Data Assimilation of GPS Precipitable Water Vapor into the JMA Mesoscale Numerical Weather Prediction Model and its Impact on Rainfall Forecasts

Nakamura, Hajime; Koizumi, Kinya; Mannoji, Nobutaka

doi:10.2151/jmsj.2004.441

Cited by 61 publications

(41 citation statements)

References 6 publications

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“…Authors also noted that the forecasts work better if the ground based automatic weather stations were used in the same assimilation run. Another example of an early stage case-based research is the assimilation of GPS PW by Nakamura et al, (2004) with 4DVAR scheme into mesoscale JMA model for summer intensive rain cases. The assimilation of GPS data improved the precipitation location, but the statistics did not show large 20 improvement.…”

Section: Introductionmentioning

confidence: 99%

4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

Rohm¹,

Guzikowski²,

Wilgan³

et al. 2018

Preprint

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Abstract. The GNSS data assimilation is currently widely discussed in the literature with respect to the various applications in meteorology and numerical weather models. Data assimilation combines atmospheric measurements with knowledge of 10 atmospheric behavior as codified in computer models. With this approach, the 'best' estimate of current conditions consistent with both information sources is produced. Some approaches allow assimilating also the non-prognostic variables, including remote sensing data from radar or GNSS (Global Navigation Satellite System). These techniques are named variational data assimilation schemes and are based on a minimization of the cost function, which contains the differences between the model state (background) and the observations. The obtained WRF predictions are validated against surface meteorological measurements, including air temperature, humidity, wind speed, and rainfall rate. Results from the first experiment (May and June, 2013) show that the assimilation of 25 GNSS data (both ZTD and PW) have positive impact on the rain and humidity forecast. However, the assimilation of ZTD is more successful, and brings substantial reduction of errors in rain forecast by 8%, and a 20% improvement in bias of humidity forecast, but it has a slight negative impact on temperature bias and wind speed. Second experiment (5-23 May, 2013) reveals that the PW or ZTD assimilation leads to a similar reduction of errors as in the first experiment, moreover, adding SYNOP and RS observations to the assimilation does not improve the humidity or rain forecasts (in the 48h forecast) 30 but reduces errors in the wind speed and temperature. Furthermore, short term predictions (up to 24h) of rain and humidity are better when SYNOP and RS observations are assimilated. The impact of assimilation of ZTD and PW in severe weather Atmos. Meas. Tech. Discuss., https://doi

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Section: Introductionmentioning

confidence: 99%

4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

Rohm¹,

Guzikowski²,

Wilgan³

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…Although their primary purpose is to study crustal deformation, they have a large potential as meteorological sensors. Several studies have shown that the water vapor information retrieved from GEONET has positive impacts on numerical weather prediction (NWP) (e.g., Nakamura et al 2004;Koizumi and Sato 2004;Seko et al 2004). Such previous studies conducted in Japan mainly used post processed GPS PWV data that was retrieved from a GPS analysis using a precise final ephemeris (orbits and clocks).…”

Section: Introductionmentioning

confidence: 99%

A Study of Near Real-time Water Vapor Analysis Using a Nationwide Dense GPS Network of Japan

Shoji¹

2009

Journal of the Meteorological Society of Japan

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In order to use the nationwide dense receiver network of the Global Positioning System (GPS) in Japan to contribute to water vapor monitoring and numerical weather prediction, a near real-time (NRT) analysis trial was performed at the Meteorological Research Institute. In this paper, the NRT analysis procedure is described along with some validation results.In view of the computational load involved in analyzing more than 1,300 GPS stations in Japan, the precise point positioning (PPP) procedure was adopted. The PPP procedure requires accurate information of GPS satellites' positions and clock o¤sets. International GNSS Service (IGS) has been routinely providing ultra rapid ephemeris (IGU) that includes satellite orbits and clock o¤sets with latency of about 3 hours. We found the accuracy of satellite clock o¤sets in IGU was insu‰cient for the retrieval of precipitable water vapor (PWV) through the PPP procedure. Therefore, we applied correction to the IGU clock using the predicted clock o¤set at an IGS station ''USUD''. The hydrogen maser atomic clock at USUD also had some di¤erences with GPS time. However, we found it could be fitted and predicted by a linear equation for a period of several days.The resulting satellite clock o¤sets exhibited some biases toward the IGS final ephemeris, but the time constant biases of satellite clock o¤sets did not a¤ect the PWV retrieval at all. The retrieved PWV data agreed well with those obtained from radio-sonde observations. The root mean square di¤erences in summer and in winter were around 3.4 mm and 1.6 mm, respectively. The results were comparable with those obtained by preceding studies using the final ephemeris. The Retrieved spatial and temporal variation of PWV in a heavy rainfall case demonstrated the usefulness of the NRT PWV retrieval for weather monitoring. We could capture the water vapor increase that preceded torrential rain.

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“…Figure 5 shows that assimilation of precipitation amount data improves precipitation forecasts throughout the whole forecast period of 18 hours for both of weak and moderate pre- cipitations. Impacts of precipitable water data from GPS ground receivers or satellite microwave imagers and radial wind data from Doppler radar were also investigated with the mesoscale 4D-Var system by Nakamura et al (2004), Koizumi and Sato (2004), and Seko et al (2004).…”

Section: Mesoscale Applications Of 4d-varmentioning

confidence: 99%

Recent Progress of Data Assimilation Methods in Meteorology

Tsuyuki

Miyoshi

2007

Journal of the Meteorological Society of Japan

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Data assimilation is a methodology for estimating accurately the state of a time-evolving complex system like the atmosphere from observational data and a numerical model of the system. It has become an indispensable tool for meteorological researches as well as for numerical weather prediction, as represented by extensive use of reanalysis datasets for research purposes. New advances of data assimilation methods emerged from the 1980s. This review paper presents the theoretical background and implementation of two advanced data assimilation methods: four-dimensional variational assimilation (4D-Var) and ensemble Kalman filtering (EnKF), which currently draw much attention in the meteorological community. Recent research results in Japan on those methods are reviewed, especially on mesoscale applications of 4D-Var and tests of the local ensemble transform Kalman filter (LETKF). Comparison of 4D-Var and EnKF is also briefly discussed. An outline of the mesoscale 4D-Var system of the Japan Meteorological Agency, which is the first operational 4D-Var for a mesoscale model, is given in Appendix.

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Data Assimilation of GPS Precipitable Water Vapor into the JMA Mesoscale Numerical Weather Prediction Model and its Impact on Rainfall Forecasts

Cited by 61 publications

References 6 publications

4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

A Study of Near Real-time Water Vapor Analysis Using a Nationwide Dense GPS Network of Japan

Recent Progress of Data Assimilation Methods in Meteorology

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