMMR estimates. The inferred rainfall rates from both techniques have been plotted on a world map for comparison. A reasonably good correlation has been obtained between the two estimates.

Passive microwave remote sensing of an anisotropic random-medium layer

NASA Technical Reports Server (NTRS)

Lee, J. K.; Kong, J. A.

1985-01-01

The principle of reciprocity is invoked to calculate the brightness temperatures for passive microwave remote sensing of a two-layer anisotropic random medium. The bistatic scattering coefficients are first computed with the Born approximation and then integrated over the upper hemisphere to be subtracted from unity, in order to obtain the emissivity for the random-medium layer. The theoretical results are illustrated by plotting the emissivities as functions of viewing angles and polarizations. They are used to interpret remote sgnsing data obtained from vegetation canopy where the anisotropic random-medium model applies. Field measurements with corn stalks arranged in various configurations with preferred azimuthal directions are successfully interpreted with this model.

Detecting Climate Variability in Tropical Rainfall

NASA Astrophysics Data System (ADS)

Berg, W.

2004-05-01

A number of satellite and merged satellite/in-situ rainfall products have been developed extending as far back as 1979. While the availability of global rainfall data covering over two decades and encompassing two major El Niño events is a valuable resource for a variety of climate studies, significant differences exist between many of these products. Unfortunately, issues such as availability often determine the use of a product for a given application instead of an understanding of the strengths and weaknesses of the various products. Significant efforts have been made to address the impact of sparse sampling by satellite sensors of variable rainfall processes by merging various satellite and in-situ rainfall products. These combine high spatial and temporal frequency satellite infrared data with higher quality passive microwave observations and rain gauge observations. Combining such an approach with spatial and temporal averaging of the data can reduce the large random errors inherent in satellite rainfall estimates to very small levels. Unfortunately, systematic biases can and do result in artificial climate signals due to the underconstrained nature of the rainfall retrieval problem. Because all satellite retrieval algorithms make assumptions regarding the cloud structure and microphysical properties, systematic changes in these assumed parameters between regions and/or times results in regional and/or temporal biases in the rainfall estimates. These biases tend to be relatively small compared to random errors in the retrieval, however, when random errors are reduced through spatial and temporal averaging for climate applications, they become the dominant source of error. Whether or not such biases impact the results for climate studies is very much dependent on the application. For example, all of the existing satellite rainfall products capture the increased rainfall in the east Pacific associated with El Niño, however, the resulting tropical response to El Niño is substantially smaller due to decreased rainfall in the west Pacific partially canceling increases in the central and east Pacific. These differences are not limited to the long-term merged rainfall products using infrared data, but are also exist in state-of-the-art rainfall retrievals from the active and passive microwave sensors on board the Tropical Rainfall Measuring Mission (TRMM). For example, large differences exist in the response of tropical mean rainfall retrieved from the TRMM microwave imager (TMI) 2A12 algorithm and the precipitation radar (PR) 2A25 algorithm to the 1997/98 El Niño. To assist scientists attempting to wade through the vast array of climate rainfall products currently available, and to help them determine whether systematic biases in these rainfall products impact the conclusions of a given study, we have developed a Climate Rainfall Data Center (CRDC). The CRDC web site (rain.atmos.colostate.edu/CRDC) provides climate researchers information on the various rainfall datasets available as well as access to experts in the field of satellite rainfall retrievals to assist them in the appropriate selection and use of climate rainfall products.

Calibration and Image Reconstruction for the Hurricane Imaging Radiometer (HIRAD)

NASA Technical Reports Server (NTRS)

Ruf, Christopher; Roberts, J. Brent; Biswas, Sayak; James, Mark W.; Miller, Timothy

2012-01-01

The Hurricane Imaging Radiometer (HIRAD) is a new airborne passive microwave synthetic aperture radiometer designed to provide wide swath images of ocean surface wind speed under heavy precipitation and, in particular, in tropical cyclones. It operates at 4, 5, 6 and 6.6 GHz and uses interferometric signal processing to synthesize a pushbroom imager in software from a low profile planar antenna with no mechanical scanning. HIRAD participated in NASA s Genesis and Rapid Intensification Processes (GRIP) mission during Fall 2010 as its first science field campaign. HIRAD produced images of upwelling brightness temperature over a aprox 70 km swath width with approx 3 km spatial resolution. From this, ocean surface wind speed and column averaged atmospheric liquid water content can be retrieved across the swath. The calibration and image reconstruction algorithms that were used to verify HIRAD functional performance during and immediately after GRIP were only preliminary and used a number of simplifying assumptions and approximations about the instrument design and performance. The development and performance of a more detailed and complete set of algorithms are reported here.

Rainfall Estimation over the Nile Basin using Multi-Spectral, Multi- Instrument Satellite Techniques

NASA Astrophysics Data System (ADS)

Habib, E.; Kuligowski, R.; Sazib, N.; Elshamy, M.; Amin, D.; Ahmed, M.

2012-04-01

Management of Egypt's Aswan High Dam is critical not only for flood control on the Nile but also for ensuring adequate water supplies for most of Egypt since rainfall is scarce over the vast majority of its land area. However, reservoir inflow is driven by rainfall over Sudan, Ethiopia, Uganda, and several other countries from which routine rain gauge data are sparse. Satellite- derived estimates of rainfall offer a much more detailed and timely set of data to form a basis for decisions on the operation of the dam. A single-channel infrared (IR) algorithm is currently in operational use at the Egyptian Nile Forecast Center (NFC). In this study, the authors report on the adaptation of a multi-spectral, multi-instrument satellite rainfall estimation algorithm (Self- Calibrating Multivariate Precipitation Retrieval, SCaMPR) for operational application by NFC over the Nile Basin. The algorithm uses a set of rainfall predictors that come from multi-spectral Infrared cloud top observations and self-calibrate them to a set of predictands that come from the more accurate, but less frequent, Microwave (MW) rain rate estimates. For application over the Nile Basin, the SCaMPR algorithm uses multiple satellite IR channels that have become recently available to NFC from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Microwave rain rates are acquired from multiple sources such as the Special Sensor Microwave/Imager (SSM/I), the Special Sensor Microwave Imager and Sounder (SSMIS), the Advanced Microwave Sounding Unit (AMSU), the Advanced Microwave Scanning Radiometer on EOS (AMSR-E), and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The algorithm has two main steps: rain/no-rain separation using discriminant analysis, and rain rate estimation using stepwise linear regression. We test two modes of algorithm calibration: real- time calibration with continuous updates of coefficients with newly coming MW rain rates, and calibration using static coefficients that are derived from IR-MW data from past observations. We also compare the SCaMPR algorithm to other global-scale satellite rainfall algorithms (e.g., 'Tropical Rainfall Measuring Mission (TRMM) and other sources' (TRMM-3B42) product, and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) CMORPH product. The algorithm has several potential future applications such as: improving the performance accuracy of hydrologic forecasting models over the Nile Basin, and utilizing the enhanced rainfall datasets and better-calibrated hydrologic models to assess the impacts of climate change on the region's water availability using global circulation models and regional climate models.

Sensitivity of Active and Passive Microwave Observations to Soil Moisture during Growing Corn

NASA Astrophysics Data System (ADS)

Judge, J.; Monsivais-Huertero, A.; Liu, P.; De Roo, R. D.; England, A. W.; Nagarajan, K.

2011-12-01

Soil moisture (SM) in the root zone is a key factor governing water and energy fluxes at the land surface and its accurate knowledge is critical to predictions of weather and near-term climate, nutrient cycles, crop-yield, and ecosystem productivity. Microwave observations, such as those at L-band, are highly sensitive to soil moisture in the upper few centimeters (near-surface). The two satellite-based missions dedicated to soil moisture estimation include, the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission and the planned NASA Soil Moisture Active/Passive (SMAP) [4] mission. The SMAP mission will include active and passive sensors at L-band to provide global observations of SM, with a repeat coverage of every 2-3 days. These observations can significantly improve root zone soil moisture estimates through data assimilation into land surface models (LSMs). Both the active (radar) and passive (radiometer) microwave sensors measure radiation quantities that are functions of soil dielectric constant and exhibit similar sensitivities to SM. In addition to the SM sensitivity, radar backscatter is highly sensitive to roughness of soil surface and scattering within the vegetation. These effects may produce a much larger dynamic range in backscatter than that produced due to SM changes alone. In this study, we discuss the field observations of active and passive signatures of growing corn at L-band from several seasons during the tenth Microwave, Water and Energy Balance Experiment (MicroWEX-10) conducted in North Central Florida, and to understand the sensitivity of these signatures to soil moisture under dynamic vegetation conditions. The MicroWEXs are a series of season-long field experiments conducted during the growing seasons of sweet corn, cotton, and energy cane over the past six years (for example, [22]). The corn was planted on July 5 and harvested on September 23, 2011 during MicroWEX-10. The size of the field was 0.04 km2 and the soils at the site were Lakeland fine sand, with 89% sand content by volume. The crop was heavily irrigated via a linear move irrigation system. Every 15-minute ground-based passive and active microwave observations at L-band were conducted at an incidence angle of 40°. In addition, concurrent observations were conducted of soil moisture, temperature, heat flux at various depths in the root zone, along with concurrent micrometeorological conditions. Weekly vegetation sampling included measurements of LAI, green and dry biomass of stems, leaves, and ears, crop height and width, vertical distribution of moisture in the canopy, leaf size and orientation, other phonological observations. Such observations at high temporal density allow detailed sensitivity analyses as the vegetation grows.

Rain-on-snow and ice layer formation detection using passive microwave radiometry: An arctic perspective

NASA Astrophysics Data System (ADS)

Langlois, A.; Royer, A.; Montpetit, B.; Johnson, C. A.; Brucker, L.; Dolant, C.; Richards, A.; Roy, A.

2015-12-01

With the current changes observed in the Arctic, an increase in occurrence of rain-on-snow (ROS) events has been reported in the Arctic (land) over the past few decades. Several studies have established that strong linkages between surface temperatures and passive microwaves do exist, but the contribution of snow properties under winter extreme events such as rain-on-snow events (ROS) and associated ice layer formation need to be better understood that both have a significant impact on ecosystem processes. In particular, ice layer formation is known to affect the survival of ungulates by blocking their access to food. Given the current pronounced warming in northern regions, more frequent ROS can be expected. However, one of the main challenges in the study of ROS in northern regions is the lack of meteorological information and in-situ measurements. The retrieval of ROS occurrence in the Arctic using satellite remote sensing tools thus represents the most viable approach. Here, we present here results from 1) ROS occurrence formation in the Peary caribou habitat using an empirically developed ROS algorithm by our group based on the gradient ratio, 2) ice layer formation across the same area using a semi-empirical detection approach based on the polarization ratio spanning between 1978 and 2013. A detection threshold was adjusted given the platform used (SMMR, SSM/I and AMSR-E), and initial results suggest high-occurrence years as: 1981-1982, 1992-1993; 1994-1995; 1999-2000; 2001-2002; 2002-2003; 2003-2004; 2006-2007; 2007-2008. A trend in occurrence for Banks Island and NW Victoria Island and linkages to caribou population is presented.

A Long-Term and Reproducible Passive Microwave Sea Ice Concentration Data Record for Climate Studies and Monitoring

NASA Technical Reports Server (NTRS)

Peng, G.; Meier, W. N.; Scott, D. J.; Savoie, M. H.

2013-01-01

A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 km × 25 km grid cells in both the Southern and Northern Hemisphere polar regions from 9 July 1987 to 31 December 2007. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The data set, along with detailed data processing steps and error source information, can be found at http://dx.doi.org/10.7265/N5B56GN3.

Snowpack monitoring in North America and Eurasia using passive microwave satellite data

NASA Technical Reports Server (NTRS)

Foster, J. L.; Rango, A.; Hall, D. K.

1980-01-01

Areas of the Canadian high plains, the Montana and North Dakota high plains, and the steppes of central Russia were studied in an effort to determine the utility of spaceborne electrical scanning microwave radiometers (ESMR) for monitoring snow depths in different geographic areas. Significant regression relationships between snow depth and microwave brightness temperatures were developed for each of these homogeneous areas. In the areas investigated, Nimbus 6 (.081 cm) ESMR data produced higher correlations than Nimbus 5 (1.55 cm) ESMR data in relating microwave brightness temperature and snow depth from one area to another because different geographic areas are likely to have different snowpack conditions.

Development of a validation model for the defense meteorological satellite program's special sensor microwave imager

NASA Technical Reports Server (NTRS)

Swift, C. T.; Goodberlet, M. A.; Wilkerson, J. C.

1990-01-01

The Defence Meteorological Space Program's (DMSP) Special Sensor Microwave/Imager (SSM/I), an operational wind speed algorithm was developed. The algorithm is based on the D-matrix approach which seeks a linear relationship between measured SSM/I brightness temperatures and environmental parameters. D-matrix performance was validated by comparing algorithm derived wind speeds with near-simultaneous and co-located measurements made by off-shore ocean buoys. Other topics include error budget modeling, alternate wind speed algorithms, and D-matrix performance with one or more inoperative SSM/I channels.

Beaufort/Bering 1979 microwave remote sensing data catalog report, 14-24 March 1979

NASA Technical Reports Server (NTRS)

Hirstein, W. S.; Hennigar, H. F.; Schaffner, S. K.; Delnore, V. E.; Grantham, W. L.

1983-01-01

The airborne microwave remote sending measurements obtained by the Langley Research Center in support of the 1979 Sea-Ice Radar Experiment (SIRE) in the Beaufort and Bering Seas are discussed. The remote sensing objective of SIRE was to define correlations between both active and passive microwave signatures and ice phenomena assocated with practical applications in the Arctic. The instruments used by Langley during SIRE include the stepped frequency microwave radiometer (SFMR), the airborne microwave scatterometer (AMSCAT), the precision radiation thermometer (PRT-5), and metric aerial photography. Remote sensing data are inventoried and cataloged in a user-friendly format. The data catalog is presented as time-history plots when and where data were obtained as well as the sensor configuration.

Time-of-Flight Microwave Camera.

PubMed

Charvat, Gregory; Temme, Andrew; Feigin, Micha; Raskar, Ramesh

2015-10-05

Microwaves can penetrate many obstructions that are opaque at visible wavelengths, however microwave imaging is challenging due to resolution limits associated with relatively small apertures and unrecoverable "stealth" regions due to the specularity of most objects at microwave frequencies. We demonstrate a multispectral time-of-flight microwave imaging system which overcomes these challenges with a large passive aperture to improve lateral resolution, multiple illumination points with a data fusion method to reduce stealth regions, and a frequency modulated continuous wave (FMCW) receiver to achieve depth resolution. The camera captures images with a resolution of 1.5 degrees, multispectral images across the X frequency band (8 GHz-12 GHz), and a time resolution of 200 ps (6 cm optical path in free space). Images are taken of objects in free space as well as behind drywall and plywood. This architecture allows "camera-like" behavior from a microwave imaging system and is practical for imaging everyday objects in the microwave spectrum.

1982 International Geoscience and Remote Sensing Symposium, Munich, West Germany, June 1-4, 1982, Digest. Volumes 1 and 2

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

Not Available

1982-01-01

Theoretical and experimental data which have defined and/or extended the effectiveness of remote sensing operations are explored, with consideration given to both scientific and commercial activities. The remote sensing of soil moisture, the sea surface, and oil slicks is discussed, as are programs using satellites for studying geodynamics and geodesy, currents and waves, and coastal zones. NASA, Canadian, and Japanese radar and microwave passive and active systems are described, together with algorithms and techniques for image processing and classification. The SAR-580 project is outlined, and attention is devoted to satellite applications in investigations of the structure of the atmosphere, agriculturemore » and land use, and geology. Design and performance features of various optical scanner, radar, and multispectral data processing systems and procedures are detailed.« less

Microwave soil moisture estimation in humid and semiarid watersheds

NASA Technical Reports Server (NTRS)

O'Neill, P. E.; Jackson, T. J.; Chauhan, N. S.; Seyfried, M. S.

1993-01-01

Land surface hydrologic-atmospheric interactions in humid and semi-arid watersheds were investigated. Active and passive microwave sensors were used to estimate the spatial and temporal distribution of soil moisture at the catchment scale in four areas. Results are presented and discussed. The eventual use of this information in the analysis and prediction of associated hydrologic processes is examined.

Interannual variation of the surface temperature of tropical forests from satellite observations

DOE PAGES

Gao, Huilin; Zhang, Shuai; Fu, Rong; ...

