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In the first part of this paper, monthly precipitation data from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission 3B42 algorithm Version 7 (TRMM-3B42V7) are evaluated over Iran using the Generalized Three-Cornered Hat (GTCH) method which is self-sufficient of reference data as input. Climate Data Unit (CRU) is added to the GTCH evaluations as an independent gauge-based dataset thus, the minimum requirement of three datasets for the model is satisfied. To ensure consistency of all datasets, the two satellite products were aggregated to 0.5° spatial resolution, which is the minimum resolution of CRU. The results show that the PERSIANN-CDR has higher Signal to Noise Ratio (SNR) than TRMM-3B42V7 for the monthly rainfall estimation, especially in the northern half of the country. All datasets showed low SNR in the mountainous area of southwestern Iran, as well as the arid parts in the southeast region of the country. Additionally, in order to evaluate the efficacy of PERSIANN-CDR and TRMM-3B42V7 in capturing extreme daily-precipitation amounts, an in-situ rain-gauge dataset collected by the Islamic Republic of the Iran Meteorological Organization (IRIMO) was employed. Given the sparsity of the rain gauges, only 0.25° pixels containing three or more gauges were used for this evaluation. There were 228 such pixels where daily and extreme rainfall from PERSIANN-CDR and TRMM-3B42V7 could be compared. However, TRMM-3B42V7 overestimates most of the intensity indices (correlation coefficients; R between 0.7648–0.8311, Root Mean Square Error; RMSE between 3.29mm/day-21.2mm/5day); PERSIANN-CDR underestimates these extremes (R between 0.6349–0.7791 and RMSE between 3.59mm/day-30.56mm/5day). Both satellite products show higher correlation coefficients and lower RMSEs for the annual mean of consecutive dry spells than wet spells. The results show that TRMM-3B42V7 can capture the annual mean of the absolute indices (the number of wet days in which daily precipitation > 10 mm, 20 mm) better than PERSIANN-CDR. The results of daily evaluations show that the similarity of Empirical Cumulative Density Function (ECDF) of satellite products and IRIMO gauges daily precipitation, as well as dry spells with different thresholds in some selected pixels (include at least five gauges), are significant. The results also indicate that ECDFs become more significant when threshold increases. In terms of regional analyses, the higher SNR of the products on monthly (based on the GTCH method) and daily evaluations (significant ECDFs) is mostly consistent. [ABSTRACT FROM AUTHOR]

Abstract: Microwave radiometers (MWRs) can be useful for the detection of mesoscale phenomena because they provide thermodynamic profiles in a minute time scale. These profiles are mainly used in non-precipitation conditions due to degraded accuracy of the MWR measurements in precipitation. Recently, Radiometrics Corporation used proprietary neural network methods to retrieve temperature, humidity and liquid profiles from off-zenith (15° elevation) radiometer observations to provide higher accuracy during precipitation. In this paper, using the MWR-retrieved temperature and humidity profiles with collocated radiosondes from June 2010 to September 2013 in Wuhan, the impact of precipitation on the MWR measurement accuracy as well as the effect of off-zenith neural network methods on it is investigated. In precipitation, the correlation coefficients of the MWR temperature and vapor density profiles against radiosondes are smaller than those in non-precipitation, and the bias and RMS against radiosondes also increase, especially around 2km heights. For the MWR relative humidity profile, the correlation coefficient in precipitation is obvious smaller than that in non-precipitation below 4.5km, and the bias and RMS against radiosondes are clearly larger above 5.5km. Moreover, the differences between the precipitation and non-precipitation cases mostly are statistically significant. Compared with the results of the zenith observation, the off-zenith observation makes a positive effect on reducing the impact of precipitation on the accuracy of MWR temperature and vapor density retrievals. On the whole, the MWR temperature bias and RMS against radiosondes in precipitation are reduced from 3.6 and 4.2K to 1.3 and 3.1K, respectively, and the MWR vapor density bias is also reduced from 1.10g/m3 to 0.18g/m3 with the RMS decreasing from 2.90g/m3 to 1.91g/m3. The temperature correlation coefficient between the MWR and radiosonde in precipitation is clearly improved above 3km heights, and the temperature bias and RMS are significantly reduced at most heights. For the MWR vapor density retrievals in precipitation, the correlation coefficient, bias and RMS against radiosondes are clearly improved above 2km heights. Additionally, the off-zenith observations during non-precipitation cases are also better compared to zenith observations. Therefore, off-zenith observations generally are better than zenith observations. This could be due to the fact that the off-zenith observations are more representative of the conditions in which radiosonde observations are also taken. [Copyright &y& Elsevier]