Intercalibrated Passive Microwave Rain Products from the Unified Microwave Ocean Retrieval Algorithm (UMORA)

2008 ◽  
Vol 47 (3) ◽  
pp. 778-794 ◽  
Author(s):  
K. A. Hilburn ◽  
F. J. Wentz
Keyword(s):  
Spatial Patterns ◽  
Liquid Water ◽  
Rain Rate ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Global Precipitation Climatology Project ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Surface Rain Rate ◽  
Microwave Imager

Abstract The Unified Microwave Ocean Retrieval Algorithm (UMORA) simultaneously retrieves sea surface temperature, surface wind speed, columnar water vapor, columnar cloud water, and surface rain rate from a variety of passive microwave radiometers including the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The rain component of UMORA explicitly parameterizes the three physical processes governing passive microwave rain retrievals: the beamfilling effect, cloud and rainwater partitioning, and effective rain layer thickness. Rain retrievals from the previous version of UMORA disagreed among different sensors and were too high in the tropics. These issues have been fixed with more realistic rain column heights and proper modeling of saturation and footprint-resolution effects in the beamfilling correction. The purpose of this paper is to describe the rain algorithm and its recent improvements and to compare UMORA retrievals with Goddard Profiling Algorithm (GPROF) and Global Precipitation Climatology Project (GPCP) rain rates. On average, TMI retrievals from UMORA agree well with GPROF; however, large differences become apparent when the instantaneous retrievals are compared on a pixel-to-pixel basis. The differences are due to fundamental algorithm differences. For example, UMORA generally retrieves higher total liquid water, but GPROF retrieves a higher surface rain rate for a given amount of total liquid water because of differences in microphysical assumptions. Comparison of UMORA SSM/I retrievals with GPCP shows similar spatial patterns, but GPCP has higher global averages because of greater amounts of precipitation in the extratropics. UMORA and GPCP have similar linear trends over the period 1988–2005 with similar spatial patterns.

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.

A Coupled-Pixel Model (CPM) Atmospheric Retrieval Algorithm for High-Resolution Imagers

2015 ◽  
Vol 32 (10) ◽  
pp. 1866-1879 ◽  
Author(s):  
Mary Morris ◽  
Christopher S. Ruf
Keyword(s):  
Wind Speed ◽  
Rain Rate ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Low Frequency ◽  
Surface Wind Speed ◽  
Retrieval Algorithm ◽  
Single Column ◽  
Simplifying Assumptions ◽  
Cross Track

AbstractLow-frequency passive microwave observations allow for oceanic remote sensing of surface wind speed and rain rate from spaceborne and airborne platforms. For most instruments, the modeling of contributions of rain absorption and reemission in a particular field of view is simplified by the observing geometry. However, the simplifying assumptions that can be applied in most applications are not always valid for the scenes that the airborne Hurricane Imaging Radiometer (HIRAD) regularly observes. Collocated Stepped Frequency Microwave Radiometer (SFMR) and HIRAD observations of Hurricane Earl (2010) indicate that retrieval algorithms based on the usual simplified model, referred to here as the decoupled-pixel model (DPM), are not able to resolve two neighboring rainbands at the edge of HIRAD’s swath. The DPM does not allow for the possibility that a single column of atmosphere can affect the observations at multiple cross-track positions. This motivates the development of a coupled-pixel model (CPM) that is developed and tested in this paper. Simulated observations as well as HIRAD’s observations of Hurricane Earl (2010) are used to test the CPM algorithm. Key to the performance of the CPM algorithm is its ability to deconvolve the cross-track scene, as well as unscramble the signatures of surface wind speed and rain rate in HIRAD’s observations. While the CPM approach was developed specifically for HIRAD, other sensors could employ this method in similar complicated observing scenarios.

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 A Unique Satellite-Based Sea Surface Wind Speed Algorithm and Its Application in Tropical Cyclone Intensity Analysis

2016 ◽  
Vol 33 (7) ◽  
pp. 1363-1375 ◽  
Author(s):  
Sungwook Hong ◽  
Hwa-Jeong Seo ◽  
Young-Joo Kwon
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Japan Meteorological Agency ◽  
Retrieval Algorithm ◽  
Sea Surface Wind Speed ◽  
Brightness Temperatures ◽  
Sea Surface Wind ◽  
Medium Range