2016-01-01

Land surface temperatures (LSTs) within tropical forests contribute to climate variations. However, observational data are very limited in such regions. This study used passive microwave remote sensing data from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS), providing observations under all weather conditions, to investigate the LST over the Amazon and Congo rainforests. The SSM/I and SSMIS data were collected from 1996 to 2012. The morning and afternoon observations from passive microwave remote sensing facilitate the investigation of the interannual changes of LST anomalies on a diurnal basis. As a result of the variability ofmore » cloud cover and the corresponding reduction of solar radiation, the afternoon LST anomalies tend to vary more than the morning LST anomalies. The dominant spatial and temporal patterns for interseasonal variations of the LST anomalies over the tropical rainforest were analyzed. The impacts of droughts and El Niños on this LST were also investigated. Lastly, the differences between early morning and late afternoon LST anomalies were identified by the remote sensing product, with the morning LST anomalies controlled by humidity (according to comparisons with the National Centers for Environmental Prediction (NCEP) reanalysis data).« less

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

Gao, Huilin; Zhang, Shuai; Fu, Rong

Land surface temperatures (LSTs) within tropical forests contribute to climate variations. However, observational data are very limited in such regions. This study used passive microwave remote sensing data from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS), providing observations under all weather conditions, to investigate the LST over the Amazon and Congo rainforests. The SSM/I and SSMIS data were collected from 1996 to 2012. The morning and afternoon observations from passive microwave remote sensing facilitate the investigation of the interannual changes of LST anomalies on a diurnal basis. As a result of the variability ofmore » cloud cover and the corresponding reduction of solar radiation, the afternoon LST anomalies tend to vary more than the morning LST anomalies. The dominant spatial and temporal patterns for interseasonal variations of the LST anomalies over the tropical rainforest were analyzed. The impacts of droughts and El Niños on this LST were also investigated. Lastly, the differences between early morning and late afternoon LST anomalies were identified by the remote sensing product, with the morning LST anomalies controlled by humidity (according to comparisons with the National Centers for Environmental Prediction (NCEP) reanalysis data).« less

Botswana water and surface energy balance research program. Part 1: Integrated approach and field campaign results

NASA Technical Reports Server (NTRS)

Vandegriend, A. A.; Owe, M.; Vugts, H. F.; Ramothwa, G. K.

1992-01-01

The Botswana water and surface energy balance research program was developed to study and evaluate the integrated use of multispectral satellite remote sensing for monitoring the hydrological status of the Earth's surface. Results of the first part of the program (Botswana 1) which ran from 1 Jan. 1988 - 31 Dec. 1990 are summarized. Botswana 1 consisted of two major, mutually related components: a surface energy balance modeling component, built around an extensive field campaign; and a passive microwave research component which consisted of a retrospective study of large scale moisture conditions and Nimbus scanning multichannel microwave radiometer microwave signatures. The integrated approach of both components in general are described and activities performed during the surface energy modeling component including the extensive field campaign are summarized. The results of the passive microwave component are summarized. The key of the field campaign was a multilevel approach, whereby measurements by various similar sensors were made at several altitudes and resolution. Data collection was performed at two adjacent sites of contrasting surface character. The following measurements were made: micrometeorological measurements, surface temperatures, soil temperatures, soil moisture, vegetation (leaf area index and biomass), satellite data, aircraft data, atmospheric soundings, stomatal resistance, and surface emissivity.

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

NASA Astrophysics Data System (ADS)

Zhou, Lu; Xu, Shiming; Liu, Jiping; Wang, Bin

2018-03-01

The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

A satellite observational and numerical study of precipitation characteristics in western North Atlantic tropical cyclones

NASA Technical Reports Server (NTRS)

Rodgers, Edward B.; Chang, Simon W.; Pierce, Harold F.

1994-01-01

Special Sensor Microwave/Imager (SSM/I) observations were used to examine the spatial and temporal changes of the precipitation characteristics of tropical cyclones. SSM/I observations were also combined with the results of a tropical cyclone numerical model to examine the role of inner-core diabatic heating in subsequent intensity changes of tropical cyclones. Included in the SSM/I observations were rainfall characteristics of 18 named western North Atlantic tropical cyclones between 1987 and 1989. The SSM/I rain-rate algorithm that employed the 85-GHz channel provided an analysis of the rain-rate distribution in greater detail. However, the SSM/I algorithm underestimated the rain rates when compared to in situ techniques but appeared to be comparable to the rain rates obtained from other satellite-borne passive microwave radiometers. The analysis of SSM/I observations found that more intense systems had higher rain rates, more latent heat release, and a greater contribution from heavier rain to the total tropical cyclone rainfall. In addition, regions with the heaviest rain rates were found near the center of the most intense tropical cyclones. Observational analysis from SSM/I also revealed that the greatest rain rates in the inner-core regions were found in the right half of fast-moving cyclones, while the heaviest rain rates in slow-moving tropical cyclones were found in the forward half. The combination of SSM/I observations and an interpretation of numerical model simulations revealed that the correlation between changes in the inner core diabetic heating and the subsequent intensity became greater as the tropical cyclones became more intense.

PolarCube: A High Resolution Passive Microwave Satellite for Sounding and Imaging at 118 GHz

NASA Astrophysics Data System (ADS)

Weaver, R. L.; Gallaher, D. W.; Gasiewski, A. J.; Sanders, B.; Periasamy, L.; Hwang, K.; Alvarenga, G.; Hickey, A. M.

2013-12-01

PolarCube is a 3U CubeSat hosting an eight-channel passive microwave spectrometer operating at the 118.7503 GHz oxygen resonance that is currently in development. The project has an anticipated launch date in early 2015. It is currently being designed to operate for approximately12 months on orbit to provide the first global 118-GHz spectral imagery of the Earth over full seasonal cycle and to sound Arctic vertical temperature structure. The principles used by PolarCube for temperature sounding are well established in number of peer-reviewed papers going back more than two decades, although the potential for sounding from a CubeSat has never before been demonstrated in space. The PolarCube channels are selected to probe atmospheric emission over a range of vertical levels from the surface to lower stratosphere. This capability has been available operationally for over three decades, but at lower frequencies and higher altitudes that do not provide the spatial resolution that will be achieved by PolarCube. While the NASA JPSS ATMS satellite sensor provides global coverage at ~32 km resolution, the PolarCube will improve on this resolution by a factor of two, thus facilitating the primary science goal of determining sea ice concentration and extent while at the same time collecting profile data on atmospheric temperature. Additionally, we seek to correlate freeze-thaw line data from SMAP with our near simultaneously collected atmospheric temperature data. In addition to polar science, PolarCube will provide a first demonstration of a very low cost passive microwave sounder that if operated in a fleet configuration would have the potential to fulfill the goals of the Precipitation Atmospheric Temperature and Humidity (PATH) mission, as defined in the NRC Decadal Survey. PolarCube 118-GHz passive microwave spectrometer in deployed configuration

Advances in satellite oceanography

NASA Technical Reports Server (NTRS)

Brown, O. B.; Cheney, R. E.

1983-01-01

Technical advances and recent applications of active and passive satellite remote sensing techniques to the study of oceanic processes are summarized. The general themes include infrared and visible radiometry, active and passive microwave sensors, and buoy location systems. The surface parameters of sea surface temperature, windstream, sea state, altimetry, color, and ice are treated as applicable under each of the general methods.

Displacement method and apparatus for reducing passivated metal powders and metal oxides

DOEpatents

Morrell,; Jonathan S. , Ripley; Edward, B [Knoxville, TN

2009-05-05

A method of reducing target metal oxides and passivated metals to their metallic state. A reduction reaction is used, often combined with a flux agent to enhance separation of the reaction products. Thermal energy in the form of conventional furnace, infrared, or microwave heating may be applied in combination with the reduction reaction.

Results and experiences from the NATO Common Shield DAT#7 experiment for the Defence Against Terrorism program

NASA Astrophysics Data System (ADS)

Peichl, Markus; Dill, Stephan; Jirousek, Matthias; Süß, Helmut

2009-05-01

Passive microwave imaging allows a daytime independent observation and examination of objects and persons without artificial exposure under nearly all weather conditions. The penetration capability of microwaves allows the detection of hidden objects like weapons and explosive devices under the clothing. In August/September 2008 a comprehensive military experiment was conducted by the German armed forces at the naval base Eckernfoerde, Germany. One activity in the Eckernfoerde trial was the simulation of a military entrance portal by a tent including various imaging and a chemical sensor suite. Besides commercial optical and infrared cameras various passive millimeter-wave imagers have been used from different German research institutions. The DLR Microwaves and Radar Institute, Department for Reconnaissance and Security (HR-AS), provided an imaging radiometer scanner operating at W band. A multitude of situations have been simulated and many persons carrying hidden objects under their clothing have been scanned. Some exemplary results from the trial are shown and discussed in the paper.

Seasonal Snow Extent and Snow Volume in South America Using SSM/I Passive Microwave Data

NASA Technical Reports Server (NTRS)

Foster, James L.; Chang, A. T. C.; Hall, D. K.; Kelly, R.; Houser, Paul (Technical Monitor)

2001-01-01

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1992-1998, both snow cover extent and snow depth (snow mass) were investigated during the winter months (May-August) in the Patagonia region of Argentina. Since above normal temperatures in this region are typically above freezing, the coldest winter month was found to be not only the month having the most extensive snow cover but also the month having the deepest snows. For the seven-year period of this study, the average snow cover extent (May-August) was about 0.46 million sq km and the average monthly snow mass was about 1.18 x 10(exp 13) kg. July 1992 was the month having the greatest snow extent (nearly 0.8 million sq km) and snow mass (approximately 2.6 x 10(exp 13) kg).

An Algorithm to Generate Deep-Layer Temperatures from Microwave Satellite Observations for the Purpose of Monitoring Climate Change. Revised

NASA Technical Reports Server (NTRS)

Goldberg, Mitchell D.; Fleming, Henry E.

1994-01-01

An algorithm for generating deep-layer mean temperatures from satellite-observed microwave observations is presented. Unlike traditional temperature retrieval methods, this algorithm does not require a first guess temperature of the ambient atmosphere. By eliminating the first guess a potentially systematic source of error has been removed. The algorithm is expected to yield long-term records that are suitable for detecting small changes in climate. The atmospheric contribution to the deep-layer mean temperature is given by the averaging kernel. The algorithm computes the coefficients that will best approximate a desired averaging kernel from a linear combination of the satellite radiometer's weighting functions. The coefficients are then applied to the measurements to yield the deep-layer mean temperature. Three constraints were used in deriving the algorithm: (1) the sum of the coefficients must be one, (2) the noise of the product is minimized, and (3) the shape of the approximated averaging kernel is well-behaved. Note that a trade-off between constraints 2 and 3 is unavoidable. The algorithm can also be used to combine measurements from a future sensor (i.e., the 20-channel Advanced Microwave Sounding Unit (AMSU)) to yield the same averaging kernel as that based on an earlier sensor (i.e., the 4-channel Microwave Sounding Unit (MSU)). This will allow a time series of deep-layer mean temperatures based on MSU measurements to be continued with AMSU measurements. The AMSU is expected to replace the MSU in 1996.

Passive microwave remote sensing of salinity in coastal zones

NASA Technical Reports Server (NTRS)

Swift, Calvin T.; Blume, Hans-Juergen C.; Kendall, Bruce M.

1987-01-01

The theory of measuring coastal-zone salinity from airborne microwave radiometers is developed. The theory, as presented, shows that precision measurements of salinity favor the lower microwave frequencies. To this end, L- and S-Band systems were built, and the flight results have shown that accuracies of at least one part per thousand were achieved.The aircraft results focus on flights conducted over the Chesapeake Bay and the mouth of the Savanna River off the Georgia Coast. This paper presents no new work, but rather summarizes the capabilities of the remote sensing technique.

A Comparison between Lightning Activity and Passive Microwave Measurements

NASA Technical Reports Server (NTRS)

Kevin, Driscoll T.; Hugh, Christian J.; Goodman, Steven J.

1999-01-01

A recent examination of data from the Lightning Imaging Sensor (LIS) and the TRMM Microwave Imager (TMI) suggests that storm with the highest frequency of lightning also possess the most pronounced microwave scattering signatures at 37 and 85 GHz. This study demonstrates a clear dependence between lightning and the passive microwave measurements, and accentuates how direct the relationship really is between cloud ice and lightning activity. In addition, the relationship between the quantity of ice content and the frequency of lightning (not just the presence of lightning) , is consistent throughout the seasons in a variety of regimes. Scatter plots will be presented which show the storm-averaged brightness temperatures as a function of the lightning density of the storms (L/Area) . In the 85 GHz and 37 GHz scatter plots, the brightness temperature is presented in the form Tb = k1 x log10(L/Area) + k2, where the slope of the regression, k1, is 58 for the 85 GHz relationship and 30.7 for the 37 GHz relationship. The regression for both these fits showed a correlation of 0.76 (r2 = 0.58), which is quite promising considering the simple procedure used to make the comparisons, which have not yet even been corrected for the view angle differences between the instruments, or the polarization corrections in the microwave imager.

Contemplating Synergistic Algorithms for the NASA ACE Mission

NASA Technical Reports Server (NTRS)

Mace, Gerald G.; Starr, David O.; Marchand, Roger; Ackerman, Steven A.; Platnick, Steven E.; Fridlind, Ann; Cooper, Steven; Vane, Deborah G.; Stephens, Graeme L.

2013-01-01

ACE is a proposed Tier 2 NASA Decadal Survey mission that will focus on clouds, aerosols, and precipitation as well as ocean ecosystems. The primary objective of the clouds component of this mission is to advance our ability to predict changes to the Earth's hydrological cycle and energy balance in response to climate forcings by generating observational constraints on future science questions, especially those associated with the effects of aerosol on clouds and precipitation. ACE will continue and extend the measurement heritage that began with the A-Train and that will continue through Earthcare. ACE planning efforts have identified several data streams that can contribute significantly to characterizing the properties of clouds and precipitation and the physical processes that force these properties. These include dual frequency Doppler radar, high spectral resolution lidar, polarimetric visible imagers, passive microwave and submillimeter wave radiometry. While all these data streams are technologically feasible, their total cost is substantial and likely prohibitive. It is, therefore, necessary to critically evaluate their contributions to the ACE science goals. We have begun developing algorithms to explore this trade space. Specifically, we will describe our early exploratory algorithms that take as input the set of potential ACE-like data streams and evaluate critically to what extent each data stream influences the error in a specific cloud quantity retrieval.

The Effects of Rainfall Inhomogeneity on Climate Variability of Rainfall Estimated from Passive Microwave Sensors

NASA Technical Reports Server (NTRS)

Kummerow, Christian; Poyner, Philip; Berg, Wesley; Thomas-Stahle, Jody

2007-01-01

Passive microwave rainfall estimates that exploit the emission signal of raindrops in the atmosphere are sensitive to the inhomogeneity of rainfall within the satellite field of view (FOV). In particular, the concave nature of the brightness temperature (T(sub b)) versus rainfall relations at frequencies capable of detecting the blackbody emission of raindrops cause retrieval algorithms to systematically underestimate precipitation unless the rainfall is homogeneous within a radiometer FOV, or the inhomogeneity is accounted for explicitly. This problem has a long history in the passive microwave community and has been termed the beam-filling error. While not a true error, correcting for it requires a priori knowledge about the actual distribution of the rainfall within the satellite FOV, or at least a statistical representation of this inhomogeneity. This study first examines the magnitude of this beam-filling correction when slant-path radiative transfer calculations are used to account for the oblique incidence of current radiometers. Because of the horizontal averaging that occurs away from the nadir direction, the beam-filling error is found to be only a fraction of what has been reported previously in the literature based upon plane-parallel calculations. For a FOV representative of the 19-GHz radiometer channel (18 km X 28 km) aboard the Tropical Rainfall Measuring Mission (TRMM), the mean beam-filling correction computed in this study for tropical atmospheres is 1.26 instead of 1.52 computed from plane-parallel techniques. The slant-path solution is also less sensitive to finescale rainfall inhomogeneity and is, thus, able to make use of 4-km radar data from the TRMM Precipitation Radar (PR) in order to map regional and seasonal distributions of observed rainfall inhomogeneity in the Tropics. The data are examined to assess the expected errors introduced into climate rainfall records by unresolved changes in rainfall inhomogeneity. Results show that global mean monthly errors introduced by not explicitly accounting for rainfall inhomogeneity do not exceed 0.5% if the beam-filling error is allowed to be a function of rainfall rate and freezing level and does not exceed 2% if a universal beam-filling correction is applied that depends only upon the freezing level. Monthly regional errors can be significantly larger. Over the Indian Ocean, errors as large as 8% were found if the beam-filling correction is allowed to vary with rainfall rate and freezing level while errors of 15% were found if a universal correction is used.

