Land Contamination Correction for Passive Microwave Radiometer Data: Demonstration of Wind Retrieval in the Great Lakes Using SSM/I

2014 ◽  
Vol 31 (10) ◽  
pp. 2094-2113 ◽  
Author(s):  
John Xun Yang ◽  
Darren S. Mckague ◽  
Christopher S. Ruf
Keyword(s):  
Great Lakes ◽  
Microwave Radiometer ◽  
Simulated Data ◽  
Passive Microwave ◽  
Current Standard ◽  
Wind Retrieval ◽  
Microwave Imager ◽  
Wind Retrievals ◽  
Land Water ◽  
Buoy Measurements

Abstract Passive microwave radiometer data over the ocean have been widely used, but data near coastlines or over lakes often cannot be used because of the large footprint with mixed signals from both land and water. For example, current standard Special Sensor Microwave Imager (SSM/I) products, including wind, water vapor, and precipitation, are typically unavailable within about 100 km of any coastline. This paper presents methods of correcting land-contaminated radiometer data in order to extract the coastal information. The land contamination signals are estimated, and then removed, using a representative antenna pattern convolved with a high-resolution land–water mask. This method is demonstrated using SSM/I data over the Great Lakes and validated with simulated data and buoy measurements. The land contamination is significantly reduced, and the wind speed retrieval is improved. This method is not restricted to SSM/I and wind retrievals alone; it can be applied more generally to microwave radiometer measurements in coastal regions for other retrieval purposes.

scholarly journals Improved Passive Microwave Retrievals of Rain Rate over Land and Ocean. Part I: Algorithm Description

2013 ◽  
Vol 30 (11) ◽  
pp. 2493-2508 ◽  
Author(s):  
Grant W. Petty ◽  
Ke Li
Keyword(s):  
Dimensional Reduction ◽  
Rain Rate ◽  
A Priori ◽  
Companion Paper ◽  
Passive Microwave ◽  
Effective Density ◽  
Retrieval Algorithm ◽  
Current Standard ◽  
Surface Rain Rate ◽  
Microwave Imager

Abstract A new approach to passive microwave retrievals of precipitation is described that relies on an objective dimensional reduction procedure to filter, normalize, and decorrelate geophysical background noise while retaining the majority of radiometric information concerning precipitation. The dimensional reduction also sharply increases the effective density of any a priori database used in a Bayesian retrieval scheme. The method is applied to passive microwave data from the Tropical Rainfall Measuring Mission (TRMM), reducing the original nine channels to three “pseudochannels” that are relatively insensitive to most background variations occurring within each of seven surface classes (one ocean plus six land and coast) for which they are defined. These pseudochannels may be used in any retrieval algorithm, including the current standard Goddard profiling algorithm (GPROF), in place of the original channels. The same methods are also under development for the Global Precipitation Measurement (GPM) Core Observatory Microwave Imager (GMI). Starting with the pseudochannel definitions, a new Bayesian algorithm for retrieving the surface rain rate is described. The algorithm uses an a priori database populated with matchups between the TRMM precipitation radar (PR) and the TRMM Microwave Imager (TMI). The explicit goal of the algorithm is to retrieve the PR-derived best estimate of the surface rain rate in portions of the TMI swath not covered by the PR. A unique feature of the new algorithm is that it provides robust posterior Bayesian probabilities of pixel-averaged rain rate exceeding various thresholds. Validation and intercomparison of the new algorithm is the subject of a companion paper.

scholarly journals Satellite passive microwave observations and analysis of Arctic and Antarctic sea ice, 1978–1987

1993 ◽  
Vol 17 ◽  
pp. 149-154 ◽  
Author(s):  
Per Gloersen ◽  
William J. Campbell ◽  
Donald J. Cavalieri ◽  
Josefino C. Comiso ◽  
Claire L. Parkinson ◽  
...  
Keyword(s):  
Sea Ice ◽  
Single Channel ◽  
Incidence Angle ◽  
Microwave Radiometer ◽  
Open Water ◽  
Temporal Variations ◽  
Passive Microwave ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Microwave Imager

We have recently completed an analysis that examines in detail the spatial and temporal variations in global sea-ice coverage from 26 October 1978, through 20 August 1987. The sea-icemeasurements we analyzed are derived from data collected by a multispectral, dual-polarized, constant incidence-angle microwave imager, the Scanning Multichannel Microwave Radiometer (SMMR) on board the NASA Nimbus 7 satellite. The characteristics of the SMMR have permitted a more accurate calculation of total sea-ice concentrations (fraction of ocean area covered by sea ice) than earlier single-channel instruments and, for the first time, a determination of both multiyear sea-ice concentrations and physical temperatures of the sea-ice pack. An estimate of the SMMR wintertime total ice concentration accuracy of ± 7% in both hemispheres has been obtained. As this is an improvement over the estimated accuracies of previous microwave sensors, we are able to present improved calculations of the sea-ice extents (areas enclosed by the 15% ice concentration boundaries), sea-ice concentrations, and open-water areas within the ice margins. This analysis will be published in a book, Arctic and Antarctic sea ice, 1978–1987: satellite passive microwave observations and analysis, due for publication in1992. Some highlights from the analysis are presented in this paper.

