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 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.

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.

scholarly journals Spectral Density of Cloud Liquid Water Content at High Frequencies

2001 ◽  
Vol 58 (5) ◽  
pp. 497-503 ◽  
Author(s):  
H. Gerber ◽  
J. B. Jensen ◽  
A. B. Davis ◽  
A. Marshak ◽  
W. J. Wiscombe
Keyword(s):  
Spectral Density ◽  
Water Content ◽  
Liquid Water ◽  
Liquid Water Content ◽  
Cloud Liquid Water ◽  
High Frequencies ◽  
Cloud Liquid Water Content

scholarly journals An Initial Assessment of the Surface Reference Technique Applied to Data from the Dual-Frequency Precipitation Radar (DPR) on the GPM Satellite

2015 ◽  
Vol 32 (12) ◽  
pp. 2281-2296 ◽  
Author(s):  
Robert Meneghini ◽  
Hyokyung Kim ◽  
Liang Liao ◽  
Jeffrey A. Jones ◽  
John M. Kwiatkowski
Keyword(s):  
Cross Section ◽  
Radar Data ◽  
Single Frequency ◽  
Initial Assessment ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Frequency Method ◽  
Ka Band ◽  
Spaceborne Radar ◽  
Ku Band

AbstractIt has long been recognized that path-integrated attenuation (PIA) can be used to improve precipitation estimates from high-frequency weather radar data. One approach that provides an estimate of this quantity from airborne or spaceborne radar data is the surface reference technique (SRT), which uses measurements of the surface cross section in the presence and absence of precipitation. Measurements from the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement (GPM) satellite afford the first opportunity to test the method for spaceborne radar data at Ka band as well as for the Ku-band–Ka-band combination.The study begins by reviewing the basis of the single- and dual-frequency SRT. As the performance of the method is closely tied to the behavior of the normalized radar cross section (NRCS or σ0) of the surface, the statistics of σ0 derived from DPR measurements are given as a function of incidence angle and frequency for ocean and land backgrounds over a 1-month period. Several independent estimates of the PIA, formed by means of different surface reference datasets, can be used to test the consistency of the method since, in the absence of error, the estimates should be identical. Along with theoretical considerations, the comparisons provide an initial assessment of the performance of the single- and dual-frequency SRT for the DPR. The study finds that the dual-frequency SRT can provide improvement in the accuracy of path attenuation estimates relative to the single-frequency method, particularly at Ku band.

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