One‐dimensional maximum‐likelihood estimation for spaceborne precipitation radar data assimilation

• Published in: Quarterly journal of the Royal Meteorological Society Vol. 147; no. 735; pp. 858 - 875
• Main Authors: Ikuta, Yasutaka, Okamoto, Kozo, Kubota, Takuji
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.01.2021; Wiley Subscription Services, Inc
• Subjects: Accuracy; Data assimilation; Data collection; Dimensions; Global precipitation; GPM/DPR; Humidity; Humidity profiles; mesoscale; Meteorological satellites; Precipitation; Precipitation forecasting; Precipitation systems; Probability density function; Probability theory; Radar; Radar data; radar reflectivity; Relative humidity; Severe weather; spaceborne precipitation radar; Weather forecasting
• ISSN: 0035-9009; 1477-870X
• DOI: 10.1002/qj.3950

Spaceborne precipitation radar such as Global Precipitation Measurement (GPM)/dual‐frequency precipitation radar (DPR) provides valuable observations of precipitation systems in three dimensions. Assimilation of GPM/DPR data is becoming an important technique for improving the accuracy of forecasting to complement scarce ground‐based observations. This study presents a new, one‐dimensional maximum‐likelihood estimation (1D‐MLE) method developed by the authors that enables the estimation of relative humidity profiles according to a non‐Gaussian probability density function. By assimilating the estimated relative humidity profiles using a four‐dimensional variational (4D‐Var) method, mesoscale precipitation forecasts by the Japan Meteorological Agency (JMA) have been considerably improved. The displacement of forecast precipitation during a severe weather event, in particular, is improved significantly. It was found that forecasting accuracy was maintained for a narrow GPM/DPR swath and low revisit frequency by repeating the assimilation–forecast cycle. Since the effectiveness was confirmed, the JMA began assimilating GPM/DPR data using the 1D‐MLE approach from March 2016. Spaceborne precipitation radar such as GPM/DPR provides valuable observations of three‐dimensional precipitation systems and complements scarce ground‐based observations. This study presents a new, one‐dimensional maximum‐likelihood estimation (1D‐MLE) method for effective assimilation of GPM/DPR that enables the estimation of relative humidity profiles according to a non‐Gaussian probability density function. By assimilating the estimated relative humidity profiles using 4D‐Var, mesoscale precipitation forecasts have been considerably improved.