Grid Point Value Data Research Articles

A New Proposal for Vertical Extrapolation of Offshore Wind Speed and an Assessment of Offshore Wind Energy Potential for the Hibikinada Area, Kitakyushu, Japan

The author’s research group has been conducting research on applications of various meteorological Grid Point Value (GPV) data offered by the Japan Meteorological Agency (JMA) to the field of wind power generation. In particular, the group’s research has been focusing on the following areas: 1) the use of GPV data from the JMA Meso-Scale Model (MSM-S; horizontal resolution: 5 km) and the JMA Local Forecast Model (LFM-S; horizontal resolution: 2 km), and 2) examinations of the prediction accuracy of local wind assessment with the use of these data. In both the MSM-S and the LFM-S, grid points are fixed at 10 m above the sea (ground) surface. The purpose of the present study is to establish a method in which the values of the MSM-S and LFM-S wind speed data from the 10 m height are used as the reference wind speed and are, using a power law, vertically extrapolated to 80 to 90 m heights, typical hub-heights of offshore wind turbines. For this purpose, the present study examined time-averaged vertical profiles of wind speed over the ocean based on the MSM-S data and in-situ data in the Hibikinada area, Kitakyushu City, Fukuoka Prefecture, Japan. As a result, it was revealed that a strong wind shear existed close to the sea surface, between the 10 and 30 m heights. In order to address the above-mentioned wind shear, a two-step vertical extrapolation method was proposed in the present study. In this method, two values of N, specifically for low and high altitudes (below and above approximately 30 m, respectively), were calculated and used. The data were created for the five years between 2012 and 2016. Similarly to previous analyses, the analysis of the created data set indicated that the potential of offshore wind power generation in the Hibikinada area, Kitakyushu City is quite high.

High-Precision Forecasting Model of Solar Irradiance Based on Grid Point Value Data Analysis for an Efficient Photovoltaic System

An accurate forecasting system is extremely crucial in order to simulate an optimum output level of photovoltaic (PV) power production for the next day. In this study, a relatively high-precision model of solar irradiance forecasting based on grid point value (GPV) datasets using relative humidity, precipitation, and three-level cloud covers parameterization has been conducted in Hitachi and four main cities in Japan. In the case of cloudy/rainy/snowy days, the influence of liquid water path is further introduced to the model. As a result, correlation coefficient r of 0.94, 0.91, 0.91, 0.89, and 0.92 have been obtained using 21UTC forecast version in 2012 datasets for Hitachi, Tokyo, Nagoya, Osaka, and Fukuoka, respectively. Surprisingly, although the earlier forecast version, using 9UTC datasets, was later applied to the model, there was no significant change to the r for these five locations as their values reduced by only approximately 0.01 at most. Furthermore, a similar trend has also been observed for the 2013 datasets from a comparison of 21UTC and 9UTC versions, which highly supports the fact that this model is reliable, since it still remains in a high-precision state even in the case where the earlier datasets of previous day are used.

Investigation of Low-Cloud Characteristics Using Mesoscale Numerical Model Data for Improvement of Fog-Detection Performance by Satellite Remote Sensing

AbstractThe comprehensive relationship between meteorological conditions and whether low water cloud touches the surface, particularly at sea, is examined with the goal of improving low-cloud detection by satellite. Gridpoint-value data provided by an operational mesoscale model with integration of Multifunction Transport Satellite-2 data can provide sufficient data for statistical analyses to find general parameters that can discern whether low clouds touch the surface, compensating for uncertainty due to the scarcity of observation sites at sea and the infrequent incidence of fog. The analyses reveal that surface-touching low clouds tend to have lower cloud-top heights than those not touching the surface, although the frequency distribution of cloud-top height differs by season. The bottom of the Γ > Γm layer (where Γ and Γm are the vertical gradient and the moist-adiabatic lapse rate of the potential temperature, respectively) with surface-touching low-cloud layers tends to be very low or almost attached to the surface. In contrast, the tops of low-cloud layers not touching the surface tend to occur near the bottom of the Γ > Γm layer. Mechanisms to correlate these meteorological conditions with whether low clouds touch the surface are inferred from investigations into the vertical structure of equivalent potential temperature. These results indicate that the temperature difference between cloud-top height and the surface can be an appropriate parameter to infer whether low clouds touch the surface. It is also suggested that only a little addition of meteorological ancillary data, such as the forecast sea surface temperature, to satellite data allows successful performance of the discrimination.

