scholarly journals Intercalibration of the GPM Microwave Radiometer Constellation

2016 ◽  
Vol 33 (12) ◽  
pp. 2639-2654 ◽  
Author(s):  
Wesley Berg ◽  
Stephen Bilanow ◽  
Ruiyao Chen ◽  
Saswati Datta ◽  
David Draper ◽  
...  

AbstractThe Global Precipitation Measurement (GPM) mission is a constellation-based satellite mission designed to unify and advance precipitation measurements using both research and operational microwave sensors. This requires consistency in the input brightness temperatures (Tb), which is accomplished by intercalibrating the constellation radiometers using the GPM Microwave Imager (GMI) as the calibration reference. The first step in intercalibrating the sensors involves prescreening the sensor Tb to identify and correct for calibration biases across the scan or along the orbit path. Next, multiple techniques developed by teams within the GPM Intersatellite Calibration Working Group (XCAL) are used to adjust the calibrations of the constellation radiometers to be consistent with GMI. Comparing results from multiple approaches helps identify flaws or limitations of a given technique, increase confidence in the results, and provide a measure of the residual uncertainty. The original calibration differences relative to GMI are generally within 2–3 K for channels below 92 GHz, although AMSR2 exhibits larger differences that vary with scene temperature. SSMIS calibration differences also vary with scene temperature but to a lesser degree. For SSMIS channels above 150 GHz, the differences are generally within ~2 K with the exception of SSMIS on board DMSP F19, which ranges from 7 to 11 K colder than GMI depending on frequency. The calibrations of the cross-track radiometers agree very well with GMI with values mostly within 0.5 K for the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) and the Microwave Humidity Sounder (MHS) sensors, and within 1 K for the Advanced Technology Microwave Sounder (ATMS).

Author(s):  
Yalei You ◽  
Christa Peters-Lidard ◽  
S. Joseph Munchak ◽  
Jackson Tan ◽  
Scott Braun ◽  
...  

AbstractPrevious studies showed that conical scanning radiometers greatly outperform cross-track scanning radiometers for precipitation retrieval over ocean. This study demonstrates a novel approach to improve precipitation rates at the cross-track scanning radiometers’ observation time by propagating the conical scanning radiometers’ retrievals to the cross-track scanning radiometers’ observation time. The improved precipitation rate is a weighted average of original cross-track radiometers’ retrievals and retrievals propagated from a conical scanning radiometer. The cross-track scanning radiometers include the Advanced Technology Microwave Sounder (ATMS) onboard the NPP satellite and four Microwave Humidity Sounders (MHSs). The conical scanning radiometers include the Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs), while the precipitation retrievals from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) are taken as the reference. Results show that the morphed precipitation rates agree much better with the reference. The degree of improvement depends on several factors, including the propagated precipitation source, the time interval between the cross-track scanning radiometer and the conical scanning radiometer, the precipitation type (convective vs. stratiform), the precipitation events’ size, and the geolocation. The study has potential to greatly improve high-impact weather systems monitoring (e.g., hurricanes) and multi-satellite precipitation products. It may also enhance the usefulness of future satellite missions with cross-track scanning radiometers onboard.


2017 ◽  
Vol 34 (8) ◽  
pp. 1693-1711 ◽  
Author(s):  
H. Dong ◽  
X. Zou

AbstractThe Global Precipitation Measurement (GPM) Microwave Imager (GMI) plays an important role in monitoring global precipitation. In this study, an along-track striping noise is found in GMI observations of brightness temperatures for the two highest-frequency channels—12 and 13—with their central frequencies centered at 183.31 GHz. These two channels are designed for sounding the water vapor in the middle and upper troposphere. The pitch maneuver data of deep space confirmed an existence of striping noise in channels 12 and 13. A striping noise mitigation method is used for extracting the striping noise from the earth scene or deep space measurements of brightness temperatures by combining the principle component analysis (PCA) with the ensemble empirical mode decomposition (EEMD) method. A power spectrum density analysis indicated that the frequency of striping noise ranges between 0.06 and 0.533 s−1, where the right bound of 0.533 s−1 of frequency is exactly the inverse of the time (i.e., 1.875 s) it takes for the GMI to complete one conical scan line. The magnitude of striping noise in the brightness temperature observations of GMI channels 12 and 13 is about ±0.3 K. It is shown that after striping noise mitigation, the observation minus model simulation (O − B) distributions of both the earth scene and deep space brightness temperatures show no visible striping features.