Current Development of Global Precipitation Mission (GPM)

NASA Technical Reports Server (NTRS)

Smith, Eric A.; Starr, David (Technical Monitor)

2001-01-01

The scientific success of the Tropical Rainfall Measuring Mission (TRMM) and additional satellite-focused precipitation retrieval projects, particularly those based on use of passive microwave radiometer measurements, have paved the way for a more advanced global precipitation mission. The new mission is motivated by a number of scientific questions that TRMM research has posed over a range of space-time scales and within a variety of scientific disciplines that are becoming more integrated into earth system science modeling. Added to this success is the realization that satellite rainfall datasets are now a foremost tool in understanding global climate variability out to decadal scales and beyond. This progress has motivated a comprehensive global measuring strategy -- leading to the "Global Precipitation Mission" (GPM). GPM is planning to expand the scope of rainfall measurement through use of a satellite constellation. The intent is to address looming scientific questions arising in the context of global climate-water cycle interactions, hydrometeorology, weather prediction & prediction of fresh water resources, the global carbon budget, and biogeochemical cycles. This talk overviews the status and scientific agenda of this mission currently planned for launch in the 2007-2008 time frame. The GPM notional design involves a 10-member satellite constellation, one of which will be an advanced TRMM-like "core" satellite carrying a dual-frequency Ku-Ka band radar (DFPR) and a TMI-like radiometer. The other nine members of the constellation will be considered daughters of the core satellite, each carrying some type of passive microwave radiometer measuring across the 10.7-85 GHz frequency range -- likely to include a combination of lightweight satellites and co-existing operational/experimental satellites carrying passive microwave radiometers (i.e., 2 DMSP/SSMISs, GCOM-B1/AMSR-J, & Megha Tropiques/MADRAS). The goal behind the constellation is to achieve no worse than 3-hour sampling at any spot on the globe. The constellation's orbit architecture will consist of a mix of sun-synchronous and non- sun-synchronous daughter satellites, with the core satellite providing relevant measurements on internal cloud-precipitation microphysical processes plus "training-calibrating" information to be used with the retrieval algorithms for the daughter satellite measurements. The GPM is organized internationally, currently involving a partnership between NASA in the US, NASDA in Japan, and ESA in Europe (representing the European community nations). The mission is expected to involve additional international participants, sister agencies to the mainstream space agencies, and a diverse collection scientists from academia, government, and the private sector.

Multi-Sensor Approach to Mapping Snow Cover Using Data From NASA's EOS Aqua and Terra Spacecraft

NASA Astrophysics Data System (ADS)

Armstrong, R. L.; Brodzik, M. J.

2003-12-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Over the past several decades both optical and passive microwave satellite data have been utilized for snow mapping at the regional to global scale. For the period 1978 to 2002, we have shown earlier that both passive microwave and visible data sets indicate a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are, depending on season, less than those provided by the visible satellite data and the visible data typically show higher monthly variability. Snow mapping using optical data is based on the magnitude of the surface reflectance while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Our previous work has defined the respective advantages and disadvantages of these two types of satellite data for snow cover mapping and it is clear that a blended product is optimal. We present a multi-sensor approach to snow mapping based both on historical data as well as data from current NASA EOS sensors. For the period 1978 to 2002 we combine data from the NOAA weekly snow charts with passive microwave data from the SMMR and SSM/I brightness temperature record. For the current and future time period we blend MODIS and AMSR-E data sets. An example of validation at the brightness temperature level is provided through the comparison of AMSR-E with data from the well-calibrated heritage SSM/I sensor over a large homogeneous snow-covered surface (Dome C, Antarctica). Prototype snow cover maps from AMSR-E compare well with maps derived from SSM/I. Our current blended product is being developed in the 25 km EASE-Grid while the MODIS data being used are in the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) allowing the blended product to indicate percent snow cover over the larger grid cell. Relationships between the percent area covered by snow as indicated by the MODIS data and the threshold for the appearance of snow as indicated by the passive microwave data are presented. Both MODIS and AMSR-E data have enhanced spatial resolution compared to the earlier data sources and examples of how this increased spatial resolution results in more accurate snow cover maps are presented. A wide range of validation data sets are being employed in this study including the NASA Cold Lands Processes Field Experiment undertaken in Colorado during 2002 and 2003.

Active learning for clinical text classification: is it better than random sampling?

PubMed

Figueroa, Rosa L; Zeng-Treitler, Qing; Ngo, Long H; Goryachev, Sergey; Wiechmann, Eduardo P

2012-01-01

This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results. Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences. The DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm. For medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty.

Active learning for clinical text classification: is it better than random sampling?

PubMed Central

Figueroa, Rosa L; Ngo, Long H; Goryachev, Sergey; Wiechmann, Eduardo P

2012-01-01

Objective This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. Design Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results. Measurements Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences. Results The DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm. Conclusion For medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty. PMID:22707743

Passive Microwave Measurements of Salinity: The Gulf Stream Experiment

NASA Technical Reports Server (NTRS)

LeVine, D. M.; Koblinsky, C.; Haken, M.; Howden, S.; Bingham, F.; Hildebrand, Peter H. (Technical Monitor)

2001-01-01

Passive microwave sensors at L-band (1.4 GHz) operating from aircraft have demonstrated that salinity can be measured with sufficient accuracy (I psu) to be scientifically meaningful in coastal waters. However, measuring salinity in the open ocean presents unresolved issues largely because of the much greater accuracy (approximately 0.2 psu) required of global maps to be scientifically viable. The development of a satellite microwave instrument to make global measurements of SSS (Sea Surface Salinity) is the focus of a joint JPL/GSFC/NASA ocean research program called Aquarius. In the summer of 1999 a series of measurements called, The Gulf Stream Experiment, were conducted as part of research at the Goddard Space Flight Center to test the potential for passive microwave remote sensing of salinity in the open ocean. The measurements consisted of airborne microwave instruments together with ships and drifters for surface truth. The study area was a 200 km by 100 km rectangle about 250 km east of Delaware Bay between the continental shelf waters and north wall of the Gulf Stream. The primary passive instruments were the ESTAR radiometer (L-band, H-pol) and the SLFMR radiometer (L-band, V-pol). In addition, the instruments on the aircraft included a C-band radiometer (ACMR), an ocean wave scatterometer (ROWS) and an infrared radiometer (for surface temperature). These instruments were mounted on the NASA P-3 Orion aircraft. Sea surface measurements consisted of thermosalinograph data provided by the R/V Cape Henlopen and the MN Oleander, and data from salinity and temperature sensors on three surface drifters deployed from the R/V Cape Henlopen. The primary experiment period was August 26-September 2, 1999. During this period the salinity field within the study area consisted of a gradient on the order of 2-3 psu in the vicinity of the shelf break and a warm core ring with a gradient of 1-2 psu. Detailed maps were made with the airborne sensors on August 28 and 29 and on September 2 flights were made over the surface drifters to look for effects due to a change in surface roughness resulting from the passage of Hurricane Dennis. Results show a good agreement between the microwave measurements and ship measurements of salinity. The features of the brightness temperature maps correspond well with the features of the salinity field measured by the ship and drifters and a preliminary retrieval of salinity compares well with the ship data.

Arctic Sea ice, 1973-1976: Satellite passive-microwave observations

NASA Technical Reports Server (NTRS)

Parkinson, Claire L.; Comiso, Josefino C.; Zwally, H. Jay; Cavalieri, Donald J.; Gloersen, Per; Campbell, William J.

1987-01-01

The Arctic region plays a key role in the climate of the earth. The sea ice cover affects the radiative balance of the earth and radically changes the fluxes of heat between the atmosphere and the ocean. The observations of the Arctic made by the Electrically Scanning Microwave Radiometer (ESMR) on board the Nimbus 5 research satellite are summarized for the period 1973 through 1976.

A Look at Seasonal Snow Cover and Snow Mass in the Southern Hemisphere from 1979-2006 Using SMMR and SSM/I Passive Microwave Data

NASA Technical Reports Server (NTRS)

Foster, James

2009-01-01

Seasonal snow cover in extra-tropical areas of South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite and from the Special Sensor Microwave Imagers (SSM/I) on board the Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2006, both snow cover extent and snow mass were estimated for the months of May-September. Most of the seasonal snow in South America occurs in the Patagonia region of Argentina. The average snow cover extent for July, the month with the greatest average extent during the 28-year period of record, is 321,674 sq km. The seasonal (May-September) 2 average snow cover extent was greatest in 1984 (464,250 sq km) and least in 1990 (69,875 sq km). In terms of snow mass, 1984 was also the biggest year (1.19 x 10(exp 13) kg) and 1990 was the smallest year (0.12 X 10(exp 13) kg). A strong relationship exists between the snow cover area and snow mass, correlated at 0.95, though no significant trend was found over the 28 year record for either snow cover extent or snow mass. For this long term climatology, snow mass and snow cover extent are shown to vary considerably from month to month and season to season. This analysis presents a consistent approach to mapping and measuring snow in South America utilizing an appropriate and readily available long term snow satellite dataset. This is the optimal dataset available, thus far, for deriving seasonal snow cover and snow mass in this region. Nonetheless, shallow snow, wet snow, snow beneath forests, as well as snow along coastal areas all may confound interpretation using passive microwave approaches. More work needs to be done to reduce the uncertainties in the data and hence, increase the confidence of the interpretation

A Prognostic Methodology for Precipitation Phase Detection using GPM Microwave Observations —With Focus on Snow Cover

NASA Astrophysics Data System (ADS)

Takbiri, Z.; Ebtehaj, A.; Foufoula-Georgiou, E.; Kirstetter, P.

2017-12-01

Improving satellite retrieval of precipitation requires increased understanding of its passive microwave signature over different land surfaces. Passive microwave signals over snow-covered surfaces are notoriously difficult to interpret because they record both emission from the land below and absorption/scattering from the liquid/ice crystals. Using data from the Global Precipitation Measurement (GPM) core satellite, we demonstrate that the microwave brightness temperatures of rain and snowfall shifts from a scattering to an emission regime from summer to winter, due to expansion of the less emissive snow cover underneath. We present evidence that the combination of low- (10-19 GHz) and high-frequency (89-166 GHz) channels provides the maximum amount of information for snowfall detection. The study also examines a prognostic nearest neighbor matching method for the detection of precipitation and its phase from passive microwave observations using GPM data. The nearest neighbor uses the weighted Euclidean distance metric to search through an a priori database that is populated with coincident GPM radiometer and radar data as well as ancillary snow cover fraction. The results demonstrate prognostic capabilities of the proposed method in detection of terrestrial snowfall. At the global scale, the average probability of hit and false alarm reaches to 0.80 and remains below 0.10, respectively. Surprisingly, the results show that the snow cover may help to better detect precipitation as the detection rate of terrestrial precipitation is increased from 0.75 (no snow cover) to 0.84 (snow-covered surfaces). For solid precipitation, this increased rate of detection is larger than its liquid counterpart by almost 8%. The main reasons are found to be related to the multi-frequency capabilities of the nearest neighbor matching that can properly isolate the atmospheric signal from the background emission and the fact that the precipitation can exhibit an emission-like (warmer than surface) signature over fresh snow cover.

Snowpack monitoring in North America and Eurasia using passive microwave satellite data

NASA Technical Reports Server (NTRS)

Foster, J. L.; Rango, A.; Hall, D. K.; Chang, A. T. C.; Allison, L. J.; Diesen, B. C., III

1980-01-01

Areas of the Canadian high plains, the Montana and North Dakota high plains, and the steppes of central Russia have been studied in an effort to determine the utility of spaceborne microwave radiometers for monitoring snow depths in different geographic areas. Significant regression relationships between snow depth and microwave brightness temperatures were developed for each of these homogeneous areas. In each of the study areas investigated in this paper, Nimbus-6 (0.81 cm) ESMR data produced higher correlations than Nimbus-5 (1.55 cm) ESMR data in relating microwave brightness temperature to snow depth. It is difficult to extrapolate relationships between microwave brightness temperature and snow depth from one area to another because different geographic areas are likely to have different snowpack conditions.

The effects of vegetation and soil hydraulic properties on passive microwave sensing of soil moisture: Data report for the 1982 fiels experiments

NASA Technical Reports Server (NTRS)

Oneill, P.; Jackson, T.; Blanchard, B. J.; Vandenhoek, R.; Gould, W.; Wang, J.; Glazar, W.; Mcmurtrey, J., III

1983-01-01

Field experiments to (1) study the biomass and geometrical structure properties of vegetation canopies to determine their impact on microwave emission data, and (2) to verify whether time series microwave data can be related to soil hydrologic properties for use in soil type classification. Truck mounted radiometers at 1.4 GHz and 5 GHz were used to obtain microwave brightness temperatures of bare vegetated test plots under different conditions of soil wetness, plant water content and canopy structure. Observations of soil moisture, soil temperature, vegetation biomass and other soil and canopy parameters were made concurrently with the microwave measurements. The experimental design and data collection procedures for both experiments are documented and the reduced data are presented in tabular form.

Interaction of the terrestrial and atmospheric hydrological cycles in the context of the North American southwest summer monsoon

NASA Technical Reports Server (NTRS)

Dickinson, Robert E.

1995-01-01

Work under this grant has used information on precipitation and water vapor fluxes in the area of the Mexican Monsoon to analyze the regional precipitation climatology, to understand the nature of water vapor transport during the monsoon using model and observational data, and to analyze the ability of the TRMM remote sensing algorithm to characterize precipitation. An algorithm for estimating daily surface rain volumes from hourly GOES infrared images was developed and compared to radar data. Estimates were usually within a factor of two, but different linear relations between satellite reflectances and rainfall rate were obtained for each day, storm type and storm development stage. This result suggests that using TRMM sensors to calibrate other satellite IR will need to be a complex process taking into account all three of the above factors. Another study, this one of the space-time variability of the Mexican Monsoon, indicate that TRMM will have a difficult time, over the course of its expected three year lifetime, identifying the diurnal cycle of precipitation over monsoon region. Even when considering monthly rainfalls, projected satellite estimates of August rainfall show a root mean square error of 38 percent. A related examination of spatial variability of mean monthly rainfall using a novel method for removing the effects of elevation from gridded gauge data, show wide variation from a satellite-based rainfall estimates for the same time and space resolution. One issue addressed by our research, relating to the basic character of the monsoon circulation, is the determination of the source region for moisture. The monthly maps produced from our study of monsoon variability show the presence of two rainfall maxima in the analysis normalized to sea level, one in south-central Arizona associated with the Mexican monsoon maximum and one in southeastern New Mexico associated with the Gulf of Mexico. From the point of view of vertically-integrated fluxes and flux divergence of water vapor from ECMWF data, most moisture at upper levels arrives from the Gulf of Mexico, while low level moisture comes from the northern Gulf of California. Composites of ECMWF analyses for wet and dry periods (classified by rain gauge data) show that both regimes show low level moisture arriving from northern and central Gulf of California. Above 700 MB, moisture comes from both source regions and the Sierra Madre Occidental. During wet periods a longer fetch through the moist air mass above western Mexico results in a greater moisture flux into the Sonoran Desert region, while there is less moisture from the Gulf of Mexico both above and below 700 mb. Work on the grant subcontract at the University of Colorado concentrated on the development of a technique useful to TRMM combining visible, infrared and passive microwave data for measuring precipitation. Two established techniques using either visible or infrared data applied over the US Southwest correlated with gauges at the 0.58 to 0.70 level. The application of some established passive microwave techniques were less successful for a variety of reason, including problems in both the gauge and satellite data quality, sampling problems and weaknesses inherent in the algorithms themselves. A more promising solution for accurate rainfall estimation was explored using visible and infrared data to perform a cloud classification, which when combined with information about the background (e.g. Iand/ocean), was used to select the most appropriate microwave algorithm from a suite of possibilities.

