scholarly journals Refinement of Surface Precipitation Estimates for the Dual-frequency Precipitation Radar on the GPM Core Observatory Using Near-Nadir Measurements

2021 ◽  
Author(s):  
Masafumi HIROSE ◽  
Shoichi SHIGE ◽  
Takuji KUBOTA ◽  
Fumie A. FURUZAWA ◽  
Haruya MINDA ◽  
...  
Keyword(s):  
Dual Frequency ◽  
Precipitation Radar ◽  
Surface Precipitation ◽  
Precipitation Estimates

scholarly journals Cloud Assumption of Precipitation Retrieval Algorithms for the Dual-Frequency Precipitation Radar

2020 ◽  
Vol 37 (11) ◽  
pp. 2015-2031
Author(s):  
Takuji Kubota ◽  
Shinta Seto ◽  
Masaki Satoh ◽  
Tomoe Nasuno ◽  
Toshio Iguchi ◽  
...  
Keyword(s):  
Liquid Water ◽  
Atmospheric Model ◽  
Liquid Water Content ◽  
Absolute Difference ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Cloud Liquid Water ◽  
Ka Band ◽  
Surface Precipitation ◽  
Precipitation Estimates

AbstractAn assumption related to clouds is one of uncertain factors in precipitation retrievals by the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory. While an attenuation due to cloud ice is negligibly small for Ku and Ka bands, attenuation by cloud liquid water is larger in the Ka band and estimating precipitation intensity with high accuracy from Ka-band observations can require developing a method to estimate the attenuation due to cloud liquid water content (CLWC). This paper describes a CLWC database used in the DPR level-2 algorithm for the GPM V06A product. In the algorithm, the CLWC value is assumed using the database with inputs of precipitation-related variables, temperature, and geolocation information. A calculation of the database was made using the 3.5-km-mesh global atmospheric simulation derived from the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) global cloud-system-resolving model. Impacts of current CLWC assumptions for surface precipitation estimates were evaluated by comparisons of precipitation retrieval results between default values and 0 mg m−3 of the CLWC. The impacts were quantified by the normalized mean absolute difference (NMAD) and the NMAD values showed 2.3% for the Ku, 9.9% for the Ka, and 6.5% for the dual-frequency algorithms in global averages, while they were larger in the tropics than in high latitudes. Effects of the precipitation estimates from the CLWC assumption were examined further in terms of retrieval processes affected by the CLWC assumption. This study emphasizes the CLWC assumption provided more effects on the precipitation estimates through estimating path-integrated attenuation due to rain.

scholarly journals Adapting Passive Microwave-Based Precipitation Algorithms to Variable Microwave Land Surface Emissivity to Improve Precipitation Estimation from the GPM Constellation

2021 ◽  
Author(s):  
F. Joseph Turk ◽  
Sarah E. Ringerud ◽  
Yalei You ◽  
Andrea Camplani ◽  
Daniele Casella ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Passive Microwave ◽  
Earth Surface ◽  
Surface Emissivity ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Near Surface ◽  
Surface Precipitation ◽  
Precipitation Measurement

AbstractA fully global satellite-based precipitation estimate that can transition across changing Earth surface and complex land/water conditions is an important capability for many hydrological applications, and for independent evaluation of the precipitation derived from weather and climate models. This capability is inherently challenging owing to the complexity of the surface geophysical properties upon which the satellite-based instruments view. To date, these satellite observations originate primarily from a variety of wide-swath passive microwave (MW) imagers and sounders. In contrast to open ocean and large water bodies, the surface emissivity contribution to passive MW measurements is much more variable for land surfaces, with varying sensitivities to near-surface precipitation. The NASA/JAXA Global Precipitation Measurement (GPM) spacecraft (2014-current) is equipped with a dual-frequency precipitation radar and a multichannel passive MW imaging radiometer specifically designed for precipitation measurement, covering substantially more land area than its predecessor Tropical Rainfall Measuring Mission (TRMM). The synergy between GPM’s instruments has guided a number of new frameworks for passive MW precipitation retrieval algorithms, whereby the information carried by the single narrow-swath precipitation radar is exploited to recover precipitation from a disparate constellation of passive MW imagers and sounders. With over six years of increased land surface coverage provided by GPM, new insight has been gained into the nature of the microwave surface emissivity over land and ice/snow covered surfaces, leading to improvements in a number of physical and semi-physical based precipitation retrieval techniques that adapt to variable Earth surface conditions. In this manuscript, the workings and capabilities of several of these approaches are highlighted.