AbstractThis study proposes a sea surface wind speed retrieval algorithm (the Hong wind speed algorithm) for use in rainy and rain-free conditions. It uses a combination of satellite-observed microwave brightness temperatures, sea surface temperatures, and horizontally polarized surface reflectivities from the fast Radiative Transfer for TOVS (RTTOV), and surface and atmospheric profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF). Regression relationships between satellite-observed brightness temperature and satellite-simulated brightness temperatures, satellite-simulated brightness temperatures, rough surface reflectivities, and between sea surface roughness and sea surface wind speed are derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2). Validation results of sea surface wind speed between the proposed algorithm and the Tropical Atmosphere Ocean (TAO) data show that the estimated bias and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s−1, and −0.52 and 1.21 m s−1, respectively. Typhoon intensities such as the current intensity (CI) number, maximum wind speed, and minimum pressure level based on the proposed technique (the Hong technique) are compared with best-track data from the Japan Meteorological Agency (JMA), the Joint Typhoon Warning Center (JTWC), and the Cooperative Institute for Mesoscale Meteorological Studies (CIMSS) for 13 typhoons that occurred in the northeastern Pacific Ocean throughout 2012. Although the results show good agreement for low- and medium-range typhoon intensities, the discrepancy increases with typhoon intensity. Consequently, this study provides a useful retrieval algorithm for estimating sea surface wind speed, even during rainy conditions, and for analyzing characteristics of tropical cyclones.

Verification study of passive microwave snowfall products using ground-based radar network observations

2020 ◽  
Author(s):  
Mario Montopoli ◽  
Kamil Mroz ◽  
Giulia Panegrossi ◽  
Daniele Casella ◽  
Luca Baldini ◽  
...  
Keyword(s):  
Large Scale ◽  
Hydrological Cycle ◽  
Passive Microwave ◽  
Advanced Technology ◽  
Retrieval Algorithm ◽  
Satellite Mission ◽  
Surface Precipitation ◽  
Radar Network ◽  
Retrieval Algorithms ◽  
Microwave Imager

<p>Snowfall remote sensing  is becoming an increasingly popular topic within both the scientific community and operational services. Studies focused on snow retrievals are important because snow represents a reservoir of fresh water and its quantification is a crucial task to thoroughly understanding the hydrological cycle. In addition, snow-cover plays a key role in the climate system, modifying the global energy budget because of its high albedo. In addition, snowfalls often represent a hazard to several public services (e.g. transportations, energy providers) as well as properties (e.g. roof loading) but also an opportunity (e.g. for hydropower).</p><p>Passive microwave observations provided by currently operating spaceborne radiometers (e.g. Advanced Technology Microwave Sounder (ATMS), the Global Precipitation Measurement (GPM) Microwave Imager (GMI)) are a unique source of global information on the occurrence and the quantity of snowfall. However, because of the weaker and more complex signatures of snow at microwave frequencies [1] compared to those from rainfall, the retrieval schemes used by such instruments are still not fully optimised for snowfall detection and estimation, and subject to large errors. The ESA-funded RAINCAST project aims, among other tasks, at the verification of passive microwave snowfall products with the goal of fostering and defining new retrieval algorithms and mission concepts specifically optimised for snowfall quantification.</p><p>In this study we show a comparative analysis between passive microwave snowfall rate estimates and high quality ground-based radar snowfall measurements to quantify the actual strengths and limitations in state-of-the-art passive microwave snowfall products. In particular, the performance of the Goddard profiling algorithm version 5A (GPROF V5A) and of a recently developed snowfall retrieval algorithm for GMI named SLALOM [2, 3] are investigated. The differences between GPROF and SLALOM are explored in relation to the environmental conditions (including the presence of supercooled droplets aloft that tend to mask the typical snowfall signature) where the snowfall retrievals are likely less accurate. In addition, ATMS snowfall products are analysed as well for selected case studies to evidence the potential and limitations of the different snowfall products in relation to the algorithm’s design (e.g., GPROF vs. SLALOM) and sensor characteristics (GMI and ATMS). Then quantitative assessments for all products are discussed by exploiting one year of ground reference radar network data over Northern U.S. and Canada provided by the Multi-Radar/Multi-sensor System (MRMS) product, available at 1x1 km horizontal regularresolution and 2 min time sampling, and providing gauge adjusted surface precipitation rate together with the indication of its phase.</p><p>Our analysis confirms results from recent work on the same topic [e.g., 4], although a long term large scale analysis that quantify passive microwave retrieval is not found in the past literature. </p><p>This work is particularly relevant not only for the quantification of the limitations of the current snowfall retrieval algorithms, but also to give recommendations for algorithm development for upcoming satellite missions (e.g. EPS-SG MWS, MWI/ICI), and for future satellite mission concepts.</p><p><strong>REFERENCES</strong></p><p>[1] Liu, G. et al, 2008. doi:  10.1029/2007JD009766.</p><p>[2] Rysman, J.-F. et al., 2018. doi: 10.3390/rs10081278</p><p>[3] Rysman J.-F., et al., 2019. doi:10.1029/2019GL084576,</p><p>[4] Von Lerber, et al. doi: /10.1175/JAMC-D-17-0176.1</p><div> <div> <div> </div> </div> </div>

scholarly journals Satellite Microwave Surface Observations in Tropical Cyclones

2010 ◽  
Vol 138 (2) ◽  
pp. 421-437 ◽  
Author(s):  
Yves Quilfen ◽  
Bertrand Chapron ◽  
Jean Tournadre
Keyword(s):  
Wind Speed ◽  
Tropical Cyclones ◽  
Rain Rate ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Surface Wind Speed ◽  
Microwave Measurements ◽  
Dual Frequency