NASA Tech Briefs, November 2008

NASA Technical Reports Server (NTRS)

2008-01-01

Topics covered include: Digital Phase Meter for a Laser Heterodyne Interferometer; Vision System Measures Motions of Robot and External Objects; Advanced Precipitation Radar Antenna to Measure Rainfall From Space; Wide-Band Radar for Measuring Thickness of Sea Ice; Vertical Isolation for Photodiodes in CMOS Imagers; Wide-Band Microwave Receivers Using Photonic Processing; L-Band Transmit/Receive Module for Phase-Stable Array Antennas; Microwave Power Combiner/Switch Utilizing a Faraday Rotator; Compact Low-Loss Planar Magic-T; Using Pipelined XNOR Logic to Reduce SEU Risks in State Machines; Quasi-Optical Transmission Line for 94-GHz Radar; Next Generation Flight Controller Trainer System; Converting from DDOR SASF to APF; Converting from CVF to AAF; Documenting AUTOGEN and APGEN Model Files; Sequence History Update Tool; Extraction and Analysis of Display Data; MRO DKF Post-Processing Tool; Rig Diagnostic Tools; MRO Sequence Checking Tool; Science Activity Planner for the MER Mission; UAVSAR Flight-Planning System; Templates for Deposition of Microscopic Pointed Structures; Adjustable Membrane Mirrors Incorporating G-Elastomers; Hall-Effect Thruster Utilizing Bismuth as Propellant; High-Temperature Crystal-Growth Cartridge Tubes Made by VPS; Quench Crucibles Reinforced with Metal; Deep-Sea Hydrothermal-Vent Sampler; Mars Rocket Propulsion System; Two-Stage Passive Vibration Isolator; Improved Thermal Design of a Compression Mold; Enhanced Pseudo-Waypoint Guidance for Spacecraft Maneuvers; Altimetry Using GPS-Reflection/Occultation Interferometry; Thermally Driven Josephson Effect; Perturbation Effects on a Supercritical C7H16/N2 Mixing Layer; Gold Nanoparticle Labels Amplify Ellipsometric Signals; Phase Matching of Diverse Modes in a WGM Resonator; WGM Resonators for Terahertz-to-Optical Frequency Conversion; Determining Concentration of Nanoparticles from Ellipsometry; Microwave-to-Optical Conversion in WGM Resonators; Four-Pass Coupler for Laser-Diode-Pumped Solid-State Laser; Low-Resolution Raman-Spectroscopy Combustion Thermometry; Temperature Sensors Based on WGM Optical Resonators; Varying the Divergence of Multiple Parallel Laser Beams; Efficient Algorithm for Rectangular Spiral Search; Algorithm-Based Fault Tolerance Integrated with Replication; Targeting and Localization for Mars Rover Operations; Terrain-Adaptive Navigation Architecture; Self-Adjusting Hash Tables for Embedded Flight Applications; Schema for Spacecraft-Command Dictionary; Combined GMSK Communications and PN Ranging; System-Level Integration of Mass Memory; Network-Attached Solid-State Recorder Architecture; Method of Cross-Linking Aerogels Using a One-Pot Reaction Scheme; An Efficient Reachability Analysis Algorithm.

Development and evaluation of ice phenology algorithm from space-borne active and passive microwave measurements

NASA Astrophysics Data System (ADS)

Kang, K.; Duguay, C. R.

2013-12-01

The presence (or absence) of ice cover plays an important role in lake-atmosphere interactions at high latitudes during the winter months. Knowledge of ice phenology (i.e. freeze-onset/melt-onset, ice-on/ice-off dates, and ice cover duration) is crucial for understanding both the role of lake ice cover in and its response to regional weather and climate. Shortening of the ice cover season in many regions of the Northern Hemisphere over recent decades has been shown to significantly influence the thermal regime as well as the water quality and quantity of lakes. In this respect, satellite remote sensing instruments are providing invaluable measurements for monitoring changes in timing of ice phenological events and the length of the ice cover (or open water) season on large northern lakes, and also for providing more spatially representative limnological information than available from in situ measurements. In this study, we present a new ice phenology retrieval algorithm developed from the synergistic use of Quick Scatterometer (QuikSCAT), Oceansat-2 Scatterometer (OSCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E). Retrieved ice dates are then evaluated against those derived from the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) 4 km resolution product (2004-2011) during the freeze-up and break-up periods (2002-2012) for 11 lakes (Amadjuak, Nettilling, Great Bear, Great Slave, Manitoba, and Winnipeg in North America as well as Inarijrvi, Ladoga, Onega, Qinghai (Koko Nor), and Baikal in Eurasia). In addition, daily wind speed derived from QuikSCAT/OSCAT is analyzed along with WindSAT surface wind vector products (2002-2012) during the open water season for the large lakes. A detailed evaluation of the new algorithm conducted over Great Slave Lake (GSL) and Great Bear Lake (GBL) reveals that estimated ice-on/ice-off dates are within 4-7 days of those derived from the IMS product. Preliminary analysis of ice dates show that ice-on occurs three to five months earlier and ice-off two months later on GSL and GBL (Canada) compared to Lake Ladoga and Lake Onega (Russia) mostly due to regional climate differences. Overall, the synergistic use of microwave satellite data from various sensors provides an invaluable opportunity for operational monitoring of ice cover on large northern lakes.

Microwave hydrology: A trilogy

NASA Technical Reports Server (NTRS)

Stacey, J. M.; Johnston, E. J.; Girard, M. A.; Regusters, H. A.

1985-01-01

Microwave hydrology, as the term in construed in this trilogy, deals with the investigation of important hydrological features on the Earth's surface as they are remotely, and passively, sensed by orbiting microwave receivers. Microwave wavelengths penetrate clouds, foliage, ground cover, and soil, in varying degrees, and reveal the occurrence of standing liquid water on and beneath the surface. The manifestation of liquid water appearing on or near the surface is reported by a microwave receiver as a signal with a low flux level, or, equivalently, a cold temperature. Actually, the surface of the liquid water reflects the low flux level from the cosmic background into the input terminals of the receiver. This trilogy describes and shows by microwave flux images: the hydrological features that sustain Lake Baykal as an extraordinary freshwater resource; manifestations of subsurface water in Iran; and the major water features of the Congo Basin, a rain forest.

Objective Characterization of Snow Microstructure for Microwave Emission Modeling

NASA Technical Reports Server (NTRS)

Durand, Michael; Kim, Edward J.; Molotch, Noah P.; Margulis, Steven A.; Courville, Zoe; Malzler, Christian

2012-01-01

Passive microwave (PM) measurements are sensitive to the presence and quantity of snow, a fact that has long been used to monitor snowcover from space. In order to estimate total snow water equivalent (SWE) within PM footprints (on the order of approx 100 sq km), it is prerequisite to understand snow microwave emission at the point scale and how microwave radiation integrates spatially; the former is the topic of this paper. Snow microstructure is one of the fundamental controls on the propagation of microwave radiation through snow. Our goal in this study is to evaluate the prospects for driving the Microwave Emission Model of Layered Snowpacks with objective measurements of snow specific surface area to reproduce measured brightness temperatures when forced with objective measurements of snow specific surface area (S). This eliminates the need to treat the grain size as a free-fit parameter.

NORSEX 1979 microwave remote sensing data report

NASA Technical Reports Server (NTRS)

Hennigar, H. F.; Schaffner, S. K.

1982-01-01

Airborne microwave remote sensing measurements obtained by NASA Langley Research Center in support of the 1979 Norwegian Remote Sensing Experiment (NORSEX) are summarized. The objectives of NORSEX were to investigate the capabilities of an active/passive microwave system to measure ice concentration and type in the vicinity of the marginal ice zone near Svalbard, Norway and to apply microwave techniques to the investigation of a thermal oceanic front near Bear Island, Norway. The instruments used during NORSEX include the stepped frequency microwave radiometer, airborne microwave scatterometer, precision radiation thermometer and metric aerial photography. The data are inventoried, summarized, and presented in a user-friendly format. Data summaries are presented as time-history plots which indicate when and where data were obtained as well as the sensor configuration. All data are available on nine-track computer tapes in card-image format upon request to the NASA Langley Technical Library.

Validation of the TOPEX rain algorithm: Comparison with ground-based radar

NASA Astrophysics Data System (ADS)

McMillan, A. C.; Quartly, G. D.; Srokosz, M. A.; Tournadre, J.

2002-02-01

Recently developed algorithms have shown the potential recovery of rainfall information from spaceborne dual-frequency altimeters. Given the long mission achieved with TOPEX and the prospect of several other dual-frequency altimeters, we need to validate the altimetrically derived values so as to foster their integration with rain information from different sensors. Comparison with some alternative climatologies shows the bimonthly means for TOPEX to be low. Rather than apply a bulk correction we investigate individual rain events to understand the cause of TOPEX's underestimation. In this paper we compare TOPEX with near-simultaneous ground-based rain radars based at a number of locations, examining both the detection of rain and the quantitative values inferred. The altimeter-only algorithm is found to flag false rain events in very low wind states (<3.8 m s-1) the application of an extra test, involving the liquid water path as sensed by the microwave radiometer, removes the spurious detections. Some false detections of rain also occur at high wind speeds (>20 m s-1), where the empirical dual-frequency relationship is less well defined. In the intermediate range of wind speeds, the TOPEX detections are usually good, with the instrument picking up small-scale variations that cannot be recovered from infrared or passive microwave techniques. The magnitude of TOPEX's rain retrievals can differ by a factor of 2 from the ground-based radar, but this may reflect the uncertainties in the validation data. In general, over these individual point comparisons TOPEX values appear to exceed the ``ground truth.'' Taking account of all the factors affecting the comparisons, we conclude that the TOPEX climatology could be improved by, in the first instance, incorporating the radiometric test and employing a better estimate of the melting layer height. Appropriate corrections for nonuniform beam filling and drizzle fraction are harder to define globally.

Development of microwave rainfall retrieval algorithm for climate applications

NASA Astrophysics Data System (ADS)

KIM, J. H.; Shin, D. B.

2014-12-01

With the accumulated satellite datasets for decades, it is possible that satellite-based data could contribute to sustained climate applications. Level-3 products from microwave sensors for climate applications can be obtained from several algorithms. For examples, the Microwave Emission brightness Temperature Histogram (METH) algorithm produces level-3 rainfalls directly, whereas the Goddard profiling (GPROF) algorithm first generates instantaneous rainfalls and then temporal and spatial averaging process leads to level-3 products. The rainfall algorithm developed in this study follows a similar approach to averaging instantaneous rainfalls. However, the algorithm is designed to produce instantaneous rainfalls at an optimal resolution showing reduced non-linearity in brightness temperature (TB)-rain rate(R) relations. It is found that the resolution tends to effectively utilize emission channels whose footprints are relatively larger than those of scattering channels. This algorithm is mainly composed of a-priori databases (DBs) and a Bayesian inversion module. The DB contains massive pairs of simulated microwave TBs and rain rates, obtained by WRF (version 3.4) and RTTOV (version 11.1) simulations. To improve the accuracy and efficiency of retrieval process, data mining technique is additionally considered. The entire DB is classified into eight types based on Köppen climate classification criteria using reanalysis data. Among these sub-DBs, only one sub-DB which presents the most similar physical characteristics is selected by considering the thermodynamics of input data. When the Bayesian inversion is applied to the selected DB, instantaneous rain rate with 6 hours interval is retrieved. The retrieved monthly mean rainfalls are statistically compared with CMAP and GPCP, respectively.

Observing the Global Water Cycle from Space

NASA Technical Reports Server (NTRS)

Hildebrand, Peter H.; Houser, Paul; Schlosser, C. Adam

2003-01-01

This paper presents an approach to measuring all major components of the water cycle from space. The goal of the paper is to explore the concept of using a sensor-web of satellites to observe the global water cycle. The details of the required measurements and observation systems are therefore only an initial approach and will undergo future refinement, as their details will be highly important. Key elements include observation and evaluation of all components of the water cycle in terms of the storage of water-in the ocean, air, cloud and precipitation, in soil, ground water, snow and ice, and in lakes and rivers-and in terms of the global fluxes of water between these reservoirs. For each component of the water cycle that must be observed, the appropriate temporal and spatial scales of measurement are estimated, along with the some of the frequencies that have been used for active and passive microwave observations of the quantities. The suggested types of microwave observations are based on the heritage for such measurements, and some aspects of the recent heritage of these measurement algorithms are listed. The observational requirements are based on present observational systems, as modified by expectations for future needs. Approaches to the development of space systems for measuring the global water cycle can be based on these observational requirements.

Cloud Particle Size and Water/Ice Ratio Estimation using the DMSP SSMIS Sounder

NASA Astrophysics Data System (ADS)

Peng, G. S.; Fote, A. A.; Wu, D. L.; Boucher, D. J.; Thomas, B. H.; Kishi, A. M.

2008-12-01

The Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager/Sounder (SSMIS) is a next-generation passive conically scanning microwave radiometer. It combines both imaging and sounding capabilities of current operational instruments, SSM/I, SSM/T-1 and SSM/T-2. It also improves the capability of temperature sounding by providing profiles from the surface up to 70 km altitude with higher spatial resolutions (~37.5 for lower air and ~75 km for upper air). DMSP Flight 17 launched on 4 November 2006 from Vandenberg Air Force Base carrying the second SSMIS sounder. During the SSMIS Cal/Val period, cold patches were observed in the 50-55 GHz temperature sounding channels at low latitudes. Cold patches were also more apparent in the horizontal polarization (H- pol) than the Vertical polarization (V-pol) channels. A difference in sensitivity of the H-pol and V-pol channels gives the ratio of water to ice in the clouds. Subsequent investigation showed that these patches appeared in the 91.6 GHz channels but not the 37 GHz channels. This information, together with the theoretical scattering efficiency for spherical particles of various sizes, gives an upper bound of < 2 mm diameter for water and ice particles that may not be detected by SSMIS operational 'cloud clearing' algorithms.

Estimation of wind speeds inside Super Typhoon Nepartak from AMSR2 low-frequency brightness temperatures

NASA Astrophysics Data System (ADS)

Zhang, Lei; Yin, Xiaobin; Shi, Hanqing; Wang, Zhenzhan; Xu, Qing

2018-04-01

Accurate estimations of typhoon-level winds are highly desired over the western Pacific Ocean. A wind speed retrieval algorithm is used to retrieve the wind speeds within Super Typhoon Nepartak (2016) using 6.9- and 10.7-GHz brightness temperatures from the Japanese Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor on board the Global Change Observation Mission-Water 1 (GCOM-W1) satellite. The results show that the retrieved wind speeds clearly represent the intensification process of Super Typhoon Nepartak. A good agreement is found between the retrieved wind speeds and the Soil Moisture Active Passive wind speed product. The mean bias is 0.51 m/s, and the root-mean-square difference is 1.93 m/s between them. The retrieved maximum wind speeds are 59.6 m/s at 04:45 UTC on July 6 and 71.3 m/s at 16:58 UTC on July 6. The two results demonstrate good agreement with the results reported by the China Meteorological Administration and the Joint Typhoon Warning Center. In addition, Feng-Yun 2G (FY-2G) satellite infrared images, Feng-Yun 3C (FY-3C) microwave atmospheric sounder data, and AMSR2 brightness temperature images are also used to describe the development and structure of Super Typhoon Nepartak.

An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path

NASA Astrophysics Data System (ADS)

Greenwald, Thomas J.; Bennartz, Ralf; Lebsock, Matthew; Teixeira, João.

2018-04-01

The first extended comprehensive data set of the retrieval uncertainties in passive microwave observations of cloud liquid water path (CLWP) for warm oceanic clouds has been created for practical use in climate applications. Four major sources of systematic errors were considered over the 9-year record of the Advanced Microwave Scanning Radiometer-EOS (AMSR-E): clear-sky bias, cloud-rain partition (CRP) bias, cloud-fraction-dependent bias, and cloud temperature bias. Errors were estimated using a unique merged AMSR-E/Moderate resolution Imaging Spectroradiometer Level 2 data set as well as observations from the Cloud-Aerosol Lidar with Orthogonal Polarization and the CloudSat Cloud Profiling Radar. To quantify the CRP bias more accurately, a new parameterization was developed to improve the inference of CLWP in warm rain. The cloud-fraction-dependent bias was found to be a combination of the CRP bias, an in-cloud bias, and an adjacent precipitation bias. Globally, the mean net bias was 0.012 kg/m2, dominated by the CRP and in-cloud biases, but with considerable regional and seasonal variation. Good qualitative agreement between a bias-corrected AMSR-E CLWP climatology and ship observations in the Northeast Pacific suggests that the bias estimates are reasonable. However, a possible underestimation of the net bias in certain conditions may be due in part to the crude method used in classifying precipitation, underscoring the need for an independent method of detecting rain in warm clouds. This study demonstrates the importance of combining visible-infrared imager data and passive microwave CLWP observations for estimating uncertainties and improving the accuracy of these observations.