scholarly journals Evaluation of GPM-DPR precipitation estimates with WegenerNet gauge data

2019 ◽  
Vol 12 (9) ◽  
pp. 5055-5070 ◽  
Author(s):  
Martin Lasser ◽  
Sungmin O ◽  
Ulrich Foelsche
Keyword(s):  
Rainfall Variability ◽  
Radar Data ◽  
General Level ◽  
Dual Frequency ◽  
Ka Band ◽  
Surface Precipitation ◽  
Precipitation Estimates ◽  
Radar Rain Rate ◽  
Rain Rates ◽  
Ku Band

Abstract. The core satellite of the Global Precipitation Measurement (GPM) mission provides precipitation observations measured with the Dual-frequency Precipitation Radar (DPR). The precipitation can only be estimated from the radar data, and therefore independent validations using direct precipitation measurements on the ground as a true reference need to be performed. Moreover, the quality and the accuracy of satellite observational data depend on various influencing factors, such as altitude, topography and rainfall type. In this way, a validation may help to minimise those uncertainties. The DPR Level 2 algorithms provide three different sets of radar rain rate estimates: Ku-band-only rain rates, Ka-band-only rain rates, and a product using both the Ku and Ka band. This study presents an evaluation of the three GPM-DPR surface precipitation estimates based on the gridded precipitation data of the WegenerNet, a local-scale terrestrial network of 153 meteorological stations in southeastern Austria. The validation is based on graphical and statistical approaches, using only data where both Ku- and Ka-band measurements are available. The focus lies on the resemblance of the rainfall variability within the whole network and the over- and underestimation of the precipitation through the GPM-DPR. The analysis rests upon 15 rainfall events observed by the GPM-DPR over the WegenerNet in the last 4 years; the meteorological winter is excluded due to technical challenges of snow measurements. The WegenerNet provides between 8 and 12 gauges within each GPM-DPR footprint. Its biases are well studied and corrected; thus, it can be taken as a robust ground reference. This work also includes considerations on the limits of such comparisons between small terrestrial networks with a high density of stations and precipitation observations from a satellite. Our results show that the GPM-DPR estimates basically match with the WegenerNet measurements, but absolute quantities are biased. The three types of radar estimates deliver similar results, where Ku-band and dual-frequency estimates are very close to each other. On a general level, Ka-band precipitation estimates deliver better results due to their greater sensitivity to low rainfall rates.

Global Multiscale Evaluation of Satellite Passive Microwave Retrieval of Precipitation during the TRMM and GPM Eras: Effective Resolution and Regional Diagnostics for Future Algorithm Development

2017 ◽  
Vol 18 (11) ◽  
pp. 3051-3070 ◽  
Author(s):  
Clément Guilloteau ◽  
Efi Foufoula-Georgiou ◽  
Christian D. Kummerow
Keyword(s):  
Spatial Variability ◽  
Continuous Improvement ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
International Agreement ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Surface Precipitation ◽  
Precipitation Measurement ◽  
Microwave Imager

