Assimilating Every‐10‐minute Himawari‐8 Infrared Radiances to Improve Convective Predictability

Sawada, Yohei; Okamoto, Kozo; Kunii, Masaru; Miyoshi, Takemasa

doi:10.1029/2018jd029643

Cited by 48 publications

(39 citation statements)

References 58 publications

(110 reference statements)

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“…This study used the local ensemble transform Kalman filter (LETKF: Hunt et al, ) applied for JMA‐NHM (NHM‐LETKF: Kunii, ). The NHM‐LETKF system has been used in many studies, including one on a local severe rainfall event in 2012 in Japan (Kunii, ), impact assessment of rapid‐scan atmospheric motion vectors (AMVs) from Himawari‐8 (Kunii et al, ), tornadic supercells (Yokota et al, ), severe weather prediction with assimilation of phased array radars and Himawari‐8 (Miyoshi et al, ), high‐resolution atmosphere–ocean coupled processes (Kunii et al, ), strongly coupled land–atmosphere data assimilation (Sawada et al, ) and improving convective predictability using frequent radiances from Himawari‐8 (Sawada et al, ). We extended the system developed in Kunii () to handle radiance observations.…”

Section: Observations Model and Data Assimilation Systemmentioning

confidence: 99%

“…Assimilating cloud‐affected infrared radiances in all‐sky conditions must be able to handle strong nonlinearity and low predictability of complicated cloud‐related processes due to the high sensitivity of infrared radiances to clouds. There have recently been many works addressing these particular challenges (Chevallier et al, ; Otkin, ; Martinet et al, ; Stengel et al, ; Zhang et al, ; Minamide and Zhang, 2018; Honda et al, ; ; Sawada et al, ). For example, Honda et al .…”

Section: Introductionmentioning

confidence: 99%

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Comparison of assimilating all‐sky and clear‐sky infrared radiances from Himawari‐8 in a mesoscale system

Okamoto

Sawada

Kunii

2019

Quart J Royal Meteoro Soc

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This study examines the advantages of infrared all-sky radiance (ASR) assimilation over traditional clear-sky radiance (CSR) assimilation using a mesoscale LETKF data assimilation system. To effectively assimilate ASR data from the Himawari-8 geostationary satellite, a cloud-dependent quality-control procedure and an observation error model were developed. A single humidity band to be assimilated and thinning distance were determined based on observation error statistics. The operational pre-processing and parameter settings, such as observation errors and an adaptive bias correction for CSR, were incorporated into the LETKF data assimilation system. A comparison of the impacts of assimilating ASRs and CSRs was accomplished using single-cycle and 10-day cycle assimilation experiments. Study results revealed that ASR assimilation provided a higher degree of improvement in the first-guess fit for conventional observations and satellite retrievals with respect to temperature, moisture and wind. Furthermore, ASR assimilation displayed a more stable improvement in the prediction of a severe rainfall event because it has more universal data coverage than CSR. Adaptive bias correction schemes with two different sets of predictors for ASRs were tested and revealed the difficulty in extracting additional information for the assimilation of no-bias corrected ASR due to complicated bias factors. This was in contrast to the CSR assimilation, where bias correction had a positive impact. KEYWORDS all-sky infrared radiance, data assimilation, Himawari-8 1 Q J R Meteorol Soc. 2019;145:745-766.wileyonlinelibrary.com/journal/qj

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Section: Observations Model and Data Assimilation Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of assimilating all‐sky and clear‐sky infrared radiances from Himawari‐8 in a mesoscale system

Okamoto

Sawada

Kunii

2019

Quart J Royal Meteoro Soc

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“…In particular, the higher resolution and low predictability call for the assimilation of spatially and temporally highly resolved observations (Gustafsson et al ., ). Consequently, substantial efforts have been made to assimilate high‐resolution radar reflectivity and cloud‐affected satellite observations (Miyoshi et al ., ; Harnisch et al ., ; Scheck et al ., ; Sawada et al ., ). However, successfully assimilating such observations requires both accurate parameterizations and accurate estimates of highly flow‐dependent error covariances (Houtekamer and Zhang, ).…”