2020 ◽  
Author(s):  
Christian Kummerow ◽  
Paula Brown

<p>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 May of 2017, GPM released Version 5 of its precipitation products starting with GMI and continuing with the constellation of radiometers.  The precipitation products from these sensors are consistent by design and show relatively minor differences in the mean global sense.  Since this release, the Combined Algorithm hydrometeor profiles have shown good consistency with surface observations and computed brightness temperatures agree reasonably well with GMI observations in precipitating regions.  The same is true for MIRS profiles in non-precipitating regions.  Version 7 of the GPROF code will therefore make use of these operational products to construct it's a-priori databases.  This will allow continuous improvements in the a-priori database as these operational products are reprocessed with newer versions, while allowing the user community to better focus on the algorithm’s error covariance matrix and its validation.  Results from early versions of this algorithm will be presented.  In addition to creating an a-priori database that can be more directly updated with improvement to the raining and non-raining scenes, GPROF is also undertaking steps to improve the orographic representation of snow and a Neural Network based Convective/Stratiform classification of precipitation that will both help improve instantaneous correlations with in-situ observations.</p>


2013 ◽  
Vol 51 (3) ◽  
pp. 1465-1477 ◽  
Author(s):  
Sayak K. Biswas ◽  
Spencer Farrar ◽  
Kaushik Gopalan ◽  
Andrea Santos-Garcia ◽  
W. Linwood Jones ◽  
...  

2016 ◽  
Vol 33 (8) ◽  
pp. 1649-1671 ◽  
Author(s):  
Eun-Kyoung Seo ◽  
Sung-Dae Yang ◽  
Mircea Grecu ◽  
Geun-Hyeok Ryu ◽  
Guosheng Liu ◽  
...  

AbstractUsing Tropical Rainfall Measuring Mission (TRMM) observations from storms collected over the oceans surrounding East Asia, during summer, a method of creating physically consistent cloud-radiation databases to support satellite radiometer retrievals is introduced. In this method, vertical profiles of numerical model-simulated cloud and precipitation fields are optimized against TRMM radar and radiometer observations using a hybrid empirical orthogonal function (EOF)–one-dimensional variational (1DVAR) approach.The optimization is based on comparing simulated to observed radar reflectivity profiles and the corresponding passive microwave observations at the frequencies of the TRMM Microwave Imager (TMI) instrument. To minimize the discrepancies between the actual and the synthetic observations, the simulated cloud and precipitation profiles are optimized by adjusting the contents of the hydrometeors. To reduce the dimension of the hydrometeor content profiles in the optimization, multivariate relations among hydrometeor species are used.After applying the optimization method to modify the simulated clouds, the optimized cloud-radiation database has a joint distribution of reflectivity and associated brightness temperatures that is considerably closer to that observed by TRMM PR and TMI, especially at 85 GHz. This implies that the EOF–1DVAR approach can generate profiles with realistic distributions of frozen hydrometeors, such as snow and graupel. This approach may be similarly adapted to operate with the variety and capabilities of the passive microwave radiometers that compose the Global Precipitation Measurement (GPM) constellation. Furthermore, it can be extended to other oceanic regions and seasons.


2005 ◽  
Vol 22 (7) ◽  
pp. 909-929 ◽  
Author(s):  
Hirohiko Masunaga ◽  
Christian D. Kummerow

Abstract A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.


2020 ◽  
Vol 59 (7) ◽  
pp. 1195-1215
Author(s):  
Ruiyao Chen ◽  
Ralf Bennartz

AbstractThe sensitivity of microwave brightness temperatures (TBs) to hydrometeors at frequencies between 89 and 190 GHz is investigated by comparing Fengyun-3C (FY-3C) Microwave Humidity Sounder-2 (MWHS-2) measurements with radar reflectivity profiles and retrieved products from the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (DPR). Scattering-induced TB depressions (ΔTBs), calculated by subtracting simulated cloud-free TBs from bias-corrected observed TBs for each channel, are compared with DPR-retrieved hydrometeor water path (HWP) and vertically integrated radar reflectivity ZINT. We also account for the number of hydrometeors actually visible in each MWHS-2 channel by weighting HWP with the channel’s cloud-free gas transmission profile and the observation slant path. We denote these transmission-weighted, slant-path-integrated quantities with a superscript asterisk (e.g., HWP*). The so-derived linear sensitivity of ΔTB with respect to HWP* increases with frequency roughly to the power of 1.78. A retrieved HWP* of 1 kg m−2 at 89 GHz on average corresponds to a decrease in observed TB, relative to a cloud-free background, of 11 K. At 183 GHz, the decrease is about 34–53 K. We perform a similar analysis using the vertically integrated, transmission-weighted slant-path radar reflectivity and find that ΔTB also decreases approximately linearly with . The exponent of 0.58 corresponds to the one we find in the purely DPR-retrieval-based ZINT–HWP relation. The observed sensitivities of ΔTB with respect to and HWP* allow for the validation of hydrometeor scattering models.