Abstract The constellation of spaceborne passive microwave (MW) sensors, coordinated under the framework of the Precipitation Measurement Missions international agreement, continuously produces observations of clouds and precipitation all over the globe. The Goddard profiling algorithm (GPROF) is designed to infer the instantaneous surface precipitation rate from the measured MW radiances. The last version of the algorithm (GPROF-2014)—the product of more than 20 years of algorithmic development, validation, and improvement—is currently used to estimate precipitation rates from the microwave imager GMI on board the GPM core satellite. The previous version of the algorithm (GPROF-2010) was used with the microwave imager TMI on board TRMM. In this paper, TMI-GPROF-2010 estimates and GMI-GPROF-2014 estimates are compared with coincident active measurements from the Precipitation Radar on board TRMM and the Dual-Frequency Precipitation Radar on board GPM, considered as reference products. The objective is to assess the improvement of the GPM-era microwave estimates relative to the TRMM-era estimates and diagnose regions where continuous improvement is needed. The assessment is oriented toward estimating the “effective resolution” of the MW estimates, that is, the finest scale at which the retrieval is able to accurately reproduce the spatial variability of precipitation. A wavelet-based multiscale decomposition of the radar and passive microwave precipitation fields is used to formally define and assess the effective resolution. It is found that the GPM-era MW retrieval can resolve finer-scale spatial variability over oceans than the TRMM-era retrieval. Over land, significant challenges exist, and this analysis provides useful diagnostics and a benchmark against which future retrieval algorithm improvement can be assessed.

scholarly journals Evaluation of GPM-DPR precipitation estimates with WegenerNet gauge data

2018 ◽  
Author(s):  
Martin Lasser ◽  
Sungmin O ◽  
Ulrich Foelsche
Keyword(s):  
Radar Data ◽  
General Level ◽  
Dual Frequency ◽  
Rainfall Events ◽  
Ka Band ◽  
Surface Precipitation ◽  
Precipitation Estimates ◽  
Radar Rain Rate ◽  
Rain Rates ◽  
Ku Band

Abstract. The core satellite of the Global Precipitation Measurement (GPM) mission provides precipitation observations measured with the Dual frequency Precipitation Radar (DPR). The precipitation can only be estimated from the radar data, and therefore, independent validations using direct precipitation observation on the ground as a true reference need to be performed. Moreover, the quality and the accuracy of the measurements depend on various influencing factors. In this way, a validation may help to minimise those uncertainties. The DPR provides three different radar rain rate estimates for the GPM core satellite: Ku-band-only rain rates, Ka-band-only rain rates and a product combining the two frequencies. This study presents an evaluation of the three GPM-DPR surface precipitation estimates based on the gridded precipitation data of the WegenerNet, a local scale terrestrial network of 153 meteorological stations in southeast Austria. The validation is based on a graphical and a statistical approach using only data where both Ku- and Ka-band measurements are available. The data delivered from the WegenerNet are gauge-based gridded rainfall observations; the meteorological winter is excluded due to technical reasons. The focus lies on the resemblance of the variability within the whole network and the over- and underestimation of the precipitation through the GPM-DPR. During the last four years 22 rainfall events were observed by the GPM-DPR over the WegenerNet and the analysis rests upon these rainfall events. The WegenerNet provides a large number of gauges within each GPM-DPR footprint. Its biases are well studied and corrected, thus, it can be taken as a robust ground reference. This work also includes considerations on the limits of such comparisons between small terrestrial networks with a high density of stations and precipitation observations from a satellite. Our results show that the GPM-DPR estimates basically match with the WegenerNet measurements, but absolute quantities are biased. The three types of radar estimates deliver similar results, where Ku-band and dual frequency estimates are very close to each other. On a general level, Ka-band precipitation estimates deliver the best results due to the high number of light rainfall events.

