Satoshi Kida

Improvement of TMI Rain Retrievals in Mountainous Areas

AbstractHeavy rainfall associated with shallow orographic rainfall systems has been underestimated by passive microwave radiometer algorithms owing to weak ice scattering signatures. The authors improve the performance of estimates made using a passive microwave radiometer algorithm, the Global Satellite Mapping of Precipitation (GSMaP) algorithm, from data obtained by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) for orographic heavy rainfall. An orographic/nonorographic rainfall classification scheme is developed on the basis of orographically forced upward vertical motion and the convergence of surface moisture flux estimated from ancillary data. Lookup tables derived from orographic precipitation profiles are used to estimate rainfall for an orographic rainfall pixel, whereas those derived from original precipitation profiles are used to estimate rainfall for a nonorographic rainfall pixel. Rainfall estimates made using the revised GSMaP algorithm are in better agreement with e...

Improvement of High-Resolution Satellite Rainfall Product for Typhoon Morakot (2009) over Taiwan

The authors improve the high-resolution Global Satellite Mapping of Precipitation (GSMaP) product for Typhoon Morakot (2009) over Taiwan by using an orographic/nonorographic rainfall classification scheme. For the estimation of the orographically forced upward motion used in the orographic/nonorographic rainfall classification scheme, the optimal horizontal length scale for averaging the elevation data is examined and found to be about 50km. It is inferred that as the air ascends en masse on the horizontal scale, it becomes unstableandconvectiondevelops.Theorographic/nonorographic rainfallclassification schemeis extendedto the GSMaP algorithm for all passive microwave radiometers in orbit, including not just microwave imagers butalsomicrowavesounders. Theretrievedrainfallrates,togetherwithinfraredimages,areusedforthehighresolution rainfall products, which leads to much better agreement with rain gauge observations.

The GSMaP Precipitation Retrieval Algorithm for Microwave Sounders—Part I: Over-Ocean Algorithm

We develop an over-ocean rainfall retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) based on the Global Satellite Mapping of Precipitation (GSMaP) microwave radiometer algorithm. This algorithm combines an emission-based estimate from brightness temperature (Tb) at 23 GHz and a scattering-based estimate from Tb at 89 GHz, depending on a scattering index (SI) computed from Tb at both 89 and 150 GHz. Precipitation inhomogeneities are also taken into account. The GSMaP-retrieved rainfall from the AMSU (GSMaP_AMSU) is compared with the National Oceanic and Atmospheric Administration (NOAA) standard algorithm (NOAA_AMSU)-retrieved data using Tropical Rainfall Measuring Mission (TRMM) data as a reference. Rain rates retrieved by GSMaP_AMSU have better agreement with TRMM estimates over midlatitudes during winter. Better estimates over multitudes over winter are given by the use of Tb at 23 GHz in the GSMaP_AMSU algorithm. It was also shown that GSMaP_AMSU has higher rain detection than NOAA_AMSU.

Evaluation of Precipitation Estimates by at-Launch Codes of GPM/DPR Algorithms Using Synthetic Data from TRMM/PR Observations

The Global Precipitation Measurement (GPM) Core Observatory will carry a Dual-frequency Precipitation Radar (DPR) consisting of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). In this study, “at-launch” codes of DPR precipitation algorithms, which will be used in GPM ground systems at launch, were evaluated using synthetic data based upon the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. Results from the codes (Version 4.20131010) of the KuPR-only, KaPR-only, and DPR algorithms were compared with “true values” calculated based upon drop size distributions assumed in the synthetic data and standard results from the TRMM algorithms at an altitude of 2 km over the ocean. The results indicate that the total precipitation amounts during April 2011 from the KuPR and DPR algorithms are similar to the true values, whereas the estimates from the KaPR data are underestimated. Moreover, the DPR estimates yielded smaller precipitation rates for rates less than about 10 mm/h and greater precipitation rates above 10 mm/h. Underestimation of the KaPR estimates was analyzed in terms of measured radar reflectivity (Zm) of the KaPR at an altitude of 2 km. The underestimation of the KaPR data was most pronounced during strong precipitation events of Zm <; 18 dBZ (high attenuation cases) over heavy precipitation areas in the Tropics, whereas the underestimation was less pronounced when the Zm > 26 (moderate attenuation cases). The results suggest that the underestimation is caused by a problem in the attenuation correction method, which was verified by the improved codes.