Scheme for detection of low clouds from geostationary weather satellite imagery

A simple and practical scheme for low cloud detection at sea based on geostationary weather satellite data is proposed. The scheme consists of several threshold discrimination tests, and time-consuming procedures are eschewed in order to enable near-real-time analysis. This scheme also minimizes the use of data other than from geostationary weather satellites, leading to a convenient low cloud detection procedure without ancillary data. Careful investigation of the radiative properties of low water clouds from radiative transfer simulation and satellite observations enables full utilization of the characteristics of satellite data and realization of the simple scheme. The threshold values, which should possess high generality, for the discrimination tests are obtained from statistical comparisons of Multi-function Transport Satellite-2 data to grid point value data, which allows for extensive data collection and eliminates the localities and anomalies. Verification by comparisons with radiosonde and lidar on satellite suggests that results obtained from the proposed low cloud detection scheme are reasonable.

Global high-resolution simulations of CO<sub>2</sub> and CH<sub>4</sub> using a NIES transport model to produce a priori concentrations for use in satellite data retrievals

Abstract. The Greenhouse gases Observing SATellite (GOSAT) measures column-averaged dry air mole fractions of carbon dioxide and methane (XCO2 and XCH4, respectively). Since the launch of GOSAT, model-simulated three-dimensional concentrations from a National Institute for Environmental Studies offline tracer Transport Model (NIES TM) have been used as a priori concentration data for operational near real-time retrievals of XCO2 and XCH4 from GOSAT short-wavelength infrared spectra at NIES. Although the choice of a priori profile has only a minor effect on retrieved XCO2 or XCH4, a realistic simulation with minimal deviation from observed data is desirable. In this paper, we describe the newly developed version of NIES TM that has been adapted to provide global and near real-time concentrations of CO2 and CH4 using a high-resolution meteorological dataset, the Grid Point Value (GPV) prepared by the Japan Meteorological Agency. The spatial resolution of the NIES TM is set to 0.5° × 0.5° in the horizontal in order to utilise GPV data, which have a resolution of 0.5° × 0.5°, 21 pressure levels and a time interval of 3 h. GPV data are provided to the GOSAT processing system with a delay of several hours, and the near real-time model simulation produces a priori concentrations driven by diurnally varying meteorology. A priori variance–covariance matrices of CO2 and CH4 are also derived from the simulation outputs and observation-based reference data for each month of the year at a resolution of 0.5° × 0.5° and 21 pressure levels. Model performance is assessed by comparing simulation results with the GLOBALVIEW dataset and other observational data. The overall root-mean-square differences between model predictions and GLOBALVIEW analysis are estimated to be 1.45 ppm and 12.52 ppb for CO2 and CH4, respectively, and the seasonal correlation coefficients are 0.87 for CO2 and 0.53 for CH4. The model showed good performance particularly at oceanic and free tropospheric sites. The high-resolution model also performs well in reproducing both the observed synoptic variations at some sites and stratospheric profiles over Japan. These results give us confidence that the performance of our GPV-forced high-resolution NIES TM is adequate for use in satellite retrievals.

定期便フライトデータと気象庁数値予報GPVデータとの比較評価

Flight trajectory optimization as well as trajectory based operations is considered as a key concept of future air traffic control system. Now, one of the significant subjects is to clarify the accuracy of numerical weather data such as wind speed or wind direction, which is used for calculation of flight trajectory optimization and trajectory prediction. In this study, the accuracy evaluation of numerical prediction GPV (Grid Point Value) data published from JMA (Japan Meteorological Agency) is assumed to be possible by comparing with flight data provided from an airline company. Accuracy of static air temperature and wind speed of GSM (Global Spectral Model) and MSM (Meso-Scale Model) weather data are evaluated and measured by RMSE (Root Mean Square Error). The analysis shows that temperature and wind RMSE of GSM and MSM initial time data are less than 2 degrees and 5 m/s respectively. It is also revealed that forecast data of GSM provides greater precision than MSM by referring to the obtained RMSE.

