Papers in Meteorology and Geophysics Vol. 65(2014)

   Radiosonde observations were conducted at Tsukuba in the warm seasons of 2011 and 2012 as a part of the research program Tokyo Metropolitan Area Convection Study for Extreme-Weather-Resilient Cities (TOMACS). The Aerological Observation Simulation (AOS) program, a system for simulating radiosonde track, was utilized in the observation for predicting the radiosonde landing area. The AOS program performance was validated based on the landing location data in operational radiosonde observations. Landing locations predicted by AOS were generally well correlated with those observed. The mean difference in landing location between simulation and observation was 16 km for 728 cases in the warm seasons of the past seven years. The AOS program adopted a Monte-Carlo method to account for uncertainties in (1) horizontal wind forecast, (2) radiosonde ascending speed, (3) pressure level reached, and (4) descending speed. This enables predicting the probability ellipse around the most likely landing location, which represents the possible landing area with a certain probability. Seventy-five percent of the observed landing locations were inside the 70% probability ellipse. Approximately 90% of the observed landing locations were inside the 90% probability ellipse. This means that the 90% probability ellipse is a reliable index of the landing area in the operational observations. However, 96% of the observed landing locations were inside the 99% probability ellipse, falling slightly short of the expected probability. The higher-than-expected rate of landing in the 70% probability ellipse was closely related to the performance of the horizontal wind forecast in the numerical weather prediction model. AOS prediction accuracy was lower on rainy days for multiple causes.
   The AOS prediction was applied to the radiosonde observations in the 2011 and 2012 TOMACS Intensive Observation Period (IOP). In these observations, a 200 g balloon was chosen, and the heights reached were lower than those of operational observations. This reduced influences of summertime easterly wind in the lower stratosphere. Parameters used in the AOS probabilistic distribution (radiosonde ascending speed, pressure level reached, and descending speed for 200 g balloons) were revised using data obtained in the present observations. Parameter revision improved AOS predictions for 200 g balloons.

   Future hydroclimate projections for Central America and the Caribbean were investigated with quantified uncertainties using 20-km and 60-km mesh global atmospheric general circulation models. In these regions, only a few future climate projections with high horizontal resolutions are available, although Central America and the Caribbean are characterized by spatial and temporal complexities in climate. Horizontal resolutions of 20 km and 60 km are comparable to those of regional climate models for a large region. Both the 20-km and 60-km mesh models reproduced reasonably well the observed seasonal precipitation patterns. Precipitation was projected to decrease in most of this region in all seasons by the end of this century. Evaporation from the ocean was projected to increase throughout the year, except in the Intertropical Convergence Zone, whereas evaporation from land areas was generally projected to decrease in the dry season and to increase in the rainy season. Surface soil moisture and total runoff in most land areas were therefore projected to decrease in both models in all seasons. Annual mean streamflow in the future climate was projected to decrease in most of Central America and the Caribbean as a result of decreased precipitation and increased evaporation. The values of hydroclimate variables over four land-only domains in the future climate changed significantly on a monthly basis within each season. In contrast, changes in the annual means of hydroclimate variables for individual countries were highly uncertain.

   Estimation of tropical cyclone (TC) intensity, that is, minimum sea level pressure (MSLP) and maximum sustained wind speed, by using satellite data is important for disaster prevention and mitigation, especially where in situ data are sparse, such as over the ocean, but it is still a challenging issue. For decades, the Dvorak technique has been the principal satellite-based method used for TC intensity estimation at the Japan Meteorological Agency and other forecast centers. However, the Dvorak technique requires specification of the TC cloud pattern, and estimation of TC intensity from the pattern is subjective and empirical. This study developed a new MSLP estimation method that uses 55-GHz band brightness temperatures (TBs) observed by the Advanced Microwave Sounding Unit-A (AMSU-A). This method is based on a regression between TC warm core intensities obtained from AMSU-A TBs and MSLPs in the best-track data archived by the Regional Specialized Meteorological Center (RSMC) Tokyo - Typhoon Center for TCs that occurred during the 2008 TC season in the western North Pacific basin. The TC warm core intensities were corrected to reduce the possible errors, such as those due to AMSU-A coarse spatial resolution and TB attenuation caused by ice cloud and rain particles. The MSLPs for TCs during 2009-2011 were then estimated by using this new method and validated against the best-track MSLPs. In the validation results, the root mean square error was 10.1 hPa, and the bias was 0.3 hPa. The MSLP estimation error was within ±5 hPa for 51.0% of the total number of observations, and within ±10 hPa for 79.3% of the total. This new method has advantages over the Dvorak technique for estimating the intensities of TCs with a relatively large warm core and some specific cloud patterns.