Vertical structure of raindrop size distribution in lower atmospheric boundary layer

[1] We examine the vertical structure of the raindrop size distribution (DSD) in the lower atmospheric boundary layer (ABL) below altitudes of 300 m, where conventional radars typically do not observe. The DSD in the lower ABL is retrieved using Ku-band broadband radar (BBR) having an observational range of 50 m to 15 km, and a high range resolution of several meters and a 3-dB beam width of 3 deg. The radar-retrieved DSD are in excellent agreement with the DSD measured with a co-located, 2 dimensional video disdrometer with correlation coefficients over 0.96 in both stratiform and convective rain events. While the DSD reveals no significant change in the stratiform event, the growth process increases about 2 times in the number of raindrops larger than 0.5 mm in diameter in the convective event. This growth process in the ABL is important when we discuss the rainfall rate from radar reflectivity factor.

Validation of rain/no-rain threshold value of cloud liquid water for microwave precipitation retrieval algorithm using CloudSat precipitation product

The rain/no-rain threshold value of cloud liquid water (CLW) is important for the microwave precipitation retrieval algorithms. In our previous study, we proposed a parameterization of rain/no-rain threshold value of CLW as a function of storm height for Global Satellite Mapping of Precipitation (GSMaP) algorithm. In this study, we determine rain/norain threshold value of CLW using CloudSat precipitation product and the cloud liquid water derived from Aqua/AMSRE. The threshold values of CLW from CloudSat precipitation product are lower than 0.5 kg m-2 for GSMaP over all regions. The threshold value of CLW is found at its peak in the Tropics and decreases poleward. The threshold value of cloud liquid water contents computed from threshold value of CLW divided by the zonal mean storm height from PR3A25 is employed on the parameterization of threshold value of CLW. The result shows that GSMaP with new parameterization can detect the shallow rain observed by CloudSat.

Development of precipitation retrieval algorithm over land for a satellite-borne microwave sounder

We develop the rain/no-rain classification (RNC) method for an over-land rainfall retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) based on the Global Satellite Mapping of Precipitation microwave radiometer algorithm. The current RNC method over land based on the window channel tends to underestimate warm rain areas due to high land surface emissivity. The sounder channels are less affected by surface emission. Therefore we developed a new RNC method using the sounder channels in order to detect the warm rain which is missed by the current method. The proposed methods are evaluated using the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. The proposed method can detect the warm rain which is missed by the original method and about 84% of the AMSU-PR matched-up cases during Jan.-Dec. 2007 is improved.

Reduction of discontinuity due to the orbit boost in TRMM Precipitation Radar product for climate studies

The Precipitation Radar (PR) on the TRMM satellite has observed tropical rainfall for more than 15 years. The long-term rainfall data from TRMM PR have been very useful for scientific studies. However, several studies have shown that the amount of weak convective rainfall decreased in the PR product due to the TRMM orbit boost from 350 km to 402.5 km in August 2001. In this study, the impact of the sensitivity degradation on the rainfall amounts is estimated by adjusting the pre-boost data with a simulation of reduced sensitivity by the orbit boost. A PR precipitation product with reduced artificial discontinuity at the timing of the orbit boost is produced for climate studies. The result shows that the decrease of the global rainfall amount due to the sensitivity is about 2.1% on average. However, the decreases of rainfall amount due to the sensitivity degradation vary spatially and they are larger, in particular, in subtropical ocean.

Development of synthetic GPM/DPR data from TRMM/PR and evaluation of GPM/DPR level-2 “at-launch” algorithms using them

The JAXA-NASA Joint Algorithm Team has developed the Level 2 (L2) algorithm for Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) core observatory, which provides estimated precipitation rate, radar reflectivity factor, and precipitation information. The synthetic DPR Level 1 (L1) data is necessary as a test bed of the DPR L2 algorithms. In this study, synthetic DPR L1 data estimated from the TRMM/PR data are produced in 7 orbits during 15th March 2007 and 32 orbits during 31st Mar. to 1st Apr. 2011. The at-launch codes of L2 Ku, L2 Ka, L2 DPR algorithms (Version 4.20130328) were applied to the L1B synthetic data. The Ku/Ka/DPR products are compared with the PR products. Precipitation rates at 2km altitudes of Ku-L2 estimates are similar to those of the PR 2A25 product. Correlation coefficients are 0.93 and 0.81 over ocean and land, respectively. On the other hand, underestimation in Ka-L2 estimates and overestimation in DPR-L2 estimates are found in precipitation rate more than 4mm/hr over ocean.

Development of precipitation retrieval algorithm for passive microwave sounder over land

Current version of the over-land Global Satellite Mapping of Precipitation (GSMaP) algorithm for microwave sounder tends to underestimate rain areas because of missing warm rain due to scattering-based algorithm only applied over land. Therefore we develop a new rain/no-rain classification (RNC) method using channels such as 89, 150, 186 and 190 GHz to detect the warm rain. In order to estimate the performance of the revised RNC method, the AMSU-PR matched-up cases are used. The result shows that the shallow precipitation over land, which is missed by the original RNC method, is detected by the revised RNC method.