On meteotsunamis around Tsushima Strait generated by the Baiu front

On July 15, 2009, a meteotsunami occurred over Tsushima Strait and flood damage was recorded on the west coast of Tsushima Island. This study investigated the meteorological systems related to that meteotsunami event using surface observation data, grid-point value data, and a mesoscale meteorological model. The mid-latitude trough in the mid-troposphere approached the moist air of the Baiu precipitation system, and a cold downdraft occurred over a wide area of western Japan. In the western Tsushima Strait, the warm, moist air of the lower atmosphere kept the front of the cold downdraft stationary. Around this area, an atmospheric gravity wave was generated continuously with the wave-conditional instability of the second kind mechanism and propagated toward Tsushima Island. The atmospheric gravity wave generated a pressure disturbance at sea level, which produced the meteotsunami. The frequency of the pressure disturbance using the numerical model had a peak in the 5- to 20-min period range, which corresponded with the peak frequency range of the observed tidal records.

海上風観測値と気象庁ＧＰＶを用いた有明海における波浪推算精度向上の検討

It is important to estimate offshore winds with high accuracy in hindcasting ocean waves. In the open ocean areas, offshore winds have been estimated by using gradient wind which can be calculated with the sea surface pressure distribution. But in the inland sea areas, the offshore winds are more complicated, because they are affected by surrounding land topography. This study reveals that accuracy of offshore wind estimation and wave hindcast in inland seas such as Ariake Sea can be improved by using GPV (Grid Point Value) data presented by Japan Meteorological Agency and observed wind data.

470大規模風場推定における気象衛星雲画像からの雲移動ベクトルの利用可能性の検討

Now, the acid rain has become one of the environmental problems of global scales. In many cases, the cause substances of the acid rain are transported through a long distance. Therefore, the acid rain is not considered as domestic problems only. The long distance transportation model has been developed as one technique for solving the long distance transportation problems of the cause substances of such acidifying. In this model, presumption of a wind field is the most important factor.In order to apply this model to the East Asia area, we tried the presumption of the wind field using the cloud picture obtained from the GMS (Geostational Meteorological Satellite, Himawari by name.) We verified the accuracy of this wind field presumption technique by comparing mutually between the presumed technique of this wind field, the wind vector obtained by the Meteorological Agency Japan, upper-layers weather data, and the GPV (Grid Point Value) data.

Short‐term rainfall prediction method using a volume scanning radar and grid point value data from numerical weather prediction

A physically based short‐term rainfall prediction method, which uses a volume scanning radar, is extended so that it utilizes grid point values from a numerical weather prediction model as supplementary information. The original short‐term prediction method mainly consists of a conceptual rainfall model that can predict rainfall distribution, particularly over mountainous regions, in a qualitative sense. On the other hand, the grid point values from the numerical weather prediction model, the Japan Spectral Model developed by the Japan Meteorological Agency, are operationally distributed as the grid point value (GPV) data. In the original short‐term prediction method the three‐dimensional wind field as well as initial distributions of the air temperature and water vapor were identified using topography and upper air observations. In the extended method, however, in identifying those initial values, the information from the GPV data is used instead of the upper air observations in order to accommodate large differences in temporal and spatial resolution between radar information and upper air observations. It is noted that this extended method does not use predicted GPV rainfall data. The conceptual rainfall model plays the role of bridging the gap between radar information and numerical weather prediction scales. This extended method is applied to a rainfall event which occurred in the bai‐u season (one of the rainy seasons of Japan) in July 1994. Results show that for the extended lead time of three and four hours, prediction of the expanding rainfall area was improved.