scholarly journals Calibration of the Dual-Frequency Precipitation Radar Onboard the Global Precipitation Measurement Core Observatory

Author(s):  
Takeshi Masaki ◽  
Toshio Iguchi ◽  
Kaya Kanemaru ◽  
Kinji Furukawa ◽  
Naofumi Yoshida ◽  
...  
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.


2019 ◽  
Vol 58 (7) ◽  
pp. 1429-1448 ◽  
Author(s):  
Gail Skofronick-Jackson ◽  
Mark Kulie ◽  
Lisa Milani ◽  
Stephen J. Munchak ◽  
Norman B. Wood ◽  
...  

AbstractRetrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.


2020 ◽  
Author(s):  
Kinji Furukawa ◽  
Takuji Kubota ◽  
Moeka Yamaji ◽  
Tomoko Tashima ◽  
Yuki Kaneko ◽  
...  

<p>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 GPM Core Observatory, launched on February 2014, carries the Dual-frequency Precipitation Radar (DPR) by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT).</p><p>JAXA is continuing DPR data monitoring to confirm that DPR function and performance are kept on orbit. A scan pattern of the DPR was changed in May 2018. The next product applying the new scan pattern will be released as an experimental product (V06X) in 2020. The DPR follow-on mission has been actively discussed in Japan.</p><p>JAXA also develops the Global Satellite Mapping of Precipitation (GSMaP), as national product to distribute hourly and 0.1-degree horizontal resolution rainfall map. GSMaP has been used for various research fields and JAXA keeps it developed and improved, in cooperation with domestic/international partner agencies.</p><p>The GSMaP near-real-time version (GSMaP_NRT) product provides global rainfall map in 4-hour after observation, and recently GSMaP near-real-time gauge-adjusted version (GSMaP_Gauge_NRT) product has been published. The higher priority to data latency time than accuracy leads to wider utilization by various users for various purposes, such as rainfall monitoring, flood alert and warning, drought monitoring, crop yield forecast, and agricultural insurance.</p><p>Improved GSMaP_Gauge_NRT product (v6) was open to the public in Dec. 2018. Correction coefficients are calculated using past 30 days based upon Mega et al. (2019)’s method. We completed reprocessing of past 19yr data record (since Mar. 2000). Validations with reference to the JMA radar around Japan show smaller RMSEs in this new product than the current NRT (no gauge-correction).</p><p>JAXA started to provide the GSMaP real-time product called GSMaP_NOW by using the geostationary satellite Himawari-8 operated by the Japan Meteorological Agency (JMA) since November 2015. Recently, the domain of GSMaP_NOW was extended to the global region in June 2019. Furthermore, we developed the gauge-adjusted real-time version, GSMaP_Gauge_NOW, which was also released in June 2019. In the method, estimates from the GSMaP_NOW are adjusted using an optimization model (Mega et al. 2019) with parameters calculated from the GSMaP_Gauge (gauge-adjusted standard version) during the past 30 days.</p><p>GSMaP products can be seen via website and easy to monitor the global rainfall with good latency. GSMaP since March 2000 up to 4-hour after observation is available from the “JAXA Global Rainfall Watch” website (https://sharaku.eorc.jaxa.jp/GSMaP/index.htm); while GSMaP_NOW product is from the "JAXA Realtime Rainfall Watch" web site (https://sharaku.eorc.jaxa.jp/GSMaP_NOW/index.htm).</p>


2021 ◽  
Vol 13 (22) ◽  
pp. 4565
Author(s):  
Maria Panfilova ◽  
Vladimir Karaev

The algorithm to retrieve wind speed in a wide swath from the normalized radar cross section (NRCS) was developed for the data of Dual Frequency Precipitation Radar (DPR) operating in scanning mode installed onboard a Global Precipitation Measurement (GPM) satellite. The data for Ku-band radar were used. Equivalent NRCS values at nadir were estimated in a wide swath under the geometrical optics approximation from off-nadir observations. Using these equivalent NRCS nadir values and the sea buoys data, the new parameterization of dependence between NRCS at nadir and the wind speed was obtained. The algorithm was validated using ASCAT (Advanced Scatterometer) data and revealed good accuracy. DPR data are promising for determining wind speed in coastal areas.


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