An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path

PubMed Central

Bennartz, Ralf; Lebsock, Matthew; Teixeira, João

2018-01-01

Abstract The first extended comprehensive data set of the retrieval uncertainties in passive microwave observations of cloud liquid water path (CLWP) for warm oceanic clouds has been created for practical use in climate applications. Four major sources of systematic errors were considered over the 9‐year record of the Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E): clear‐sky bias, cloud‐rain partition (CRP) bias, cloud‐fraction‐dependent bias, and cloud temperature bias. Errors were estimated using a unique merged AMSR‐E/Moderate resolution Imaging Spectroradiometer Level 2 data set as well as observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization and the CloudSat Cloud Profiling Radar. To quantify the CRP bias more accurately, a new parameterization was developed to improve the inference of CLWP in warm rain. The cloud‐fraction‐dependent bias was found to be a combination of the CRP bias, an in‐cloud bias, and an adjacent precipitation bias. Globally, the mean net bias was 0.012 kg/m2, dominated by the CRP and in‐cloud biases, but with considerable regional and seasonal variation. Good qualitative agreement between a bias‐corrected AMSR‐E CLWP climatology and ship observations in the Northeast Pacific suggests that the bias estimates are reasonable. However, a possible underestimation of the net bias in certain conditions may be due in part to the crude method used in classifying precipitation, underscoring the need for an independent method of detecting rain in warm clouds. This study demonstrates the importance of combining visible‐infrared imager data and passive microwave CLWP observations for estimating uncertainties and improving the accuracy of these observations. PMID:29938146

Use of Collocated KWAJEX Satellite, Aircraft, and Ground Measurements for Understanding Ambiguities in TRMM Radiometer Rain Profile Algorithm

NASA Technical Reports Server (NTRS)

Smith, Eric A.; Fiorino, Steven

2002-01-01

Coordinated ground, aircraft, and satellite observations are analyzed from the 1999 TRMM Kwajalein Atoll field experiment (KWAJEX) to better understand the relationships between cloud microphysical processes and microwave radiation intensities in the context of physical evaluation of the Level 2 TRMM radiometer rain profile algorithm and uncertainties with its assumed microphysics-radiation relationships. This talk focuses on the results of a multi-dataset analysis based on measurements from KWAJEX surface, air, and satellite platforms to test the hypothesis that uncertainties in the passive microwave radiometer algorithm (TMI 2a12 in the nomenclature of TRMM) are systematically coupled and correlated with the magnitudes of deviation of the assumed 3-dimensional microphysical properties from observed microphysical properties. Re-stated, this study focuses on identifying the weaknesses in the operational TRMM 2a12 radiometer algorithm based on observed microphysics and radiation data in terms of over-simplifications used in its theoretical microphysical underpinnings. The analysis makes use of a common transform coordinate system derived from the measuring capabilities of the aircraft radiometer used to survey the experimental study area, i.e., the 4-channel AMPR radiometer flown on the NASA DC-8 aircraft. Normalized emission and scattering indices derived from radiometer brightness temperatures at the four measuring frequencies enable a 2-dimensional coordinate system that facilities compositing of Kwajalein S-band ground radar reflectivities, ARMAR Ku-band aircraft radar reflectivities, TMI spacecraft radiometer brightness temperatures, PR Ku-band spacecraft radar reflectivities, bulk microphysical parameters derived from the aircraft-mounted cloud microphysics laser probes (including liquid/ice water contents, effective liquid/ice hydrometeor radii, and effective liquid/ice hydrometeor variances), and rainrates derived from any of the individual ground, aircraft, or satellite algorithms applied to the radar or radiometer measurements, or their combination. The results support the study's underlying hypothesis, particularly in context of ice phase processes, in that the cloud regions where the 2a12 algorithm's microphysical database most misrepresents the microphysical conditions as determined by the laser probes, are where retrieved surface rainrates are most erroneous relative to other reference rainrates as determined by ground and aircraft radar. In reaching these conclusions, TMI and PR brightness temperatures and reflectivities have been synthesized from the aircraft AMPR and ARMAR measurements with the analysis conducted in a composite framework to eliminate measurement noise associated with the case study approach and single element volumes obfuscated by heterogeneous beam filling effects. In diagnosing the performance of the 2a12 algorithm, weaknesses have been found in the cloud-radiation database used to provide microphysical guidance to the algorithm for upper cloud ice microphysics. It is also necessary to adjust a fractional convective rainfall factor within the algorithm somewhat arbitrarily to achieve satisfactory algorithm accuracy.

ENSO Precipitation Variations as Seen by GPM and TRMM Radar and Passive Microwave Observations

NASA Astrophysics Data System (ADS)

Adler, R. F.; Wang, J. J.

2017-12-01

Tropical precipitation variations related to ENSO are the largest-scale such variations both spatially and in magnitude and are also the main driver of surface temperature-surface rainfall relationships on the inter-annual scale. GPM (and TRMM before it) provide a unique capability to examine these relations with both the passive and active microwave approaches. Documenting the phase and magnitudes of these relationships are important to understand these large-scale processes and to validate climate models. However, as past research by the authors have shown, the results of these relations have been different for passive vs. radar retrievals. In this study we re-examine these relations with the new GPM Version 5 products, focusing on the 2015-2016 El Nino event. The recent El Nino peaked in Dec. 2015 through Feb. 2016 with the usual patterns of precipitation anomalies across the Tropics as evident in both the GPM GMI and the Near Surface (NS) DPR (single frequency) retrievals. Integrating both the rainfall anomalies and the SST anomalies over the entire tropical ocean area (25N-25S) and comparing how they vary as a function of time on a monthly scale during the GPM era (2014-2017), the radar-based results show contrasting results to those from the GMI-based (and GPCP) results. The passive microwave data (GMI and GPCP) indicates a slope of 17%/C for the precipitation variations, while the radar NS indicates about half that ( 8%/C). This NS slope is somewhat less than calculated before with GPM's V4 data, but is larger than obtained with TRMM PR data ( 0%/C) for an earlier period during the TRMM era. Very similar results as to the DPR NS calculations are also obtained for rainfall at 2km and 4km altitude and for the Combined (DPR + GMI) product. However, at 6km altitude, although the reflectivity and rainfall magnitudes are much less than at lower altitudes, the slope of the rainfall/SST relation is 17%/C, the same as calculated with the passive microwave data. The reasons for these differences are explored and lead to conclusions that the radar-based estimates of surface rainfall with GPM have limitations (and are negatively biased) in relatively intense rainfall and this leads to an underestimation of large-scale rainfall under El Nino conditions, where more oceanic rainfall, and more intense rainfall are prevalent.

Microwave Remote Sensing of Falling Snow

NASA Technical Reports Server (NTRS)

Kim, Min-Jeong; Wang, J. R.; Meneghini, R.; Johnson, B.; Tanelli, S.; Roman-Nieves, J. I.; Sekelsky, S. M.; Skofronick-Jackson, G.

2005-01-01

This study analyzes passive and active microwave measurements during the 2003 Wakasa Bay field experiment for understanding of the electromagnetic characteristics of frozen hydrometeors at millimeter-wave frequencies. Based on these understandings, parameterizations of the electromagnetic scattering properties of snow at millimeter-wave frequencies are developed and applied to the hydrometeor profiles obtained by airborne radar measurements. Calculated brightness temperatures and radar reflectivity are compared with the millimeter-wave measurements.

Radiation measurements from polar and geosynchronous satellites

NASA Technical Reports Server (NTRS)

Vonderhaar, T. H.; Kidder, S. Q.; Hillger, D. W.; Ellis, J. S.

1978-01-01

The following topics are discussed: (1) cloud effects in climate determination; (2) annual variation in the global heat balance of the earth; (3) the accuracy of precipitation estimates made from passive microwave measurements from satellites; (4) seasonal oceanic precipitation frequencies; (5) determination of mesoscale temperature and moisture fields over land from satellite radiance measurements; and (6) Nimbus 6 scanning microwave spectrometer data evaluation for surface wind and pressure components in tropical storms.

Abnormal Winter Melting of the Arctic Sea Ice Cap Observed by the Spaceborne Passive Microwave Sensors

NASA Astrophysics Data System (ADS)

Lee, Seongsuk; Yi, Yu

2016-12-01

The spatial size and variation of Arctic sea ice play an important role in Earth’s climate system. These are affected by conditions in the polar atmosphere and Arctic sea temperatures. The Arctic sea ice concentration is calculated from brightness temperature data derived from the Defense Meteorological Satellite program (DMSP) F13 Special Sensor Microwave/Imagers (SSMI) and the DMSP F17 Special Sensor Microwave Imager/Sounder (SSMIS) sensors. Many previous studies point to significant reductions in sea ice and their causes. We investigated the variability of Arctic sea ice using the daily sea ice concentration data from passive microwave observations to identify the sea ice melting regions near the Arctic polar ice cap. We discovered the abnormal melting of the Arctic sea ice near the North Pole during the summer and the winter. This phenomenon is hard to explain only surface air temperature or solar heating as suggested by recent studies. We propose a hypothesis explaining this phenomenon. The heat from the deep sea in Arctic Ocean ridges and/ or the hydrothermal vents might be contributing to the melting of Arctic sea ice. This hypothesis could be verified by the observation of warm water column structure below the melting or thinning arctic sea ice through the project such as Coriolis dataset for reanalysis (CORA).

Time-of-Flight Microwave Camera

PubMed Central

Charvat, Gregory; Temme, Andrew; Feigin, Micha; Raskar, Ramesh

2015-01-01

Microwaves can penetrate many obstructions that are opaque at visible wavelengths, however microwave imaging is challenging due to resolution limits associated with relatively small apertures and unrecoverable “stealth” regions due to the specularity of most objects at microwave frequencies. We demonstrate a multispectral time-of-flight microwave imaging system which overcomes these challenges with a large passive aperture to improve lateral resolution, multiple illumination points with a data fusion method to reduce stealth regions, and a frequency modulated continuous wave (FMCW) receiver to achieve depth resolution. The camera captures images with a resolution of 1.5 degrees, multispectral images across the X frequency band (8 GHz–12 GHz), and a time resolution of 200 ps (6 cm optical path in free space). Images are taken of objects in free space as well as behind drywall and plywood. This architecture allows “camera-like” behavior from a microwave imaging system and is practical for imaging everyday objects in the microwave spectrum. PMID:26434598

Time-of-Flight Microwave Camera

NASA Astrophysics Data System (ADS)

Charvat, Gregory; Temme, Andrew; Feigin, Micha; Raskar, Ramesh

2015-10-01

Microwaves can penetrate many obstructions that are opaque at visible wavelengths, however microwave imaging is challenging due to resolution limits associated with relatively small apertures and unrecoverable “stealth” regions due to the specularity of most objects at microwave frequencies. We demonstrate a multispectral time-of-flight microwave imaging system which overcomes these challenges with a large passive aperture to improve lateral resolution, multiple illumination points with a data fusion method to reduce stealth regions, and a frequency modulated continuous wave (FMCW) receiver to achieve depth resolution. The camera captures images with a resolution of 1.5 degrees, multispectral images across the X frequency band (8 GHz-12 GHz), and a time resolution of 200 ps (6 cm optical path in free space). Images are taken of objects in free space as well as behind drywall and plywood. This architecture allows “camera-like” behavior from a microwave imaging system and is practical for imaging everyday objects in the microwave spectrum.

Development Of VHF (240-270 MHz) Antennas For SoOp (Signal Of Opportunity) Receiver For 6u Cubesat Platforms

NASA Technical Reports Server (NTRS)

Joseph, A. T.; Deshpande, M.; O'Neill, P. E.; Miles, L.

2016-01-01

The main goal of this research is to design, fabricate, and test deployable VHF antennas for 6U Cubesat platforms to enable validation of root zone soil moisture (RZSM) estimation algorithms for signal of opportunity (SoOp) remote sensing over the 240-270 MHz frequency band. The proposed work provides a strong foundation for establishing a technology development path for maturing a truly global direct surface soil moisture (SM) and RZSM measurement system (Figure 1) over a variety of land covers with limited density restrictions. In SoOp methodology, signals transmitted by already existing transmitters (known as transmitters of opportunity, in this case the Military Satellite Communication (MilSatCom) System's UHF Follow-On program) are utilized to measure properties of reflecting targets by recording reflected signals using a simple passive microwave receiver.

Improved Calibration through SMAP RFI Change Detection

NASA Technical Reports Server (NTRS)

Piepmeier, Jeffrey; De Amici, Giovanni; Mohammed, Priscilla; Peng, Jinzheng

2017-01-01

Anthropogenic Radio-Frequency Interference (RFI) drove both the SMAP (Soil Moisture Active Passive) microwave radiometer hardware and Level 1 science algorithm designs to use new technology and techniques for the first time on a spaceflight project. Care was taken to provide special features allowing the detection and removal of harmful interference in order to meet the error budget. Nonetheless, the project accepted a risk that RFI and its mitigation would exceed the 1.3-K error budget. Thus, RFI will likely remain a challenge afterwards due to its changing and uncertain nature. To address the challenge, we seek to answer the following questions: How does RFI evolve over the SMAP lifetime? What calibration error does the changing RFI environment cause? Can time series information be exploited to reduce these errors and improve calibration for all science products reliant upon SMAP radiometer data? In this talk, we address the first question.

Passive microwave remote sensing of soil moisture - Results from HAPEX, FIFE and MONSOON 90

NASA Technical Reports Server (NTRS)

Schmugge, T.; Jackson, T. J.; Kustas, W. P.; Wang, J. R.

1992-01-01

HAPEX (Hydrologic Atmospheric Pilot Experiment), FIFE (First ISLSCP Field Experiment) and MONSOON 90 which used an imaging microwave radiometer operating at a frequency of 1.42 GHz are reported. For FIFE and MONSOON 90, a wide range of moisture conditions were present and it was possible to observe the drydown of the soil following heavy rain and to map its spatial variation. The quantitative agreement of microwave observations and ground measurements was very good. In HAPEX there were no significant rains and conditions were generally rather dry, however, moisture variations due to irrigation were observed.

Passive microwave remote sensing of soil moisture: Results from HAPEX, FIFE, and MONSOON 90

NASA Technical Reports Server (NTRS)

Schmugge, Thomas; Jackson, T. J.; Wang, J. R.

1991-01-01

HAPEX (Hydrologic Atmospheric Pilot Experiment), FIFE (First ISLSCP Field Experiment) and MONSOON 90 which used an imaging microwave radiometer operating at a frequency of 1.42 GHz are reported. For FIFE and MONSOON 90, a wide range of moisture conditions were present and it was possible to observe the drydown of the soil following heavy rain and to map its spatial variation. The quantitive agreement of microwave observations and ground measurements was very good. In HAPEX there were no significant rains and conditions were generally rather dry, however, moisture variations due to irrigation were observed.

A selective review of ground based passive microwave radiometric probing of the atmosphere

NASA Technical Reports Server (NTRS)

Welch, W. J.

1969-01-01

The absorption of the various atmospheric constituents with significant microwave spectra is reviewed. Based on the available data, an estimate is made of the uncertainty in the microwave absorption coefficients of the major constituents, water vapor and oxygen. Then there is an examination of the integral equations which describe the three basic types of observations: measurement of the spectrum of absorption of the sun's radiation by an atmospheric constituent, measurement of the emission spectrum of a constituent, and measurement at one frequency of the zenith angle dependence of the absorption or emission of the atmosphere.

Application of the Markov Chain Monte Carlo method for snow water equivalent retrieval based on passive microwave measurements

NASA Astrophysics Data System (ADS)

Pan, J.; Durand, M. T.; Vanderjagt, B. J.

2015-12-01

Markov Chain Monte Carlo (MCMC) method is a retrieval algorithm based on Bayes' rule, which starts from an initial state of snow/soil parameters, and updates it to a series of new states by comparing the posterior probability of simulated snow microwave signals before and after each time of random walk. It is a realization of the Bayes' rule, which gives an approximation to the probability of the snow/soil parameters in condition of the measured microwave TB signals at different bands. Although this method could solve all snow parameters including depth, density, snow grain size and temperature at the same time, it still needs prior information of these parameters for posterior probability calculation. How the priors will influence the SWE retrieval is a big concern. Therefore, in this paper at first, a sensitivity test will be carried out to study how accurate the snow emission models and how explicit the snow priors need to be to maintain the SWE error within certain amount. The synthetic TB simulated from the measured snow properties plus a 2-K observation error will be used for this purpose. It aims to provide a guidance on the MCMC application under different circumstances. Later, the method will be used for the snowpits at different sites, including Sodankyla, Finland, Churchill, Canada and Colorado, USA, using the measured TB from ground-based radiometers at different bands. Based on the previous work, the error in these practical cases will be studied, and the error sources will be separated and quantified.

