Local Ensemble Transform Kalman Filter Experiments with the Nonhydrostatic Icosahedral Atmospheric Model NICAM

Terasaki, Koji; Sawada, Masahiro; Miyoshi, Takemasa

doi:10.2151/sola.2015-006

Cited by 41 publications

(49 citation statements)

References 12 publications

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“…The LETKF solves the analysis equations at each grid point independently. Terasaki et al [2015] successfully implemented the LETKF with NICAM and assimilated conventional observations. In this study we extend their work to assimilate the satellite-derived precipitation GSMaP with the NICAM-LETKF system.…”

Section: Nicam-letkfmentioning

confidence: 99%

“…Otherwise, the invalid data are filled by the average value of the valid data before aggregation. [Terasaki et al, 2015] (black) is modified with the newly developed algorithms and data (red). The broken arrow denotes preprocessing, and the full arrows denote data flows.…”

Section: Gsmap Precipitationmentioning

confidence: 99%

“…Thus, we aim to validate whether or not the approach of LKM13, LKMH16a, and LMK16b also works in another global data assimilation system. We use the coupled NICAM-LETKF [Terasaki et al, 2015], composed of NICAM and the Local Ensemble Transform Kalman Filter (LETKF) [Hunt et al, 2007].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Nicam-letkfmentioning

confidence: 99%

Section: Gsmap Precipitationmentioning

confidence: 99%

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

et al. 2017

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“…The LETKF can be implemented independently of the model, is suitable for ensemble forecasting and is efficient for parallel computing. Recently, the LETKF has been implemented with various models such as the global and regional atmosphere (e.g., Miyoshi and Aranami 2006;Miyoshi et al 2010;Miyoshi and Kunii 2012;Terasaki et al 2015), global and coastal ocean (Hoffman et al 2008;Penny et al 2013) and Martian atmosphere (Hoffman et al 2010;Greybush et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…To assimilate all observations, an observation operator which converts the model gridded data to the observed quantity must be defined for all observations. The observation operator used in ALEDAS was related to spatial interpolation and was based on the trilinear interpolation by using 8 surrounding model grid points to an observation point (see Terasaki et al 2015). However, the interpolation algorithm in this observation operator has a limitation; it cannot convert the model gridded data into values observed at the poles.…”

Section: Introductionmentioning

confidence: 99%

Using Observations near the Poles in the AFES-LETKF Data Assimilation System

Yamazaki

Enomoto

Miyoshi

et al. 2017

SOLA

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The observation operators in the local ensemble transform Kalman filter (LETKF) were improved to enable use of observations in the vicinity of the poles in the data assimilation system composed of the atmospheric general circulation model for the Earth Simulator (AFES) and the LETKF. The improved observation operators allow to assimilate the observations located south (north) of southernmost (northernmost) Gaussian grid latitudes. An algorithm for searching the nearest observations from an analyzed grid for error covariance localization was also modified to efficiently assimilate observations near the poles.The new algorithms were incorporated into the LETKF, and the impacts of routine radiosonde observations at the South Pole during the periods of July 2012 and January 2013 were assessed. The radiosonde observations suppressed an artificial expansion of the analysis ensemble spread which occasionally caused numerical instability in the upper troposphere and the lower stratosphere over the Antarctic regions. The analysis was also improved in the Antarctic regions.

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Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere

Kotsuki

Ota

Miyoshi

2017

Quart J Royal Meteoro Soc

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Covariance inflation plays an important role in the ensemble Kalman filter because the ensemble-based error variance is usually underestimated due to various factors such as the limited ensemble size and model imperfections. Manual tuning of the inflation parameters by trial and error is computationally expensive; therefore, several studies have proposed approaches to adaptive estimation of the inflation parameters. Among others, this study focuses on the covariance relaxation method which realizes spatially dependent inflation with a spatially homogeneous relaxation parameter. This study performs a series of experiments with the non-hydrostatic icosahedral atmospheric model (NICAM) and the local ensemble transform Kalman filter (LETKF) assimilating the real-world conventional observations and satellite radiances. Two adaptive covariance relaxation methods are implemented: relaxation to prior spread based on Ying and Zhang (adaptive-RTPS), and relaxation to prior perturbation (adaptive-RTPP). Both adaptive-RTPS and adaptive-RTPP generally improve the analysis compared to a baseline control experiment with an adaptive multiplicative inflation method. However, the adaptive-RTPS and adaptive-RTPP methods lead to an over-dispersive (under-dispersive) ensemble in the sparsely (densely) observed regions compared with the adaptive multiplicative inflation method. We find that the adaptive-RTPS and adaptive-RTPP methods are robust to a sudden change in the observing networks and observation error settings.

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Local Ensemble Transform Kalman Filter Experiments with the Nonhydrostatic Icosahedral Atmospheric Model NICAM

Cited by 41 publications

References 12 publications

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

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

Using Observations near the Poles in the AFES-LETKF Data Assimilation System

Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere

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