Masahiro Kazumori

Impact Study of AMSR-E Radiances in the NCEP Global Data Assimilation System

Abstract The impact of radiance observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) was investigated in the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS). The GDAS used NCEP’s Gridpoint Statistical Interpolation (GSI) analysis system and the operational NCEP global forecast model. To improve the performance of AMSR-E low-frequency channels, a new microwave ocean emissivity model and its adjoint with respect to the surface wind speed and temperature were developed and incorporated into the assimilation system. The most significant impacts of AMSR-E radiances on the analysis were an increase in temperature of about 0.2 K at 850 hPa at the higher latitudes and a decrease in humidity of about 0.1 g kg−1 at 850 hPa over the ocean when the new emissivity model was used. There was no significant difference in the mean 6-h rainfall in the assimilation cycle. The forecasts made from the assimilation that included the AMSR-E ...

Satellite Radiance Assimilation in the JMA Operational Mesoscale 4DVAR System

AbstractThe direct radiance assimilation scheme used in the Japan Meteorological Agency (JMA) global analysis system is applied to the JMA mesoscale four-dimensional variational data assimilation (4DVAR) system with two modifications. First, the data-thinning distance is shortened, and, second, the atmospheric profiles are extrapolated from the mesoscale model top to the radiative transfer model top using the U.S. Standard Atmosphere lapse rate. Although the variational bias correction method is widely used in many numerical weather prediction centers for global radiance assimilations, a radiance bias correction method for regional models has not been established because of difficulties in estimating the biases within limited regions and times. This paper examined the use of the bias correction coefficients estimated in the global system for the mesoscale system when the radiance data were introduced instead of the retrievals. It was found that the profile extrapolation was necessary to reduce the biases....

Impacts of Improved Quality Control and Ocean Emissivity Model for AMSR-E Radiance Assimilation in JMA

Since 2006, the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Special Sensor Microwave Imager (SSM/I), and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) clear ocean radiance data have been utilized in Japan Meteorological Agency (JMA) operational global four dimensional variational (4D-Var) data assimilation system [1]. The quality control and bias correction to screen out cloud- and rain affected radiance data are crucial parts in the pre-process of the clear radiance data assimilation. Improved quality control and bias correction were developed and FAST emissivity model version 3 (FASTEM-3) [3] was employed for the assimilation. Experiments showed a reduction of bias in moisture field and improvements of 850 hPa temperature and 500 hPa geopotential height forecasts in the Southern Hemisphere. The results suggest the use of the new quality control and bias correction scheme can select clear radiance data effectively and accurate moisture analysis produce better global forecasts in the JMA operational Numerical Weather Prediction (NWP) system.

Asymmetric Features of Oceanic Microwave Brightness Temperature in HighSurface Wind Speed Condition

Asymmetric features of oceanic brightness temperature from spaceborne microwave imagers in high surface wind speed conditions were investigated with two kinds of collocated data. The first is simultaneous measurements of microwave brightness temperatures and surface wind vectors from the Advanced Microwave Scanning Radiometer (AMSR) and SeaWinds on Advanced Earth Observing Satellite II. The second is microwave brightness temperature observations (AMSR2 and the Special Sensor Microwave Imager Sounder) and surface wind vectors in the European Centre for Medium-Range Weather Forecasts numerical weather prediction model. Both collocated data sets showed that the vertical-polarized and the horizontal-polarized microwave brightness temperature have out-of-phase asymmetric features in terms of relative wind direction (RWD) at high surface wind speeds. Furthermore, different asymmetric features were found for the northern and Southern Hemispheres and for ascending and descending satellite orbits. Although similar asymmetric features can be found in other microwave imager studies, the causes of the asymmetry have not been fully investigated. To investigate the cause of the asymmetry, the observation frequency regarding air-sea temperature difference was examined in upwind, downwind, and crosswind cases. Two important factors contribute to the asymmetry. First, the observations from inclined polar orbit satellites provide different samplings on atmospheric stability in terms of the RWD. Second, the oceanic microwave brightness temperatures have negative correlations with atmospheric stability at high surface wind speeds. The out-of-phase asymmetry is closely related with atmospheric stability, and it appears under a high-surface-wind-speed condition.

Operational use of satellite radiance in JMA mesoscale analysis

In Japan Meteorological Agencys mesoscale Numerical Weather Prediction system, a radiance assimilation scheme was introduced on 13 December 2010. The radiance assimilation enables us to use the satellite radiance data directly without any error contamination from the retrieval process. From data denial experiments, it was confirmed that the radiance assimilation brought considerable improvements, especially in tropospheric geopotential height analysis and short-range precipitation forecasts. And the radiance data constrained the temperature and moisture fields strongly over the ocean in clear and non-precipitation areas. On the other hand, the contribution in thick cloud and rainy area and over the land was relatively weak in the analysis. Assimilation of cloud- and rain- affected radiances and surface sensitive radiances is expected to bring further improvement of the analysis and precipitation forecast score.