Evaluation of GPM Dual-Frequency Precipitation Radar Algorithms to Estimate Drop Size Distribution Parameters, Using Ground-Based Measurement over the Central Andes of Peru

2021 ◽  
Author(s):  
Carlos Del Castillo-Velarde ◽  
Shailendra Kumar ◽  
Jairo M. Valdivia-Prado ◽  
Aldo S. Moya-Álvarez ◽  
Jose Luis Flores-Rojas ◽  
...  
Keyword(s):  
Size Distribution ◽  
Drop Size ◽  
Central Andes ◽  
Drop Size Distribution ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Distribution Parameters

Remote sensing of the sea waves by the dual-frequency precipitation radar: First results

2015 ◽  
Author(s):  
V.Yu. Karaev ◽  
M.A. Panfilova ◽  
Yu.A. Titchenko ◽  
Eu.M. Meshkov ◽  
G.N. Balandina
Keyword(s):  
Remote Sensing ◽  
Sea Waves ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
First Results

Vertical precipitation profiles estimated by satellite-based passive microwave retrievals

2021 ◽  
Author(s):  
Nobuyuki Utsumi ◽  
F. Joseph Turk ◽  
Ziad. S. Haddad ◽  
Pierre-Emmanuel Kirstetter ◽  
Hyungjun Kim
Keyword(s):  
Water Content ◽  
Passive Microwave ◽  
Precipitation Rate ◽  
Vertical Profiles ◽  
High Latitudes ◽  
Surface Precipitation ◽  
Profile Information ◽  
Precipitation Estimates ◽  
Condensed Water ◽  
Global Precipitation

<p>Passive microwave (MW) observation from low Earth-orbiting satellites is one of the major sources of information for global precipitation monitoring. Although various precipitation retrieval techniques based on passive MW observation have been developed, most of them focus on estimating precipitation rate at near surface height. Vertical profile information of precipitation is meaningful for process-based understanding of precipitation systems. Also, a previous study found that the use of the vertical precipitation profile information can improve sub-hourly surface precipitation estimates (Utsumi et al., 2019).</p><p>This study investigates the precipitation vertical profiles estimated by two passive MW algorithms, i.e., the Emissivity Principal Components (EPC) algorithm developed by authors (Turk et al., 2018; Utsumi et al., 2021) and the Goddard Profiling Algorithm (GPROF). The vertical profiles of condensed water content estimated by the two passive MW algorithms for the Global Precipitation Measurement Microwave Imager (GMI) observations are validation with the GMI + Dual-frequency Precipitation Radar combined algorithm (CMB) for June 2014 – May 2015. The condensed water content profiles estimated by the passive MW algorithms show biases in their magnitude (i.e., EPC underestimates the magnitude by 20 – 50% in the middle-to-high latitudes; GPROF overestimates the magnitude by 20 – 50% in the middle-to-high latitudes and more than 50% overestimation in the tropics). On the other hand, the shapes of the profiles are reproduced well by the passive MW algorithms. The relationship between the estimation performances of surface precipitation rate and vertical profiles are also investigated. It is shown that the error in the profile magnitude shows a clear positive relationship with the surface precipitation error. The estimation performance of the profile shapes also shows connection with the surface precipitation error. This result indicates that physically reasonable connections between the surface precipitation estimate and its associated profiles are achieved to some extent by the passive MW algorithms. This also implies that properly constraining physical parameters of the precipitation profiles would lead to the improvements of the surface precipitation estimates.</p><p>References</p><p>Utsumi, N., Kim, H., Turk, F. J., & Haddad, Ziad. S. (2019). Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information. Journal of Hydrometeorology, 20(5), 1015–1026.</p><p>Turk, F. J., Haddad, Z. S., Kirstetter, P.-E., You, Y., & Ringerud, S. (2018). An observationally based method for stratifying a priori passive microwave observations in a Bayesian-based precipitation retrieval framework. Quarterly Journal of the Royal Meteorological Society, 144(S1), 145–164.</p><p>Utsumi, N., Turk, F. J., Haddad, Z. S., Kirstetter, P.-E., & Kim, H. (2021). Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals. Journal of Hydrometeorology, 22(1), 95–112.</p>

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