Abstract:The new Version 2.3 of the Global Precipitation Climatology Project (GPCP) Monthly analysis is described in terms of changes made to improve the homogeneity of the product, especially after 2002. These changes include corrections to cross-calibration of satellite data inputs and updates to the gauge analysis. Over-ocean changes starting in 2003 resulted in an overall precipitation increase of 1.8% after 2009. Updating the gauge analysis to its final, high-quality version increases the global land total by 1.8% for the post-2002 period. These changes correct a small, incorrect dip in the estimated global precipitation over the last decade given by the earlier Version 2.2. The GPCP analysis is also used to describe global precipitation in 2017. The general La Niña pattern for 2017 is noted and the evolution from the early 2016 El Niño pattern is described. The 2017 global value is one of the highest for the 1979-2017 period, exceeded only by 2016 and 1998 (both El Niño years), and reinforces the small positive trend. Results for 2017 also reinforce significant trends in precipitation intensity (on a monthly scale) in the tropics. These results for 2017 indicate the value of the GPCP analysis, in addition to research, for climate monitoring.

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Daily rainfall data collected from more than 100 gauges over Thailand for the period 1993-2002 are used to study the climatology and spatial and temporal characteristics of Thailand rainfall variations. Comparison of the Thailand gauge (TG) data binned at 1°ϫ 1°with the Global Precipitation Climatology Centre (GPCC) monitoring product shows a small bias (1.11%), and the differences can be reconciled in terms of the increased number of stations in the TG dataset. Comparison of daily TG with Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 rain estimates shows improvements over version 5 (V5) in terms of bias and mean absolute difference (MAD). The V5 is computed from the adjusted Geostationary Operational Environmental Satellite (GOES) precipitation index (AGPI) and V6 is computed using the TRMM Multisatellite Precipitation Analysis (TMPA) algorithm. The V6 histogram is much closer to that of TG than V5 in terms of rain fraction and conditional rain rates. Scatterplots show that both versions of the satellite products are deficient in capturing heavy rain events. In terms of detecting rain events, a critical success index (CSI) shows no difference between V6 and V5 3B42. The CSI for V6 is higher for the rainy season than the dry season. These results are generally insensitive to rain-rate threshold and averaging periods. The temporal and spatial autocorrelation of daily rain rates for TG, V6, and V5 3B42 are computed. Autocorrelation function analyses show improved temporal and spatial autocorrelations for V6 compared to TG over V5 with e-folding times of 1, 1, and 2 days, and isotropic spatial decorrelation distances of 1.14°, 1.87°, and 3.61°for TG, V6, and V5, respectively. Rain event statistics show that the V6 3B42 overestimates the rain event durations and underestimates the rain event separations and the event conditional rain rates when compared to TG. This study points to the need to further improve the 3B42 algorithm to lower the false detection rate and improve the estimation of heavy rainfall events.

Various recent studies have shown that societal efforts to mitigate (e.g. “lockdown”) the outbreak of the 2019 coronavirus disease (COVID-19) caused non-negligible impacts on the environment, especially air quality. To examine if interventional policies due to COVID-19 have had a similar impact in the US state of California, this paper investigates the spatiotemporal patterns and changes in air pollution before, during and after the lockdown of the state, comparing the air quality measurements in 2020 with historical averages from 2015 to 2019. Through time series analysis, a sudden drop and uptick of air pollution are found around the dates when shutdown and reopening were ordered, respectively. The spatial patterns of nitrogen dioxide (NO 2 ) tropospheric vertical column density (TVCD) show a decreasing trend over the locations of major powerplants and an increasing trend over residential areas near interactions of national highways. Ground-based observations around California show a 38%, 49%, and 31% drop in the concentration of NO 2 , carbon monoxide (CO) and particulate matter 2.5 (PM 2.5 ) during the lockdown (March 19–May 7) compared to before (January 26–March 18) in 2020. These are 16%, 25% and 19% sharper than the means of the previous five years in the same periods, respectively. Our study offers evidence of the environmental impact introduced by COVID-19, and insight into related economic influences.