Differences Between the HUT Snow Emission Model and MEMLS and Their Effects on Brightness Temperature Simulation

NASA Technical Reports Server (NTRS)

Pan, Jinmei; Durand, Michael; Sandells, Melody; Lemmetyinen, Juha; Kim, Edward J.; Pulliainen, Jouni; Kontu, Anna; Derksen, Chris

2015-01-01

Microwave emission models are a critical component of snow water equivalent retrieval algorithms applied to passive microwave measurements. Several such emission models exist, but their differences need to be systematically compared. This paper compares the basic theories of two models: the multiple-layer HUT (Helsinki University of Technology) model and MEMLS (Microwave Emission Model of Layered Snowpacks). By comparing the mathematical formulation side-by-side, three major differences were identified: (1) by assuming the scattered intensity is mostly (96) in the forward direction, the HUT model simplifies the radiative transfer (RT) equation into 1-flux; whereas MEMLS uses a 2-flux theory; (2) the HUT scattering coefficient is much larger than MEMLS; (3 ) MEMLS considers the trapped radiation inside snow due to internal reflection by a 6-flux model, which is not included in HUT. Simulation experiments indicate that, the large scattering coefficient of the HUT model compensates for its large forward scattering ratio to some extent, but the effects of 1-flux simplification and the trapped radiation still result in different T(sub B) simulations between the HUT model and MEMLS. The models were compared with observations of natural snow cover at Sodankyl, Finland; Churchill, Canada; and Colorado, USA. No optimization of the snow grain size was performed. It shows that HUT model tends to under estimate T(sub B) for deep snow. MEMLS with the physically-based improved Born approximation performed best among the models, with a bias of -1.4 K, and an RMSE of 11.0 K.

Microwave Brightness Temperatures of Tilted Convective Systems

NASA Technical Reports Server (NTRS)

Hong, Ye; Haferman, Jeffrey L.; Olson, William S.; Kummerow, Christian D.

1998-01-01

Aircraft and ground-based radar data from the Tropical Ocean and Global Atmosphere Coupled-Ocean Atmosphere Response Experiment (TOGA COARE) show that convective systems are not always vertical. Instead, many are tilted from vertical. Satellite passive microwave radiometers observe the atmosphere at a viewing angle. For example, the Special Sensor Microwave/Imager (SSM/I) on Defense Meteorological Satellite Program (DMSP) satellites and the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) on the TRMM satellite have an incident angle of about 50deg. Thus, the brightness temperature measured from one direction of tilt may be different than that viewed from the opposite direction due to the different optical depth. This paper presents the investigation of passive microwave brightness temperatures of tilted convective systems. To account for the effect of tilt, a 3-D backward Monte Carlo radiative transfer model has been applied to a simple tilted cloud model and a dynamically evolving cloud model to derive the brightness temperature. The radiative transfer results indicate that brightness temperature varies when the viewing angle changes because of the different optical depth. The tilt increases the displacements between high 19 GHz brightness temperature (Tb(sub 19)) due to liquid emission from lower level of cloud and the low 85 GHz brightness temperature (Tb(sub 85)) due to ice scattering from upper level of cloud. As the resolution degrades, the difference of brightness temperature due to the change of viewing angle decreases dramatically. The dislocation between Tb(sub 19) and Tb(sub 85), however, remains prominent.

Understanding Computation of Impulse Response in Microwave Software Tools

ERIC Educational Resources Information Center

Potrebic, Milka M.; Tosic, Dejan V.; Pejovic, Predrag V.

2010-01-01

In modern microwave engineering curricula, the introduction of the many new topics in microwave industrial development, or of software tools for design and simulation, sometimes results in students having an inadequate understanding of the fundamental theory. The terminology for and the explanation of algorithms for calculating impulse response in…

Summer Arctic ice concentrations and characteristics from SAR and SSM/I data

NASA Technical Reports Server (NTRS)

Comiso, Joey C.; Kwok, Ron

1993-01-01

The extent and concentration of the Summer minima provide indirect information about the long term ability of the perennial portion of the ice pack to survive the Arctic atmosphere and ocean system. Both active and passive microwave data were used with some success for monitoring the ice cover during the Summer, but they both suffer from similar problems caused by the presence of meltponding, surface wetness, flooding, and freeze/thaw cycles associated with periodic changes in surface air temperatures. A comparative analysis of ice conditions in the Arctic region using coregistered ERS-1 SAR (Synthetic Aperture Radar) and SSM/I (Special Sensor Microwave/Imager) data was made. The analysis benefits from complementary information from the two systems, the good spatial resolution of SAR data, and the good time resolution of and global coverage by SSM/I data. The results show that in many areas ice concentrations derived from SAR data are significantly different (usually higher) than those derived from passive microwave data. Additional insights about surface conditions can be inferred depending on the nature of the discrepancies.

Calcific tendinopathy of the rotator cuff. Conservative management with 434 Mhz local microwave diathermy (hyperthermia): a case study.

PubMed

Di Cesare, Annalisa; Giombini, Arrigo; Dragoni, Stefano; Agnello, Luciano; Ripani, Maurizio; Saraceni, Vincenzo Maria; Maffulli, Nicola

2008-01-01

To report the effects of local microwave diathermy (hyperthermia) at 434 Mhz on calcific tendinopathy of the shoulder in two middle aged patients. Two middle-aged women with calcific tendinopathy of the shoulder were treated with local microwave diathermy (hyperthermia) at 434 Mhz three times a week for four weeks. Plain radiographs and ultrasonography demonstrated calcific deposits in the area of infraspinatus or supraspinatus. Shoulder Pain and Disability Index (SPADI) and passive Range of Motion (ROM) were used to assess the response to treatment. At the end of the treatment period, the improvement as measured by the SPADI score was respectively 30% for the first patient and 40% for the second patient with an improvement of the shoulder passive ROM for both patients. The calcific deposits seen on the initial radiographs and ultrasonography were no longer visible. At 1 year follow-up, both patients continued to be symptom free. Hyperthermia is a safe option in the management of calcific tendinopathy of the shoulder. Prospective randomized controlled studies with long term assessment are needed to further document its therapeutic efficacy.

Assessment of the use of space technology in the monitoring of oil spills and ocean pollution: Technical volume. Executive summary

NASA Technical Reports Server (NTRS)

Alvarado, U. R. (Editor); Chafaris, G.; Chestek, J.; Contrad, J.; Frippel, G.; Gulatsi, R.; Heath, A.; Hodara, H.; Kritikos, H.; Tamiyasu, K.

1980-01-01

The potential of space systems and technology for detecting and monitoring ocean oil spills and waste pollution was assessed as well as the impact of this application on communication and data handling systems. Agencies charged with responsibilities in this area were identified and their measurement requirements were ascertained in order to determine the spatial resolution needed to characterize operational and accidental discharges. Microwave and optical sensors and sensing techniques were evaluated as candidate system elements. Capabilities are described for the following: synthetic aperture radar, microwave scatterometer, passive microwave radiometer, microwave altimeter, electro-optical sensors currently used in airborne detection, existing space-based optical sensors, the thematic mapper, and the pointable optical linear array.

3-D Inhomogeous Radiative Transfer Model using a Planar-stratified Forward RT Model and Horizontal Perturbation Series

NASA Astrophysics Data System (ADS)

Zhang, K.; Gasiewski, A. J.

2017-12-01

A horizontally inhomogeneous unified microwave radiative transfer (HI-UMRT) model based upon a nonspherical hydrometeor scattering model is being developed at the University of Colorado at Boulder to facilitate forward radiative simulations for 3-dimensionally inhomogeneous clouds in severe weather. The HI-UMRT 3-D analytical solution is based on incorporating a planar-stratified 1-D UMRT algorithm within a horizontally inhomogeneous iterative perturbation scheme. Single-scattering parameters are computed using the Discrete Dipole Scattering (DDSCAT v7.3) program for hundreds of carefully selected nonspherical complex frozen hydrometeors from the NASA/GSFC DDSCAT database. The required analytic factorization symmetry of transition matrix in a normalized RT equation was analytically proved and validated numerically using the DDSCAT-based full Stokes matrix of randomly oriented hydrometeors. The HI-UMRT model thus inherits the properties of unconditional numerical stability, efficiency, and accuracy from the UMRT algorithm and provides a practical 3-D two-Stokes parameter radiance solution with Jacobian to be used within microwave retrievals and data assimilation schemes. In addition, a fast forward radar reflectivity operator with Jacobian based on DDSCAT backscatter efficiency computed for large hydrometeors is incorporated into the HI-UMRT model to provide applicability to active radar sensors. The HI-UMRT will be validated strategically at two levels: 1) intercomparison of brightness temperature (Tb) results with those of several 1-D and 3-D RT models, including UMRT, CRTM and Monte Carlo models, 2) intercomparison of Tb with observed data from combined passive and active spaceborne sensors (e.g. GPM GMI and DPR). The precise expression for determining the required number of 3-D iterations to achieve an error bound on the perturbation solution will be developed to facilitate the numerical verification of the HI-UMRT code complexity and computation performance.

Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN, and RADARSAT data

USGS Publications Warehouse

Belchansky, Gennady I.; Douglas, David C.

2002-01-01

The Special Sensor Microwave Imager (SSM/I) microwave satellite radiometer and its predecessor SMMR are primary sources of information for global sea ice and climate studies. However, comparisons of SSM/I, Landsat, AVHRR, and ERS-1 synthetic aperture radar (SAR) have shown substantial seasonal and regional differences in their estimates of sea ice concentration. To evaluate these differences, we compared SSM/I estimates of sea ice coverage derived with the NASA Team and Bootstrap algorithms to estimates made using RADARSAT, and OKEAN-01 satellite sensor data. The study area included the Barents Sea, Kara Sea, Laptev Sea, and adjacent parts of the Arctic Ocean, during October 1995 through October 1999. Ice concentration estimates from spatially and temporally near-coincident imagery were calculated using independent algorithms for each sensor type. The OKEAN algorithm implemented the satellite's two-channel active (radar) and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter. The RADARSAT algorithm utilized a segmentation approach of the measured radar backscatter, and the SSM/I ice concentrations were derived at National Snow and Ice Data Center (NSIDC) using the NASA Team and Bootstrap algorithms. Seasonal and monthly differences between SSM/I, OKEAN, and RADARSAT ice concentrations were calculated and compared. Overall, total sea ice concentration estimates derived independently from near-coincident RADARSAT, OKEAN-01, and SSM/I satellite imagery demonstrated mean differences of less than 5.5% (S.D.<9.5%) during the winter period. Differences between the SSM/I NASA Team and the SSM/I Bootstrap concentrations were no more than 3.1% (S.D.<5.4%) during this period. RADARSAT and OKEAN-01 data both yielded higher total ice concentrations than the NASA Team and the Bootstrap algorithms. The Bootstrap algorithm yielded higher total ice concentrations than the NASA Team algorithm. Total ice concentrations derived from OKEAN-01 and SSM/I satellite imagery were highly correlated during winter, spring, and fall, with mean differences of less than 8.1% (S.D.<15%) for the NASA Team algorithm, and less than 2.8% (S.D.<13.8%) for the Bootstrap algorithm. Respective differences between SSM/I NASA Team and SSM/I Bootstrap total concentrations were less than 5.3% (S.D.<6.9%). Monthly mean differences between SSM/I and OKEAN differed annually by less than 6%, with smaller differences primarily in winter. The NASA Team and Bootstrap algorithms underestimated the total sea ice concentrations relative to the RADARSAT ScanSAR no more than 3.0% (S.D.<9%) and 1.2% (S.D.<7.5%) during cold months, and no more than 12% and 7% during summer, respectively. ScanSAR tended to estimate higher ice concentrations for ice concentrations greater than 50%, when compared to SSM/I during all months. ScanSAR underestimated total sea ice concentration by 2% compared to the OKEAN-01 algorithm during cold months, and gave an overestimation by 2% during spring and summer months. Total NASA Team and Bootstrap sea ice concentration estimates derived from coincident SSM/I and OKEAN-01 data demonstrated mean differences of no more than 5.3% (S.D.<7%), 3.1% (S.D.<5.5%), 2.0% (S.D.<5.5%), and 7.3% (S.D.<10%) for fall, winter, spring, and summer periods, respectively. Large disagreements were observed between the OKEAN and NASA Team results in spring and summer for estimates of the first-year (FY) and multiyear (MY) age classes. The OKEAN-01 algorithm and data tended to estimate, on average, lower concentrations of young or FY ice and higher concentrations of total and MY ice for all months and seasons. Our results contribute to the growing body of documentation about the levels of disparity obtained when seasonal sea ice concentrations are estimated using various types of satellite data and algorithms.

Operational Soil Moisture Retrieval Techniques: Theoretical Comparisons in the Context of Improving the NASA Standard Approach

NASA Astrophysics Data System (ADS)

Mladenova, I. E.; Jackson, T. J.; Bindlish, R.; Njoku, E. G.; Chan, S.; Cosh, M. H.

2012-12-01

We are currently evaluating potential improvements to the standard NASA global soil moisture product derived using observations acquired from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). A major component of this effort is a thorough review of the theoretical basis of available passive-based soil moisture retrieval algorithms suitable for operational implementation. Several agencies provide routine soil moisture products. Our research focuses on five well-establish techniques that are capable of carrying out global retrieval using the same AMSR-E data set as the NASA approach (i.e. X-band brightness temperature data). In general, most passive-based algorithms include two major components: radiative transfer modeling, which provides the smooth surface reflectivity properties of the soil surface, and a complex dielectric constant model of the soil-water mixture. These two components are related through the Fresnel reflectivity equations. Furthermore, the land surface temperature, vegetation, roughness and soil properties need to be adequately accounted for in the radiative transfer and dielectric modeling. All of the available approaches we have examined follow the general data processing flow described above, however, the actual solutions as well as the final products can be very different. This is primarily a result of the assumptions, number of sensor variables utilized, the selected ancillary data sets and approaches used to account for the effect of the additional geophysical variables impacting the measured signal. The operational NASA AMSR-E-based retrievals have been shown to have a dampened temporal response and sensitivity range. Two possible approaches to addressing these issues are being evaluated: enhancing the theoretical basis of the existing algorithm, if feasible, or directly adjusting the dynamic range of the final soil moisture product. Both of these aspects are being actively investigated and will be discussed in our talk. Improving the quality and reliability of the global soil moisture product would result in greater acceptance and utilization in the related applications. USDA is an equal opportunity provider and employer.

Assessment of Radiometer Calibration with GPS Radio Occultation for the MiRaTA CubeSat Mission.

PubMed

Marinan, Anne D; Cahoy, Kerri L; Bishop, Rebecca L; Lui, Susan S; Bardeen, James R; Mulligan, Tamitha; Blackwell, William J; Leslie, R Vincent; Osaretin, Idahosa; Shields, Michael

2016-12-01

The Microwave Radiometer Technology Acceleration (MiRaTA) is a 3U CubeSat mission sponsored by the NASA Earth Science Technology Office (ESTO). The science payload on MiRaTA consists of a tri-band microwave radiometer and Global Positioning System (GPS) radio occultation (GPSRO) sensor. The microwave radiometer takes measurements of all-weather temperature (V-band, 50-57 GHz), water vapor (G-band, 175-191 GHz), and cloud ice (G-band, 205 GHz) to provide observations used to improve weather forecasting. The Aerospace Corporation's GPSRO experiment, called the Compact TEC (Total Electron Content) and Atmospheric GPS Sensor (CTAGS), measures profiles of temperature and pressure in the upper troposphere/lower stratosphere (∼20 km) and electron density in the ionosphere (over 100 km). The MiRaTA mission will validate new technologies in both passive microwave radiometry and GPS radio occultation: (1) new ultra-compact and low-power technology for multi-channel and multi-band passive microwave radiometers, (2) the application of a commercial off the shelf (COTS) GPS receiver and custom patch antenna array technology to obtain neutral atmospheric GPSRO retrieval from a nanosatellite, and (3) a new approach to spaceborne microwave radiometer calibration using adjacent GPSRO measurements. In this paper, we focus on objective (3), developing operational models to meet a mission goal of 100 concurrent radiometer and GPSRO measurements, and estimating the temperature measurement precision for the CTAGS instrument based on thermal noise. Based on an analysis of thermal noise of the CTAGS instrument, the expected temperature retrieval precision is between 0.17 K and 1.4 K, which supports the improvement of radiometric calibration to 0.25 K.

Snow Microwave Radiative Transfer (SMRT): A new model framework to simulate snow-microwave interactions for active and passive remote sensing applications

NASA Astrophysics Data System (ADS)

Loewe, H.; Picard, G.; Sandells, M. J.; Mätzler, C.; Kontu, A.; Dumont, M.; Maslanka, W.; Morin, S.; Essery, R.; Lemmetyinen, J.; Wiesmann, A.; Floury, N.; Kern, M.