Author(s):  
David W. Draper ◽  
David A. Newell ◽  
Frank J. Wentz ◽  
Sergey Krimchansky ◽  
Gail M. Skofronick-Jackson

2020 ◽  
Author(s):  
Steven Chan

<p>In recent decades, passive microwave remote sensing at lower frequencies (1-10 GHz) has become a primary means to routinely monitor soil moisture on a global scale. Despite the success of a number of L- and C/X-band radiometers independently developed and launched by various government agencies over the last two decades, there has not been a concerted effort to leverage the combined brightness temperature (T<sub>B</sub>) observations from these instruments to derive an integrated soil moisture data record within a consistent geophysical inversion framework. The availability of such a consistent data record would provide critical insights into the dynamics of surface hydrological processes, including anomaly detection, interannual variability, and monitoring of the onset and evolution of long-term spatial and temporal variability due to natural or anthropogenic changes in land surface conditions.</p><p>Recent advances in T<sub>B</sub> intercalibration on current and historical satellites have resulted in the availability of consistent T<sub>B</sub> observations that extend from years to decades. For passive microwave remote sensing of soil moisture, satellite intercalibration undertaken by the Global Precipitation Measurement (GPM) mission [1-2] has resulted in a decadal repository of intercalibrated T<sub>B</sub> observations at X-band (10.7 GHz) frequencies from GMI (2014-present), AMSR2 (2012-present), WindSat (2003-present), TMI (1997-2015) and AMSR-E (2002-2011). Likewise, recent studies on relative calibration by SMOS (2009-present) and SMAP (2015-present) teams have also enabled the production of a similar repository of intercalibrated T<sub>B</sub> observations for soil moisture estimation at L-band (1.41 GHz) frequencies [3]. When used as inputs to a common geophysical inversion model, these T<sub>B</sub> observations can be used for soil moisture estimation. Because consistency has been reinforced at the level of T<sub>B</sub> observations among satellites, the resulting record of soil moisture retrieval is expected to exhibit the same internal consistency. Together, therefore, these T<sub>B</sub> repositories provide the foundation for the development of current and historical consistent soil moisture data records with more frequent and wider coverage than any single satellite can achieve alone.</p><p>In this presentation, we will describe a NASA-funded initiative [4] (MEaSUREs: Making Earth System Data Records for use in Research Environments) to create a consistent soil moisture decadal data record from multiple satellites for terrestrial hydrological applications. Preliminary results, ancillary data preparation, product delivery schedule, and deliverables of this initiative will be discussed in this presentation.</p><p>References:</p><ol><li>Berg, W., S. Bilanow, R. Chen, S. Datta, D. Draper, H. Ebrahimi, S. Farrar, W. Jones, R. Kroodsma, D. McKague, V. Payne, J. Wang, T. Wilheit, and J. Yang. 2016. “Intercalibration of the GPM Microwave Radiometer Constellation,” J. Atmos. Oceanic Technol., 33, pp. 2639–2654, doi: 10.1175/JTECH-D-16-0100.1.</li> <li>Biswas, S. K., S. Farrar, K. Gopalan, A. Santos-Garcia, W. L. Jones and S. Bilanow. 2013. “Intercalibration of Microwave Radiometer Brightness Temperatures for the Global Precipitation Measurement Mission,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 3, pp. 1465–1477. doi: 10.1109/TGRS.2012.2217148.</li> <li>Bindlish, R., S. Chan, T. Jackson, A. Colliander, and Y. Kerr. 2018. “Integration of SMAP and SMOS Observations,” 2018 IEEE IGARSS, Valencia, Spain.</li> <li>"MEaSUREs: Making Earth System Data Records for Use in Research Environments," Accessed Nov 8, 2018. [Online]. Available: https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects</li> </ol>


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