Section: Introductionmentioning

confidence: 97%

A convective‐scale 1,000‐member ensemble simulation and potential applications

Necker

Geiss²,

Weißmann

et al. 2020

Quart J Royal Meteoro Soc

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This study presents the first convective-scale 1,000-member ensemble simulation over central Europe, which provides a unique data set for various applications. A comparison with the operational regional 40-member ensemble of Deutscher Wetterdienst shows that the 1,000-member simulation exhibits realistic spread properties overall. Based on this, we discuss two potential applications. First, we quantify the sampling error of spatial covariances of smaller subsets compared with the 1,000-member simulation. Knowledge about sampling errors and their dependence on ensemble size is crucial for ensemble and hybrid data assimilation and for developing better approaches for localization in this context. Secondly, we present an approach for estimating the relative potential impact of different observable quantities using ensemble sensitivity analysis. This will provide the basis for consecutive studies developing future observation and data assimilation strategies. Sensitivity studies on the ensemble size indicate that about 200 ensemble members are required to estimate the potential impact of observable quantities with respect to precipitation forecasts. K E Y W O R D Sconvective-scale, covariance, data assimilation, ensemble sensitivity analysis, localization, observing system, sampling error

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“…The Himawari-8 AHI is capable of supplying a scan of the full-disk of the earth's surface and the target region at the temporal resolution of 10-min and 2.5-min, respectively. On account of the high temporal resolution, it is important for better understanding the R S spatiotemporal variations in short time scales [54]. The information of Himawari-8 AHI bands is shown in Table 1 [55,56], and more detailed information about the Himawari-8 AHI is described in Bessho et al [50].…”

Section: Himawari-8 Ahi Datamentioning

confidence: 99%

Estimation of Surface Downward Shortwave Radiation over China from Himawari-8 AHI Data Based on Random Forest

Hou

Zhang

et al. 2020

Remote Sensing

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Downward shortwave radiation (RS) drives many processes related to atmosphere–surface interactions and has great influence on the earth’s climate system. However, ground-measured RS is still insufficient to represent the land surface, so it is still critical to generate high accuracy and spatially continuous RS data. This study tries to apply the random forest (RF) method to estimate the RS from the Himawari-8 Advanced Himawari Imager (AHI) data from February to May 2016 with a two-km spatial resolution and a one-day temporal resolution. The ground-measured RS at 86 stations of the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) are collected to evaluate the estimated RS data from the RF method. The evaluation results indicate that the RF method is capable of estimating the RS well at both the daily and monthly time scales. For the daily time scale, the evaluation results based on validation data show an overall R value of 0.92, a root mean square error (RMSE) value of 35.38 (18.40%) Wm−2, and a mean bias error (MBE) value of 0.01 (0.01%) Wm−2. For the estimated monthly RS, the overall R was 0.99, the RMSE was 7.74 (4.09%) Wm−2, and the MBE was 0.03 (0.02%) Wm−2 at the selected stations. The comparison between the estimated RS data over China and the Clouds and Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) RS dataset was also conducted in this study. The comparison results indicate that the RS estimates from the RF method have comparable accuracy with the CERES-EBAF RS data over China but provide higher spatial and temporal resolution.

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Assimilating Every‐10‐minute Himawari‐8 Infrared Radiances to Improve Convective Predictability

Cited by 48 publications

References 58 publications

Comparison of assimilating all‐sky and clear‐sky infrared radiances from Himawari‐8 in a mesoscale system

Comparison of assimilating all‐sky and clear‐sky infrared radiances from Himawari‐8 in a mesoscale system

A convective‐scale 1,000‐member ensemble simulation and potential applications

Estimation of Surface Downward Shortwave Radiation over China from Himawari-8 AHI Data Based on Random Forest

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