2016-12-01

Forward modeling of snow-microwave interactions is widely used to interpret microwave remote sensing data from active and passive sensors. Though different models are yet available for that purpose, a joint effort has been undertaken in the past two years within the ESA Project "Microstructural origin of electromagnetic signatures in microwave remote sensing of snow". The new Snow Microwave Radiative Transfer (SMRT) model primarily facilitates a flexible treatment of snow microstructure as seen by X-ray tomography and seeks to unite respective advantages of existing models. In its main setting, SMRT considers radiation transfer in a plane-parallel snowpack consisting of homogeneous layers with a layer microstructure represented by an autocorrelation function. The electromagnetic model, which underlies permittivity, absorption and scattering calculations within a layer, is based on the improved Born approximation. The resulting vector-radiative transfer equation in the snowpack is solved using spectral decomposition of the discrete ordinates discretization. SMRT is implemented in Python and employs an object-oriented, modular design which intends to i) provide an intuitive and fail-safe API for basic users ii) enable efficient community developments for extensions (e.g. for improvements of sub-models for microstructure, permittivity, soil or interface reflectivity) from advanced users and iii) encapsulate the numerical core which is maintained by the developers. For cross-validation and inter-model comparison, SMRT implements various ingredients of existing models as selectable options (e.g. Rayleigh or DMRT-QCA phase functions) and shallow wrappers to invoke legacy model code directly (MEMLS, DMRT-QMS, HUT). In this paper we give an overview of the model components and show examples and results from different validation schemes.

A method for combining passive microwave and infrared rainfall observations

NASA Technical Reports Server (NTRS)

Kummerow, Christian; Giglio, Louis

1995-01-01

Because passive microwave instruments are confined to polar-orbiting satellites, rainfall estimates must interpolate across long time periods, during which no measurements are available. In this paper the authors discuss a technique that allows one to partially overcome the sampling limitations by using frequent infrared observations from geosynchronous platforms. To accomplish this, the technique compares all coincident microwave and infrared observations. From each coincident pair, the infrared temperature threshold is selected that corresponds to an area equal to the raining area observed in the microwave image. The mean conditional rainfall rate as determined from the microwave image is then assigned to pixels in the infrared image that are colder than the selected threshold. The calibration is also applied to a fixed threshold of 235 K for comparison with established infrared techniques. Once a calibration is determined, it is applied to all infrared images. Monthly accumulations for both methods are then obtained by summing rainfall from all available infrared images. Two examples are used to evaluate the performance of the technique. The first consists of a one-month period (February 1988) over Darwin, Australia, where good validation data are available from radar and rain gauges. For this case it was found that the technique approximately doubled the rain inferred by the microwave method alone and produced exceptional agreement with the validation data. The second example involved comparisons with atoll rain gauges in the western Pacific for June 1989. Results here are overshadowed by the fact that the hourly infrared estimates from established techniques, by themselves, produced very good correlations with the rain gauges. The calibration technique was not able to improve upon these results.

Assessment of Radiometer Calibration with GPS Radio Occultation for the MiRaTA CubeSat Mission

PubMed Central

Marinan, Anne D.; Cahoy, Kerri L.; Bishop, Rebecca L.; Lui, Susan S.; Bardeen, James R.; Mulligan, Tamitha; Blackwell, William J.; Leslie, R. Vincent; Osaretin, Idahosa; Shields, Michael

2017-01-01

The Microwave Radiometer Technology Acceleration (MiRaTA) is a 3U CubeSat mission sponsored by the NASA Earth Science Technology Office (ESTO). The science payload on MiRaTA consists of a tri-band microwave radiometer and Global Positioning System (GPS) radio occultation (GPSRO) sensor. The microwave radiometer takes measurements of all-weather temperature (V-band, 50-57 GHz), water vapor (G-band, 175-191 GHz), and cloud ice (G-band, 205 GHz) to provide observations used to improve weather forecasting. The Aerospace Corporation's GPSRO experiment, called the Compact TEC (Total Electron Content) and Atmospheric GPS Sensor (CTAGS), measures profiles of temperature and pressure in the upper troposphere/lower stratosphere (∼20 km) and electron density in the ionosphere (over 100 km). The MiRaTA mission will validate new technologies in both passive microwave radiometry and GPS radio occultation: (1) new ultra-compact and low-power technology for multi-channel and multi-band passive microwave radiometers, (2) the application of a commercial off the shelf (COTS) GPS receiver and custom patch antenna array technology to obtain neutral atmospheric GPSRO retrieval from a nanosatellite, and (3) a new approach to spaceborne microwave radiometer calibration using adjacent GPSRO measurements. In this paper, we focus on objective (3), developing operational models to meet a mission goal of 100 concurrent radiometer and GPSRO measurements, and estimating the temperature measurement precision for the CTAGS instrument based on thermal noise. Based on an analysis of thermal noise of the CTAGS instrument, the expected temperature retrieval precision is between 0.17 K and 1.4 K, which supports the improvement of radiometric calibration to 0.25 K. PMID:28828144

GPM Plans for Radiometer Intercalibration

NASA Technical Reports Server (NTRS)

Stocker, Erich Franz; Stout, John; Chou, Joyce

2011-01-01

The international Global Precipitation Measurement (GPM) mission led by NASA and JAXA is planned as a multi-radiometer constellation mission. A key mission component is the ability to intercalibrate the Tb from the partner constellation radiometers and create inter-calibrated, mission consistent Tc. One of the enabling strategies for this approach is the launching of a joint NASA/JAXA core satellite which contains a JAXA/NICT provided dual precipitation radar and a NASA provided Microwave Imaging passive radiometer. The observations from these instruments on the core satellite provide the opportunity to develop a transfer reference standard that can then be applied across the partner provided constellation radiometers that enables the creation of mission consistent brightness temperatures. The other aspect of the strategy is the development of a community consensus intercalibration algorithm that will be applied to the Tb observations from partner radiometers and create the best calibrated Tc. Also described is the development of the framework in which the inter-calibration is included in the final algorithm. A part of the latter effort has been the development of a generic, logical structure which can be applied across radiometer types and which guarantees the user community that key information for using Tc properly is recorded. Key

Help, I don’t know which sea ice algorithm to use?!: Developing an authoritative sea ice climate data record

NASA Astrophysics Data System (ADS)

Meier, W.; Stroeve, J.; Duerr, R. E.; Fetterer, F. M.

2009-12-01

The declining Arctic sea ice is one of the most dramatic indicators of climate change and is being recognized as a key factor in future climate impacts on biology, human activities, and global climate change. As such, the audience for sea ice data is expanding well beyond the sea ice community. The most comprehensive sea ice data are from a series of satellite-borne passive microwave sensors. They provide a near-complete daily timeseries of sea ice concentration and extent since late-1978. However, there are many complicating issues in using such data, particularly for novice users. First, there is not one single, definitive algorithm, but several. And even for a given algorithm, different processing and quality-control methods may be used, depending on the source. Second, for all algorithms, there are uncertainties in any retrieved value. In general, these limitations are well-known: low spatial-resolution results in an imprecise ice edge determination and lack of small-scale detail (e.g., lead detection) within the ice pack; surface melt depresses concentration values during summer; thin ice is underestimated in some algorithms; some algorithms are sensitive to physical surface temperature; other surface features (e.g., snow) can influence retrieved data. While general error estimates are available for concentration values, currently the products do not carry grid-cell level or even granule level data quality information. Finally, metadata and data provenance information are limited, both of which are essential for future reprocessing. Here we describe the progress to date toward development of sea ice concentration products and outline the future steps needed to complete a sea ice climate data record.

On the performance of SART and ART algorithms for microwave imaging

NASA Astrophysics Data System (ADS)

Aprilliyani, Ria; Prabowo, Rian Gilang; Basari

2018-02-01

The development of advanced technology leads to the change of human lifestyle in current society. One of the disadvantage impact is arising the degenerative diseases such as cancers and tumors, not just common infectious diseases. Every year, victims of cancers and tumors grow significantly leading to one of the death causes in the world. In early stage, cancer/tumor does not have definite symptoms, but it will grow abnormally as tissue cells and damage normal tissue. Hence, early cancer detection is required. Some common diagnostics modalities such as MRI, CT and PET are quite difficult to be operated in home or mobile environment such as ambulance. Those modalities are also high cost, unpleasant, complex, less safety and harder to move. Hence, this paper proposes a microwave imaging system due to its portability and low cost. In current study, we address on the performance of simultaneous algebraic reconstruction technique (SART) algorithm that was applied in microwave imaging. In addition, SART algorithm performance compared with our previous work on algebraic reconstruction technique (ART), in order to have performance comparison, especially in the case of reconstructed image quality. The result showed that by applying SART algorithm on microwave imaging, suspicious cancer/tumor can be detected with better image quality.

Retrieving Liquid Water Path and Precipitable Water Vapor from the Atmospheric Radiation Measurement (ARM) Microwave Radiometers

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

Turner, David D.; Clough, Shepard A.; Liljegren, James C.

2007-11-01

Ground-based two-channel microwave radiometers have been used for over 15 years by the Atmospheric Radiation Measurement (ARM) program to provide observations of downwelling emitted radiance from which precipitable water vapor (PWV) and liquid water path (LWP) – twp geophysical parameters critical for many areas of atmospheric research – are retrieved. An algorithm that utilizes two advanced retrieval techniques, a computationally expensive physical-iterative approach and an efficient statistical method, has been developed to retrieve these parameters. An important component of this Microwave Retrieval (MWRRET) algorithm is the determination of small (< 1K) offsets that are subtracted from the observed brightness temperaturesmore » before the retrievals are performed. Accounting for these offsets removes systematic biases from the observations and/or the model spectroscopy necessary for the retrieval, significantly reducing the systematic biases in the retrieved LWP. The MWRRET algorithm provides significantly more accurate retrievals than the original ARM statistical retrieval which uses monthly retrieval coefficients. By combining the two retrieval methods with the application of brightness temperature offsets to reduce the spurious LWP bias in clear skies, the MWRRET algorithm provides significantly better retrievals of PWV and LWP from the ARM 2-channel microwave radiometers compared to the original ARM product.« less

Remote sensing of snow and ice.

USGS Publications Warehouse

Meier, M.F.

1980-01-01

Active and passive sensors operating in the visible, near infrared, thermal infrared, and microwave wavelengths are described in regard to general applications and in regard to specific USA or USSR satellites. -from Author

Tunable microwave metasurfaces for high-performance operations: dispersion compensation and dynamical switch

PubMed Central

Xu, He-Xiu; Tang, Shiwei; Ma, Shaojie; Luo, Weijie; Cai, Tong; Sun, Shulin; He, Qiong; Zhou, Lei

2016-01-01

Controlling the phase distributions on metasurfaces leads to fascinating effects such as anomalous light refraction/reflection, flat-lens focusing, and optics-vortex generation. However, metasurfaces realized so far largely reply on passive resonant meta-atoms, whose intrinsic dispersions limit such passive meta-devices’ performances at frequencies other than the target one. Here, based on tunable meta-atoms with varactor diodes involved, we establish a scheme to resolve these issues for microwave metasurfaces, in which the dispersive response of each meta-atom is precisely controlled by an external voltage imparted on the diode. We experimentally demonstrate two effects utilizing our scheme. First, we show that a tunable gradient metasurface exhibits single-mode high-efficiency operation within a wide frequency band, while its passive counterpart only works at a single frequency but exhibits deteriorated performances at other frequencies. Second, we demonstrate that the functionality of our metasurface can be dynamically switched from a specular reflector to a surface-wave convertor. Our approach paves the road to achieve dispersion-corrected and switchable manipulations of electromagnetic waves. PMID:27901088

Tunable microwave metasurfaces for high-performance operations: dispersion compensation and dynamical switch.

PubMed

Xu, He-Xiu; Tang, Shiwei; Ma, Shaojie; Luo, Weijie; Cai, Tong; Sun, Shulin; He, Qiong; Zhou, Lei

2016-11-30

Controlling the phase distributions on metasurfaces leads to fascinating effects such as anomalous light refraction/reflection, flat-lens focusing, and optics-vortex generation. However, metasurfaces realized so far largely reply on passive resonant meta-atoms, whose intrinsic dispersions limit such passive meta-devices' performances at frequencies other than the target one. Here, based on tunable meta-atoms with varactor diodes involved, we establish a scheme to resolve these issues for microwave metasurfaces, in which the dispersive response of each meta-atom is precisely controlled by an external voltage imparted on the diode. We experimentally demonstrate two effects utilizing our scheme. First, we show that a tunable gradient metasurface exhibits single-mode high-efficiency operation within a wide frequency band, while its passive counterpart only works at a single frequency but exhibits deteriorated performances at other frequencies. Second, we demonstrate that the functionality of our metasurface can be dynamically switched from a specular reflector to a surface-wave convertor. Our approach paves the road to achieve dispersion-corrected and switchable manipulations of electromagnetic waves.

Tunable microwave metasurfaces for high-performance operations: dispersion compensation and dynamical switch

NASA Astrophysics Data System (ADS)

Xu, He-Xiu; Tang, Shiwei; Ma, Shaojie; Luo, Weijie; Cai, Tong; Sun, Shulin; He, Qiong; Zhou, Lei

2016-11-01

Controlling the phase distributions on metasurfaces leads to fascinating effects such as anomalous light refraction/reflection, flat-lens focusing, and optics-vortex generation. However, metasurfaces realized so far largely reply on passive resonant meta-atoms, whose intrinsic dispersions limit such passive meta-devices’ performances at frequencies other than the target one. Here, based on tunable meta-atoms with varactor diodes involved, we establish a scheme to resolve these issues for microwave metasurfaces, in which the dispersive response of each meta-atom is precisely controlled by an external voltage imparted on the diode. We experimentally demonstrate two effects utilizing our scheme. First, we show that a tunable gradient metasurface exhibits single-mode high-efficiency operation within a wide frequency band, while its passive counterpart only works at a single frequency but exhibits deteriorated performances at other frequencies. Second, we demonstrate that the functionality of our metasurface can be dynamically switched from a specular reflector to a surface-wave convertor. Our approach paves the road to achieve dispersion-corrected and switchable manipulations of electromagnetic waves.

Soil Moisture Active Passive (SMAP) Microwave Radiometer Radio-Frequency Interference (RFI) Mitigation: Initial On-Orbit Results

NASA Technical Reports Server (NTRS)

Mohammed, Priscilla N.; Piepmeier, Jeffrey R.; Johnson, Joel T.; Aksoy, Mustafa; Bringer, Alexandra

2015-01-01

The Soil Moisture Active Passive (SMAP) mission, launched in January 2015, provides global measurements of soil moisture using a microwave radiometer. SMAPs radiometer passband lies within the passive frequency allocation. However, both unauthorized in-band transmitters as well as out-of-band emissions from transmitters operating at frequencies adjacent to this allocated spectrum have been documented as sources of radio frequency interference (RFI) to the L-band radiometers on SMOS and Aquarius. The spectral environment consists of high RFI levels as well as significant occurrences of low level RFI equivalent to 0.1 to 10 K. The SMAP ground processor reports the antenna temperature both before and after RFI mitigation is applied. The difference between these quantities represents the detected RFI level. The presentation will review the SMAP RFI detection and mitigation procedure and discuss early on-orbit RFI measurements from the SMAP radiometer. Assessments of global RFI properties and source types will be provided, as well as the implications of these results for SMAP soil moisture measurements.

Aircraft active microwave measurements for estimating soil moisture

NASA Technical Reports Server (NTRS)

Jackson, T. J.; Chang, A.; Schmugge, T. J.

1981-01-01

Both active and passive microwave sensors are sensitive to variations in near-surface soil moisture. The principal advantage of active microwave systems for soil moisture applications is that high spatial resolution can be retained even at satellite attitudes. The considered investigation is concerned with the use of active microwave scatterometers for estimating near-surface soil moisture. Microwave scatterometer data were obtained during a series of three aircraft flights over a group of Oklahoma research watersheds during May 1978. Data were obtained for the C, L, and P bands at angles of incidence between 5 and 50 degrees. The best results were obtained using C band data at incidence angles of 10 and 15 degrees and soil moisture depth of 0 to 15 cm. These results were in excellent agreement with the conclusions of the truck-mounted scatterometer measurement program reported by Ulaby et al. (1978, 1979).