Abstract Sea surface estimates of local winds, waves, and rain-rate conditions are crucial to complement infrared/visible satellite images in estimating the strength of tropical cyclones (TCs). Satellite measurements at microwave frequencies are thus key elements of present and future observing systems. Available for more than 20 years, passive microwave measurements are very valuable but still suffer from insufficient resolution and poor wind vector retrievals in the rainy conditions encountered in and around tropical cyclones. Scatterometer and synthetic aperture radar active microwave measurements performed at the C and Ku band on board the European Remote Sensing (ERS), the Meteorological Operational (MetOp), the Quick Scatterometer (QuikSCAT), the Environmental Satellite (Envisat), and RadarSat satellites can also be used to map the surface wind field in storms. Their accuracy is limited in the case of heavy rain and possible saturation of the microwave signals is reported. Altimeter dual-frequency measurements have also been shown to provide along-track information related to surface wind speed, wave height, and vertically integrated rain rate at about 6-km resolution. Although limited for operational use by their dimensional sampling, the dual-frequency capability makes altimeters a unique satellite-borne sensor to perform measurements of key surface parameters in a consistent way. To illustrate this capability two Jason-1 altimeter passes over Hurricanes Isabel and Wilma are examined. The area of maximum TC intensity, as described by the National Hurricane Center and by the altimeter, is compared for these two cases. Altimeter surface wind speed and rainfall-rate observations are further compared with measurements performed by other remote sensors, namely, the Tropical Rainfall Measuring Mission instruments and the airborne Stepped Frequency Microwave Radiometer.

scholarly journals Retrieved wind speed from the Orbiting Carbon Observatory-2

2020 ◽  
Vol 13 (12) ◽  
pp. 6889-6899
Author(s):  
Robert R. Nelson ◽  
Annmarie Eldering ◽  
David Crisp ◽  
Aronne J. Merrelli ◽  
Christopher W. O'Dell
Keyword(s):  
Wind Speed ◽  
Near Infrared ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Retrieval Algorithm ◽  
Economic Sectors ◽  
Wind Speeds ◽  
Clear Sky ◽  
Potential Benefits ◽  
Microwave Sensors

Abstract. Satellite measurements of surface wind speed over the ocean inform a wide variety of scientific pursuits. While both active and passive microwave sensors are traditionally used to detect surface wind speed over water surfaces, measurements of reflected sunlight in the near-infrared made by the Orbiting Carbon Observatory-2 (OCO-2) are also sensitive to the wind speed. In this work, retrieved wind speeds from OCO-2 glint measurements are validated against the Advanced Microwave Scanning Radiometer-2 (AMSR2). Both sensors are in the international Afternoon Constellation (A-Train), allowing for a large number of co-located observations. Several different OCO-2 retrieval algorithm modifications are tested, with the most successful being a single-band Cox–Munk-only model. Using this, we find excellent agreement between the two sensors, with OCO-2 having a small mean bias against AMSR2 of −0.22 m s−1, an RMSD of 0.75 m s−1, and a correlation coefficient of 0.94. Although OCO-2 is restricted to clear-sky measurements, potential benefits of its higher spatial resolution relative to microwave instruments include the study of coastal wind processes, which may be able to inform certain economic sectors.

scholarly journals An experimental 2DVAR retrieval using AMSR2

2019 ◽  
Author(s):  
David Ian Duncan ◽  
Patrick Eriksson ◽  
Simon Pfreundschuh
Keyword(s):  
Wind Speed ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Surface Wind ◽  
Optimal Estimation ◽  
Surface Wind Speed ◽  
Model Errors ◽  
Near Surface ◽  
Noise Characteristics ◽  
Microwave Imager

Abstract. A two-dimensional variational retrieval (2DVAR) is presented for a passive microwave imager. The overlapping antenna patterns of all frequencies from the Advanced Microwave Scanning Radiometer-2 (AMSR2) are explicitly simulated to attempt retrieval of near surface wind speed and surface skin temperature at finer spatial scales than individual antenna beams. This is achieved, with the effective spatial resolution of retrieved parameters shown by analysis of 2DVAR averaging kernels. Sea surface temperature retrievals achieve about 30 km resolution, with wind speed retrievals at about 10 km resolution. It is argued that multi-dimensional optimal estimation permits greater use of total information content from microwave sensors than other methods, with no compromises on target resolution needed; instead, various targets are retrieved at the highest possible spatial resolution, driven by the channels' sensitivities. All AMSR2 channels can be simulated within near their published noise characteristics for observed clear-sky scenes, though calibration and emissivity model errors are key challenges. This experimental retrieval shows the feasibility of 2DVAR for cloud-free retrievals, and opens the possibility of standalone 3DVAR retrievals of water vapour and hydrometeor fields from microwave imagers in the future. The results have implications for future satellite missions and sensor design, as spatial oversampling can somewhat mitigate the need for larger antennas in the push for higher spatial resolution.

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