Estimation of tropical cyclone (TC) intensity by using satellite observations is essential for operational TC warnings in the western North Pacific basin where reconnaissance aircraft observations are not conducted. The Japan Meteorological Agency (JMA) typically uses a method of estimating the maximum wind speed of a TC based on the brightness temperature at the 10, 19, 21, 37, and 85-GHz channels of the TRMM Microwave Imager (TMI). In the original method, parameters for concentric circles and annular regions within 2° latitude from the TC center were calculated to represent the TC structure. To improve the estimation, parameters for the four quadrants on the forward, backward, left, and right sides of the TC center relative to the TC motion were added to represent asymmetric components of the TC structure. These parameters were calculated for TC cases from 1998 through 2008 in the western North Pacific basin, and k-means clustering was applied to the parameters to classify the TC cases into 10 clusters. Then a regression equation for the estimation of the TC intensity was computed for each cluster. Several selected parameters and the maximum wind speed in the best track data of the JMA were set for the explanatory variables and the explained variable, respectively, for each regression equation.
The improved estimation method, based on the TC cases from 1998 through 2008, was applied to TCs from 2009 through 2012 to validate the estimation of maximum wind speed. The root mean square error derived from all validated cases was 6.26 m s^-1. The estimates in the clusters for TCs that had relatively asymmetric structures were mostly improved in comparison with those based on the original estimation method. However, in several clusters for TCs that had relatively symmetric structure, the estimation errors were larger than those of the original method. This suggests that the improvement of the estimation method in the present study is limited because of several factors, including the fact that the TC maximum wind speed in the best track data is mainly based on the Dvorak analysis and thus has a certain amount of error, as well as the uncertainty of the TC position determined by using interpolation of the 6-hourly best track data.

The Japan Meteorological Agency Regional Atmospheric Transport Model (JMA-RATM, previously called the Mesoscale Tracer Transport Model) that is used operationally for the Volcanic Ash Fall Forecast has been revised. Major improvements of the JMA-RATM are as follows: (i) For the initial condition of the eruption column model, the time-series variation of eruption cloud echo height data observed by weather radars is used instead of visual camera observation. (ii) For the input meteorological field, the grid point values of the Local Forecast Model (LFM, 2 km grid spacing and 60 vertical layers) are available instead of the Mesoscale Model (MSM, 5 km spacing and 50 layers); both models originate from the JMA Nonhydrostatic Model (JMA-NHM). (iii) In the atmospheric transport model calculations, Suzuki's resistance law is extended with the Cunningham slip correction, and rainout (in-cloud scavenging) and washout (below-cloud scavenging) processes by snow and graupel are incorporated in addition to rain. The target of the model predictions is tephra fall, which includes both ash fall quantity and lapilli fall area. Comparative calculations with the JMA-RATM were conducted for the lapilli fall event during the eruptions of Shinmoe-dake volcano on 26-27 January, 14 February, 13 March and 18 April 2011. The use of the time-series data of eruption cloud echoes and the LFM grid point values was effective for predictions of both ash fall quantity and lapilli fall area. The Cunningham slip correction had a marginal effect on the ash fall prediction. In-cloud and below-cloud scavenging processes had a large influence on the ash fall prediction; therefore, the scavenging rate will need to be calibrated against ash fall observation data in rain or snow. The values for the density and form of tephra, based on observation data, also had a large impact on the lapilli fall prediction; however, the occurrence of undetected error requires future research on the effect of wind in the eruption column model.