The assimilation of satellite microwave observation in JMA's meso-scale model

Accurate Numerical Weather Prediction (NWP) is one of the essential information for natural disaster prevention. Japan Meteorological Agency (JMA) has been operating a Meso-scale model (MSM). The target of the MSM is to provide guidance for issuing warnings or making very short-range forecasts of precipitation to cover Japan and its surrounding areas. In order to produce accurate precipitation forecasts by MSM, realistic moisture fields as initial conditions are necessary. The initial fields are produced in analyses with a four dimensional variational data assimilation method. The initial fields are updated eight times per day to capture rapid change of mesoscale weather conditions. Because Japan is a country surrounded by ocean, moisture information over the ocean is a key for the accurate humidity analysis and the precipitation forecasting. Observations of microwave imagers in space contain the moisture information over the ocean. The microwave imager data are available in wide coverage under all weather conditions and play an important role in the analysis. Microwave brightness temperature in clear sky condition and retrieved precipitation in rainy condition from various microwave imagers are assimilated in the analysis. In this study, Global Change Observation Mission 1st Water (GCOM-W1) / Advanced Microwave Scanning Radiometer-2 (AMSR2) data were newly incorporated in the analysis. From the preliminary AMSR2 data assimilation experiment, improvements of the humidity analysis and the precipitation forecasting were found. The results suggest the use of multiple satellite data is necessary to produce realistic moisture fields as the initial condition for the operational NWP.

Retrieval of atmospheric water vapor from AMSR-E and the application for NWP at JMA

Atmospheric water vapor is a key geophysical parameter for Numerical Weather Prediction (NWP). For operational forecasts of heavy precipitation and typhoon, accurate moisture analysis fields as the initial conditions of NWP are required. Atmospheric water vapor is a source of clouds, rains and snows, and the distribution and amount play important roles for a representation of latent heat releases and absorptions in NWP model. Numerous moisture observations are required to produce accurate and realistic analyses and forecasts in the data assimilation. In this paper, a retrieval algorithm of atmospheric water vapor for AMSR-E and results of the product verification and comparison are presented. In addition, some NWP experiment results in JMA Meso-Scale Model data assimilation system are shown as an application example of this algorithm.

Status of GCOM-W1 development and expected meteorological applications

The Global Change Observation Mission (GCOM) consists of two polar orbiting satellite observing systems, GCOM-W (Water) and GCOM-C (Climate), and three generations to achieve global and long-term monitoring of the Earth. GCOM-W1 is the first satellite of the GCOM-W series and scheduled to be launched in Japanese fiscal year 2011. The mission instrument will be the Advanced Microwave Scanning Radiometer-2 (AMSR2), which is the successor instrument of AMSR on ADEOS-II and AMSR-E on EOS Aqua platform. Development of the GCOM-W1 system progresses favorably. The mechanical and thermal tests using the GCOM-W1 structural and thermal model were successfully completed. The GCOM-W1 and AMSR2 proto-flight models are under their proto-flight testing. In the middle of 2010, AMSR2 will be delivered to satellite system prior to the system proto-flight test of GCOM-W1. Retrieval algorithms are being developed by collaborating with principal investigators. Algorithm comparisons or integrations are now underway for several algorithms to find best available algorithms for post-launch processing. Also, maintaining and extending the validation sites such as the Mongolian Plateau site for soil moisture is being implemented. In addition to the long-term climate variability monitoring, meteorological applications will be the most important operational utilization of AMSR2 data. Currently, AMSR-E data are being used for numerical prediction through data assimilation at several meteorological agencies. Also, retrieved geophysical parameters such as sea surface temperature are being used for diagnostics of the weather and ocean variations.

A retrieval algorithm of atmospheric water vapor and cloud liquid water for AMSR-E

A retrieval algorithm of atmospheric water vapor and cloud liquid water for Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) was developed. The algorithm uses a simple radiative transfer model which is composed of single atmospheric layer and ocean surface. The atmospheric transmittance was derived from an iteration calculation using vertical mean temperature estimated from ancillary 850 hPa temperature data and observed AMSR-E brightness temperature. The algorithm is applicable for other microwave instruments. The retrieved atmospheric water vapor is expected to be used in Japan Meteorological Agency (JMA) operational Meso-scale analysis and forecast. In a case study on heavy ram event in Japan, assimilation of the retrieved atmospheric water vapor from Special Sensor Microwave Imager Sounder (SSMIS) showed intensified moisture flows in the Meso-scale analysis and forecast and produced a strong rainfall band in the forecast.

Impact studies of AMSR-E ocean surface wind speed data in NWP at JMA

The impact of assimilation of Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) ocean surface wind speed data was investigated in Japan Meteorological Agency (JMA) global four dimensional vanational data assimilation system. Japan Aerospace Exploration Agency (JAXA) produces ocean surface wind speed data from AMSR-E measurements as a standard level 2 product using 37GHz vertical and horizontal polarized channels in no rainy area. As a research product, all weather ocean surface wind speed data are also produced by including AMSR-E low frequency (6.925 and cyclone Nargis in Myanmar 2008 was performed to examine the impact of microwave imager radiance data and AMSR-E all weather ocean wind speed data. Although there were no significant improvements in the cyclone track forecast by adding all weather ocean wind speed data, the assimilation of the data inside the cyclone strengthened the intensity and the maximum wind speed in the forecast realistically.