scholarly journals Satellite passive microwave observations and analysis of Arctic and Antarctic sea ice, 1978–1987

1993 ◽  
Vol 17 ◽  
pp. 149-154 ◽  
Author(s):  
Per Gloersen ◽  
William J. Campbell ◽  
Donald J. Cavalieri ◽  
Josefino C. Comiso ◽  
Claire L. Parkinson ◽  
...  
Keyword(s):  
Sea Ice ◽  
Single Channel ◽  
Incidence Angle ◽  
Microwave Radiometer ◽  
Open Water ◽  
Temporal Variations ◽  
Passive Microwave ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Microwave Imager

We have recently completed an analysis that examines in detail the spatial and temporal variations in global sea-ice coverage from 26 October 1978, through 20 August 1987. The sea-icemeasurements we analyzed are derived from data collected by a multispectral, dual-polarized, constant incidence-angle microwave imager, the Scanning Multichannel Microwave Radiometer (SMMR) on board the NASA Nimbus 7 satellite. The characteristics of the SMMR have permitted a more accurate calculation of total sea-ice concentrations (fraction of ocean area covered by sea ice) than earlier single-channel instruments and, for the first time, a determination of both multiyear sea-ice concentrations and physical temperatures of the sea-ice pack. An estimate of the SMMR wintertime total ice concentration accuracy of ± 7% in both hemispheres has been obtained. As this is an improvement over the estimated accuracies of previous microwave sensors, we are able to present improved calculations of the sea-ice extents (areas enclosed by the 15% ice concentration boundaries), sea-ice concentrations, and open-water areas within the ice margins. This analysis will be published in a book, Arctic and Antarctic sea ice, 1978–1987: satellite passive microwave observations and analysis, due for publication in1992. Some highlights from the analysis are presented in this paper.

Revealing the Winds under the Rain. Part I: Passive Microwave Rain Retrievals Using a New Observation-Based Parameterization of Subsatellite Rain Variability and Intensity—Algorithm Description

2013 ◽  
Vol 52 (12) ◽  
pp. 2828-2848 ◽  
Author(s):  
S. M. Hristova-Veleva ◽  
P. S. Callahan ◽  
R. S. Dunbar ◽  
B. W. Stiles ◽  
S. H. Yueh ◽  
...  
Keyword(s):  
Simulated Data ◽  
Passive Microwave ◽  
Spatial Inhomogeneity ◽  
Retrieval Algorithm ◽  
Wind Analysis ◽  
Weather Forecasters ◽  
Ocean Surface Winds ◽  
Wind Retrievals ◽  
Rain Retrieval ◽  
Nonuniform Beam

AbstractScatterometer ocean surface winds have been providing very valuable information to researchers and operational weather forecasters for over 10 years. However, the scatterometer wind retrievals are compromised when rain is present. Merely flagging all rain-affected areas removes the most dynamic and interesting areas from the wind analysis. Fortunately, the Advanced Earth Observing Satellite II (ADEOS-II) mission carried a radiometer [the Advanced Microwave Scanning Radiometer (AMSR)] and a scatterometer, allowing for independent, collocated retrievals of rain. The authors developed an algorithm that uses AMSR observations to estimate the rain inside the scatterometer beam. This is the first in a series of papers that describe their approach to providing rain estimation and correction to scatterometer observations. This paper describes the retrieval algorithm and evaluates it using simulated data. Part II will present its validation when applied to AMSR observations. This passive microwave rain retrieval algorithm addresses the issues of nonuniform beam filling and hydrometeor uncertainty in a novel way by 1) using a large number of soundings to develop the retrieval database, thus accounting for the geographically varying atmospheric parameters; 2) addressing the spatial inhomogeneity of rain by developing multiple retrieval databases with different built-in inhomogeneity and rain intensity, along with a “rain indicator” to select the most appropriate database for each observed scene; 3) developing a new cloud-versus-rain partitioning that allows the use of a variety of drop size distribution assumptions to account for some of the natural variability diagnosed from the soundings; and 4) retrieving atmospheric and surface parameters just outside the rainy areas, thus providing information about the environment to help decrease the uncertainty of the rain estimates.

scholarly journals Improvement of TMI Rain Retrievals in Mountainous Areas

2013 ◽  
Vol 52 (1) ◽  
pp. 242-254 ◽  
Author(s):  
Shoichi Shige ◽  
Satoshi Kida ◽  
Hiroki Ashiwake ◽  
Takuji Kubota ◽  
Kazumasa Aonashi
Keyword(s):  
Heavy Rainfall ◽  
Microwave Radiometer ◽  
Vertical Motion ◽  
Tropical Rainfall Measuring Mission ◽  
Moisture Flux ◽  
Passive Microwave ◽  
Ancillary Data ◽  
Orographic Rainfall ◽  
Microwave Imager ◽  
Trmm Microwave Imager