A monolithic integrated photonic microwave filter

NASA Astrophysics Data System (ADS)

Fandiño, Javier S.; Muñoz, Pascual; Doménech, David; Capmany, José

2017-02-01

Meeting the increasing demand for capacity in wireless networks requires the harnessing of higher regions in the radiofrequency spectrum, reducing cell size, as well as more compact, agile and power-efficient base stations that are capable of smoothly interfacing the radio and fibre segments. Fully functional microwave photonic chips are promising candidates in attempts to meet these goals. In recent years, many integrated microwave photonic chips have been reported in different technologies. To the best of our knowledge, none has monolithically integrated all the main active and passive optoelectronic components. Here, we report the first demonstration of a tunable microwave photonics filter that is monolithically integrated into an indium phosphide chip. The reconfigurable radiofrequency photonic filter includes all the necessary elements (for example, lasers, modulators and photodetectors), and its response can be tuned by means of control electric currents. This is an important step in demonstrating the feasibility of integrated and programmable microwave photonic processors.

Sensor Calibration and Ocean Products for TRMM Microwave Radiometer

NASA Technical Reports Server (NTRS)

Wentz, Frank J.; Lawrence, Richard J. (Technical Monitor)

2003-01-01

During the three years of finding, we have carefully corrected for two sensor/platform problems, developed a physically based retrieval algorithm to calculate SST, wind speed, water vapor, cloud liquid water and rain rates, validated these variables, and demonstrated that satellite microwave radiometers can provide very accurate SST retrievals through clouds. Prior to this, there was doubt by some scientists that the technique of microwave SST retrieval from satellites is a viable option. We think we have put these concerns to rest, and look forward to making microwave SSTs a standard component of the Earth science data sets. Our TMI SSTs were featured on several network news broadcasts and were reported in Science magazine. Additionally, we have developed a SST algorithm for VIRS to facilitate IR/MW inter-comparisons and completed research into diurnal cycles and air-sea interactions.

Sensor Calibration and Ocean Products for TRMM Microwave Radiometer

NASA Technical Reports Server (NTRS)

Lawrence, Richard J. (Technical Monitor); Wentz, Frank J.

2003-01-01

During the three years of fundin& we have carefully corrected for two sensor/platform problems, developed a physically based retrieval algorithm to calculate SST, wind speed, water vapor, cloud liquid water and rain rates, validated these variables, and demonstrated that satellite microwave radiometers can provide very accurate SST retrievals through clouds. Prior to this, there was doubt by some scientists that the technique of microwave SST retrieval from satellites is a viable option. We think we have put these concerns to rest, and look forward to making microwave SSTs a standard component of the Earth science data sets. Our TMI SSTs were featured on several network news broadcasts and were reported in Science magazine. Additionally, we have developed a SST algorithm for VIRS to facilitate IR/MW inter-comparisons and completed research into diurnal cycles and air-sea interactions.

Frequency band justifications for passive sensors, 1 to 10 GHz. [for monitoring earth resources and the environment

NASA Technical Reports Server (NTRS)

1976-01-01

Remote sensor systems operating in the microwave region of the frequency spectrum provide information unobtainable with basic imaging techniques such as photography, television, or multispectral imaging. The frequency allocation requirements for passive microwave sensors used in the earth exploration satellite and space research services are presented for: (1) agriculture, forestry, and range resources; (2) land use survey and mapping: (3) water resources; (4) weather and climate; (5) environmental quality; and (6) marine resources, estuarine and oceans. Because measurements are required simultaneously in multiple frequency bands to adequately determine values of some phenomena, the relationships between frequency bands are discussed. The various measurement accuracies, dynamic range, resolutions and frequency needs are examined. A band-by-band summary of requirements, unique aspects, and sharing analyses of the required frequency bands is included.

Generation of “gigantic” ultra-short microwave pulses based on passive mode-locking effect in electron oscillators with saturable absorber in the feedback loop

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

Ginzburg, N. S., E-mail: ginzburg@appl.sci-nnov.ru; Denisov, G. G.; Vilkov, M. N.

2016-05-15

A periodic train of powerful ultrashort microwave pulses can be generated in electron oscillators with a non-linear saturable absorber installed in the feedback loop. This method of pulse formation resembles the passive mode-locking widely used in laser physics. Nevertheless, there is a specific feature in the mechanism of pulse amplification when consecutive energy extraction from different fractions of a stationary electron beam takes place due to pulse slippage over the beam caused by the difference between the wave group velocity and the electron axial velocity. As a result, the peak power of generated “gigantic” pulses can exceed not only themore » level of steady-state generation but also, in the optimal case, the power of the driving electron beam.« less

Synergistic use of multispectral satellite data for monitoring land surface change

NASA Technical Reports Server (NTRS)

Choudhury, Bhaskar J.

1991-01-01

Observations by the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites were used to compute visible and near infrared reflectances and surface temperature, while passive microwave observations at 37 GHz frequency by the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) on board, respectively, the Nimbus-7 and DMSP-F8 satellites were used to compute polarization difference. These observations were analyzed along transects from rainforest to desert over northern Africa for the period 1979-1987, which included an unprecedented drought during 1984 over the Sahel zone. Model simulations were made to understand the interrelationship among multispectral data.

High resolution radiometric measurements of convective storms during the GATE experiment

NASA Technical Reports Server (NTRS)

Fowler, G.; Lisa, A. S.

1976-01-01

Using passive microwave data from the NASA CV-990 aircraft and radar data collected during the Global Atmospheric Research Program Atlantic Tropical Experiment (GATE), an empirical model was developed relating brightness temperatures sensed at 19.35 GHz to surface rainfall rates. This model agreed well with theoretical computations of the relationship between microwave radiation and precipitation in the tropics. The GATE aircraft microwave data was then used to determine the detailed structure of convective systems. The high spatial resolution of the data permitted identification of individual cells which retained unique identities throughout their lifetimes in larger cloud masses and allowed analysis of the effects of cloud merger.

High-performance flexible microwave passives on plastic

NASA Astrophysics Data System (ADS)

Ma, Zhenqiang; Seo, Jung-Hun; Cho, Sang June; Zhou, Weidong

2014-06-01

We report the demonstration of bendable inductors, capacitors and switches fabricated on a polyethylene terephthalate (PET) substrate that can operate at high microwave frequencies. By employing bendable dielectric and single crystalline semiconductor materials, spiral inductors and metal-insulator-metal (MIM) capacitors with high quality factors and high resonance frequencies and single-pole, single-throw (SPST) switches were archived. The effects of mechanical bending on the performance of inductors, capacitors and switches were also measured and analyzed. We further investigated the highest possible resonance frequencies and quality factors of inductors and capacitors and, high frequency responses and insertion loss. These demonstrations will lead to flexible radio-frequency and microwave systems in the future.

Book Review

NASA Astrophysics Data System (ADS)

Clevers, J. G. P. W.

2015-02-01

About thirty years after the previous advanced textbook on Microwave Remote Sensing by Ulaby, Moore and Fung has been published as three separate volumes, now an up-to-date new textbook has been published. The 1000-page book covers theoretical models, system design and operation, and geoscientific applications of active and passive microwave remote sensing systems. It is designed as a textbook at the postgraduate level, as well as a reference for the practicing professional. The book is caught by a thorough introduction into the physics and mathematics of electrical engineering applied to microwave radiation. Here on overview of its chapters with a short description of its focus will be given.

Development of a modified two-scale electromagnetic model simulating both active and passive microwave measurements: Comparison to data remotely sensed over the ocean

NASA Astrophysics Data System (ADS)

Boukabara, S. A.; Eymard, L.; Guillou, C.; Lemaire, D.; Sobieski, P.; Guissard, A.

2002-08-01

Spaceborne microwave remote sensing allows the determination of oceanic and atmospheric parameters. Operational payloads such as ERS-1 and ERS-2 and TOPEX/Poseidon as well as missions such as Jason (from NASA-Centre National d'Etudes) or Envisat (from the European Space Agency), have contained or contain paired microwave instruments looking at the nadir direction. This combination consists of microwave radiometers and a radar-altimeter. For the frequencies chosen in oceanographic satellite payloads, the active mode signal is mostly dependent on the surface state through its reflectivity and thus used for the near-surface wind speed retrieval. The active mode can also be attenuated by the atmosphere. On the other hand, the passive mode is related to the surface emissivity and the atmospheric radiation through the radiative transfer equation. Until now, the oceanic and atmospheric parameters have been retrieved separately, the latter being used to correct radar measurements. However, the reflectivity and the emissivity of a target are not independent quantities; hence the synergistic use of these two kinds of microwave measurements should allow one to improve the retrieval quality of the sea and atmosphere parameters. For this purpose, a unified model has been developed for the simulation of both the microwave backscattering coefficient σ° (active measurement) and the microwave emissivity, an important factor for the brightness temperature TB simulation, for every configuration (incidence angles, frequency, polarizations), taking into account the fact that the reflectivity and the emissivity are complementary to unity. The atmospheric absorption is computed following a widely used model from the literature. This paper gives a description and a first attempt of validation of this approach through a comparison with real data. The performance of the model is assessed by comparing the simulations to both brightness temperatures and backscattering coefficients from ERS-1 and TOPEX/Poseidon's instruments during the SEMAPHORE experiment, over a two-month period.

Microwave Radiometer Technology Acceleration Mission (MiRaTA): Advancing Weather Remote Sensing with Nanosatellites

NASA Astrophysics Data System (ADS)

Cahoy, K.; Blackwell, W. J.; Bishop, R. L.; Erickson, N.; Fish, C. S.; Neilsen, T. L.; Stromberg, E. M.; Bardeen, J.; Dave, P.; Marinan, A.; Marlow, W.; Kingsbury, R.; Kennedy, A.; Byrne, J. M.; Peters, E.; Allen, G.; Burianek, D.; Busse, F.; Elliott, D.; Galbraith, C.; Leslie, V. V.; Osaretin, I.; Shields, M.; Thompson, E.; Toher, D.; DiLiberto, M.

2014-12-01

The Microwave Radiometer Technology Acceleration (MiRaTA) is a 3U CubeSat mission sponsored by the NASA Earth Science Technology Office (ESTO). Microwave radiometer measurements and GPS radio occultation (GPSRO) measurements of all-weather temperature and humidity provide key contributions toward improved weather forecasting. The MiRaTA mission will validate new technologies in both passive microwave radiometry and GPS radio occultation: (1) new ultra-compact and low-power technology for multi-channel and multi-band passive microwave radiometers, and (2) new GPS receiver and patch antenna array technology for GPS radio occultation retrieval of both temperature-pressure profiles in the atmosphere and electron density profiles in the ionosphere. In addition, MiRaTA will test (3) a new approach to spaceborne microwave radiometer calibration using adjacent GPSRO measurements. The radiometer measurement quality can be substantially improved relative to present systems through the use of proximal GPSRO measurements as a calibration standard for radiometric observations, reducing and perhaps eliminating the need for costly and complex internal calibration targets. MiRaTA will execute occasional pitch-up maneuvers so that the radiometer and GPSRO observations sound overlapping volumes of atmosphere through the Earth's limb. To validate system performance, observations from both microwave radiometer (MWR) and GPSRO instruments will be compared to radiosondes, global high-resolution analysis fields, other satellite observations, and to each other using radiative transfer models. Both the radiometer and GPSRO payloads, currently at TRL5 but to be advanced to TRL7 at mission conclusion, can be accommodated in a single 3U CubeSat. The current plan is to launch from an International Space Station (ISS) orbit at ~400 km altitude and 52° inclination for low-cost validation over a ~90-day mission to fly in 2016. MiRaTA will demonstrate high fidelity, well-calibrated radiometric sensing from a nanosatellite platform, thereby enabling new architectural approaches for mission implementation at lower cost and risk with more flexible access to space.

Passive Microwave Studies of Atmospheric Precipitation and State

NASA Technical Reports Server (NTRS)

Staelin, David H.; Rosenkranz, Philip W.; Shiue, James C. (Technical Monitor)

2002-01-01

The principal contributions of this research on novel passive microwave spectral techniques are in the areas of: (1) global precipitation mapping using the opaque spectral bands on research and operational weather satellites, (2) development and analysis of extensive aircraft observational imaging data sets obtained using the MIT instrument NAST-M near 54 and 118 GHz over hurricanes and weather ranging from tropical to polar; simultaneous data from the 8500-channel infrared spectrometer NAST-I was obtained and analyzed separately, (3) estimation of hydrometeor diameters in cell tops using data from aircraft and spacecraft, (4) continued improvement of expressions for atmospheric transmittance at millimeter and sub-millimeter wavelengths, (5) development and airborne use of spectrometers operating near 183- and 425-GHz bands, appropriate to practical systems in geosynchronous orbit, and (6) preliminary studies of the design and performance of future geosynchronous microwave sounders for temperature and humidity profiles and for continuous monitoring of regional precipitation through most clouds. This work was a natural extension of work under NASA Grant NAG5-2545 and its predecessors. This earlier work had developed improved airborne imaging microwave spectrometers and had shown their sensitivity to precipitation altitude and character. They also had prepared the foundations for precipitation estimation using the opaque microwave bands. The field demonstration and improvement of these capabilities was then a central part of the present research reported here, during which period the first AMSU data became available and several hurricanes were overflown by NAST-M, yielding unique data about their microwave signatures. This present work has in turn helped lay the foundation for future progress in incorporating the opaque microwave channels in systems for climatologically precise global precipitation mapping from current and future operational satellites. Extension of these techniques to global snowfall mapping, even over ice and snow, is one such opportunity signaled by this research.

Parallel Processing Systems for Passive Ranging During Helicopter Flight

NASA Technical Reports Server (NTRS)

Sridhar, Bavavar; Suorsa, Raymond E.; Showman, Robert D. (Technical Monitor)

1994-01-01

The complexity of rotorcraft missions involving operations close to the ground result in high pilot workload. In order to allow a pilot time to perform mission-oriented tasks, sensor-aiding and automation of some of the guidance and control functions are highly desirable. Images from an electro-optical sensor provide a covert way of detecting objects in the flight path of a low-flying helicopter. Passive ranging consists of processing a sequence of images using techniques based on optical low computation and recursive estimation. The passive ranging algorithm has to extract obstacle information from imagery at rates varying from five to thirty or more frames per second depending on the helicopter speed. We have implemented and tested the passive ranging algorithm off-line using helicopter-collected images. However, the real-time data and computation requirements of the algorithm are beyond the capability of any off-the-shelf microprocessor or digital signal processor. This paper describes the computational requirements of the algorithm and uses parallel processing technology to meet these requirements. Various issues in the selection of a parallel processing architecture are discussed and four different computer architectures are evaluated regarding their suitability to process the algorithm in real-time. Based on this evaluation, we conclude that real-time passive ranging is a realistic goal and can be achieved with a short time.

Estimating Soil and Vegetation Parameters using Synergies between Optical and Microwave Observations

NASA Astrophysics Data System (ADS)

Timmermans, J.; Gomez-Dans, J. L.; Lewis, P.; Loew, A.; Schlenz, F.; Mathieu, P. P.; Pounder, N. L.; Styles, J.

2017-12-01

The large amount of remote sensing data available provides a huge potential for various applications, such as crop monitoring. This potential has not been realized yet because inversion-algorithms mostly use a single sensor approach. Consequently, products that combine different low-level observations from different sensors are hard to find. The difficulty in a multi-sensor approach is that 1) different sensor types (microwave/ optical) require different radiative transfer (RT) models and 2) it require consistency between the models. The goal of this research was to investigate the synergistic potential of integrating optical (Opt) and passive microwave (PM) RT models within the Earth Observation Land Data Assimilation System (EOLDAS). EOLDAS uses a Bayesian data assimilation approach together with observation operators such as PROSAIL to estimate state variables. In order to use PM observations, the Community Microwave Emission Model was integrated into the system. Results show a high potential when both Opt and PM observations are used independently. Using only RapidEye only with SAIL RT model, LAI was estimated with R=0.68, with leaf water content and dry matter having lower correlations |R|<0.4. Results for retrieving soil temperature and leaf area index retrievals using only Elbarra observations were good with respectively R=[0.85, 0.79], and for soil moisture also very good with R=0.73 (focusing on dry-spells of at least 9 days only), and with R=0.89 and R=0.77 for respectively the trend and anomalies. Synergistically using Opt and MW observations also shows good potential. Results show that absolute errors decreased (with RMSE=1.22 and S=0.89), but with lower R=0.59; sparse optical observations only improved part of the temporal domain. This shows that PM observations provide good information for the overall trend of the retrieved LAI due to the regular acquisitions, while Opt observations provides better information of the absolute values of the LAI.