Assimilating the global satellite mapping of precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)

Abstract

This study aims to propose two new approaches to improve precipitation forecasts from numerical weather prediction (NWP) models through effective data assimilation of satellite‐derived precipitation. The assimilation of precipitation data is known to be very difficult mainly because of highly non‐Gaussian statistics of precipitation variables. Following Lien et al., this study addresses the non‐Gaussianity issue by applying the Gaussian transformation (GT) based on the empirical cumulative distribution function (CDF) of precipitation. We propose a method that constructs the CDF with only recent 1 month samples, without using a long period of samples needed previously. We also propose a method to use the inverse GT, with which we can obtain realistic precipitation fields from biased NWP model outputs. We assimilate the Japan Aerospace eXploration Agency's Global Satellite Mapping of Precipitation (GSMaP) data into the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) at 112 km horizontal resolution. Assimilating the GSMaP data results in improved weather forecasts compared to the control experiment assimilating only rawinsonde data. We find that horizontal observation thinning is necessary, probably due to the horizontal observation‐error correlations in the GSMaP data. We also obtained precipitation fields similar to GSMaP from the NICAM precipitation forecasts by using the inverse GT, leading to an improved precipitation forecast.

Online Model Parameter Estimation With Ensemble Data Assimilation in the Real Global Atmosphere: A Case With the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Global Satellite Mapping of Precipitation Data

“…The NICAM‐LETKF assimilates satellite radiances of the Advanced Microwave Sounding Unit‐A (AMSU‐A; Terasaki & Miyoshi, ) and GSMaP precipitation (Kotsuki, Miyoshi, et al, ) in addition to conventional observation from the operational system of the National Centers for Environmental Prediction (NCEP; a.k.a. NCEP PREPBUFR).…”

“…The first approach generally involves data assimilation, which estimates the optimal initial conditions based on observations and model forecasts with their error covariance. While precipitation data assimilation is known to be complicated, recent studies have succeeded in improving initial conditions and precipitation forecasts by assimilating satellite‐based precipitation products (Lien, Miyoshi, & Kalnay, ; Kotsuki, Miyoshi, et al, ). For the second approach, the tuning of NWP model parameters plays an important role.…”

“…B 1 is estimated by assimilating the Japan Aerospace eXploration Agency (JAXA)'s Global Satellite Mapping of Precipitation (GSMaP; Kubota et al, ; Ushio et al, ), so that the estimated parameter would reduce the precipitation forecast errors relative to GSMaP data. The experimental system is based on the one by Kotsuki, Miyoshi, et al () who assimilated GSMaP with NICAM‐LETKF successfully.…”

Accelerating assimilation development for new observing systems using EFSO

“…Assimilation of all precipitation data without special treatments usually leads to no impacts or negative impacts. In spite of these issues, Lien et al (2013Lien et al ( , 2016b and Kotsuki et al (2017) conducted a series of experiments, from an idealized configuration to realistic systems, to show that it is possible to improve the medium-range forecasts in a global model by the assimilation of global precipitation data. The keys in their experiments are to use 1. an LETKF to exploit the flow-dependent background error correlation between prognostic variables and diagnosed precipitation;…”

Accelerating assimilation development for new observing systems using EFSO

“…Assimilation of all precipitation data without special treatments usually leads to no impacts or negative impacts. In spite of these issues, Lien et al (2013Lien et al ( , 2016b and Kotsuki et al (2017) conducted a series of experiments, from an idealized configuration to realistic systems, to show that it is possible to improve the medium-range forecasts in a global model by the assimilation of global precipitation data. The keys in their experiments are to use (1) An LETKF to exploit the flow-dependent background error correlation between prognostic variables and diagnosed 5 precipitation, (2) A Gaussian transformation of the precipitation variable, which mitigates the inherent non-Gaussianity of precipitation data, and (3) Proper QC criteria to exclude the "bad" observations that we cannot effectively use, including the important requirement that enough ensemble background members should have non-zero precipitation (Lien et al, 2013).…”