AbstractHeavy rainfall associated with shallow orographic rainfall systems has been underestimated by passive microwave radiometer algorithms owing to weak ice scattering signatures. The authors improve the performance of estimates made using a passive microwave radiometer algorithm, the Global Satellite Mapping of Precipitation (GSMaP) algorithm, from data obtained by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) for orographic heavy rainfall. An orographic/nonorographic rainfall classification scheme is developed on the basis of orographically forced upward vertical motion and the convergence of surface moisture flux estimated from ancillary data. Lookup tables derived from orographic precipitation profiles are used to estimate rainfall for an orographic rainfall pixel, whereas those derived from original precipitation profiles are used to estimate rainfall for a nonorographic rainfall pixel. Rainfall estimates made using the revised GSMaP algorithm are in better agreement with estimates from data obtained by the radar on the TRMM satellite and by gauge-calibrated ground radars than are estimates made using the original GSMaP algorithm.

scholarly journals Simulating Passive Microwave Radiometer Designs Using Simulink

SIMULATION ◽  
2002 ◽  
Vol 78 (1) ◽  
pp. 36-55 ◽  
Author(s):  
Derek M. Burrage ◽  
Mark A. Goodberlet ◽  
Malcolm L. Heron
Keyword(s):  
Microwave Radiometer ◽  
Passive Microwave

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

2017 ◽  
Vol 21 (6) ◽  
pp. 2685-2700 ◽  
Author(s):  
Zeinab Takbiri ◽  
Ardeshir M. Ebtehaj ◽  
Efi Foufoula-Georgiou
Keyword(s):  
Rank Correlation ◽  
Nearest Neighbor Search ◽  
Water Levels ◽  
Passive Microwave ◽  
Sensor Data ◽  
Convective Precipitation ◽  
Retrieval Algorithm ◽  
Inversion Method ◽  
Diurnal Variability ◽  
Microwave Imager

Abstract. 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.

scholarly journals Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

1993 ◽  
Vol 17 ◽  
pp. 131-136 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Carolyn J. Merry ◽  
Don J. Cavalieri
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Correlated Data ◽  
Data Sets ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Scatter Plots ◽  
Microwave Imager ◽  
Highly Correlated ◽  
The Antarctic

Spaceborne data are becoming sufficiently extensive spatially and sufficiently lengthy over time to provide important gauges of global change. There is a potentially long record of microwave brightness temperature from NASA's Scanning Multichannel Microwave Radiometer (SMMR), followed by the Navy's Special Sensor Microwave Imager (SSM/I). Thus it is natural to combine data from successive satellite programs into a single, long record. To do this, we compare brightness temperature data collected during the brief overlap period (7 July-20 August 1987) of SMMR and SSM/I. Only data collected over the Antarctic ice sheet are used to limit spatial and temporal complications associated with the open ocean and sea ice. Linear regressions are computed from scatter plots of complementary pairs of channels from each sensor revealing highly correlated data sets, supporting the argument that there are important relative calibration differences between the two instruments. The calibration scheme was applied to a set of average monthly brightness temperatures for a sector of East Antarctica.

scholarly journals Frequency and distribution of winter melt events from passive microwave satellite data in the pan-Arctic, 1988–2013

2016 ◽  
Author(s):  
Libo Wang ◽  
Peter Toose ◽  
Ross Brown ◽  
Chris Derksen
Keyword(s):  
Snow Cover ◽  
Microwave Radiometer ◽  
Surface Air Temperature ◽  
Arctic Region ◽  
Temporal Variations ◽  
Passive Microwave ◽  
The Arctic ◽  
Winter Period ◽  
Strongly Correlated ◽  
Air Temperatures

Abstract. This study presents an algorithm for detecting winter melt events in seasonal snow cover based on temporal variations in the brightness temperature difference between 19 and 37 GHz from satellite passive microwave measurements. An advantage of the passive microwave approach is that it is based on the physical presence of liquid water in the snowpack, which may not be the case with melt events inferred from surface air temperature data. The algorithm is validated using in situ observations from weather stations, snowpit surveys, and a surface-based passive microwave radiometer. The results of running the algorithm over the pan-Arctic region (north of 50º N) for the 1988–2013 period show that winter melt days are relatively rare averaging less than 7 melt days per winter over most areas, with higher numbers of melt days (around two weeks per winter) occurring in more temperate regions of the Arctic (e.g. central Quebec and Labrador, southern Alaska, and Scandinavia). The observed spatial pattern was similar to winter melt events inferred with surface air temperatures from ERA-interim and MERRA reanalysis datasets. There was little evidence of trends in winter melt frequency except decreases over northern Europe attributed to a shortening of the duration of the winter period. The frequency of winter melt events is shown to be strongly correlated to the duration of winter period. This must be taken into account when analyzing trends to avoid generating false increasing trends from shifts in the timing of the snow cover season.

Large area mapping of soil moisture using the ESTAR passive microwave radiometer in Washita'92

1995 ◽  
Vol 54 (1) ◽  
pp. 27-37 ◽  
Author(s):  
Thomas J. Jackson ◽  
David M. Le Vine ◽  
Calvin T. Swift ◽  
Thomas J. Schmugge ◽  
Frank R. Schiebe
Keyword(s):  
Soil Moisture ◽  
Microwave Radiometer ◽  
Passive Microwave ◽  
Large Area
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