Abstract-Falling snow is an important component of global precipitation in extratropical regions. This paper describes the methodology and results of physically based retrievals of snow falling over land surfaces. Because microwave brightness temperatures emitted by snow-covered surfaces are highly variable, precipitating snow above such surfaces is difficult to observe using window channels that occur at low frequencies ( 100 GHz). Furthermore, at frequencies 37 GHz, sensitivity to liquid hydrometeors is dominant. These problems are mitigated at high frequencies ( 100 GHz) where water vapor screens the surface emission, and sensitivity to frozen hydrometeors is significant. However, the scattering effect of snowfall in the atmosphere at those higher frequencies is also impacted by water vapor in the upper atmosphere. The theory of scattering by randomly oriented dry snow particles at high microwave frequencies appears to be better described by regarding snow as a concatenation of "equivalent" ice spheres rather than as a sphere with the effective dielectric constant of an air-ice mixture. An equivalent sphere snow scattering model was validated against high-frequency attenuation measurements. Satellite-based high-frequency observations from an Advanced Microwave Sounding Unit (AMSU-B) instrument during the March 5-6, 2001 New England blizzard were used to retrieve snowfall over land. Vertical distributions of snow, temperature, and relative humidity profiles were derived from the Mesoscale Model (MM5) cloud model. Those data were applied and modified in a radiative transfer model that derived brightness temperatures consistent with the AMSU-B observations. The retrieved snowfall distribution was validated with radar reflectivity measurements obtained from a ground-based radar network.

This study seeks to evaluate the impact of several newly available sources of meteorological data on mesoscale model forecasts of the extratropical cyclone that struck Florida on 2 February 1998. Intermittent measurements of precipitation and integrated water vapor (IWV) distributions were obtained from Special Sensor Microwave/ Imager (SSM/I) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations. The TMI also provided sea surface temperatures (SSTs) with structural detail of the Loop Current and Gulf Stream. Continuous lightning distributions were measured with a network of very low frequency radio receivers. Lightning data were tuned with intermittent spaceborne microwave radiometer data through a probability matching technique to continuously estimate convective rainfall rates. A series of experiments were undertaken to evaluate the effect of those data on mesoscale model forecasts produced after assimilating processed rainfall and IWV for 6 h. Assimilating processed rainfall, IWV, and SSTs from TMI measurements in the model yielded improved forecasts of precipitation distributions and vertical motion fields. Assimilating those data also produced an improved 9-h forecast of the radar reflectivity cross section that was validated with a coincident observation from the TRMM spaceborne precipitation radar. Sensitivity experiments showed that processed rainfall information had greater impact on the rainfall forecast than IWV and SST information. Assimilating latent heating in the correct location of the forecast model was found to be more important than an accurate determination of the rainfall intensity.

In this study, we present an improved physical model to retrieve snowfall rate over land using brightness temperature observations from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Microwave Sounder Unit-B (AMSU-B) at 89 GHz, 150 GHz, 183.3±1 GHz, 183.3±3 GHz, and 183.3±7 GHz. The retrieval model is applied to the New England blizzard of March 5, 2001 which deposited about 75 cm of snow over much of Vermont, New Hampshire, and northern New York.In this improved physical model, prior retrieval assumptions about snowflake shape, particle size distributions, environmental conditions, and optimization methodology have been updated. Here, single scattering parameters for snow particles are calculated with the Discrete-Dipole Approximation (DDA) method instead of assuming spherical shapes.Five different snow particle models (hexagonal columns, hexagonal plates, and three different kinds of aggregates) are considered. Snow particle size distributions are assumed to vary with air temperature and to follow aircraft measurements described by previous studies.Brightness temperatures at AMSU-B frequencies for the New England blizzard are calculated using these DDA calculated single scattering parameters and particle size distributions. The vertical profiles of pressure, temperature, relative humidity and hydrometeors are provided by MM5 model simulations. These profiles are treated as the a priori data base in the Bayesian retrieval algorithm. In algorithm applications to the blizzard data, calculated brightness temperatures associated with selected database profiles agree with AMSU-B observations to within about ±5 K at all five frequencies.Retrieved snowfall rates compare favorably with the near-concurrent National Weather Service (NWS) radar reflectivity measurements. The relationships between the NWS radar measured reflectivities Z e and retrieved snowfall rate R for a given snow particle model are derived by a histogram matching technique. All of these Z e -R relationships fall in the range of previously established Z e -R relationships for snowfall. This suggests that the current physical model developed in this study can reliably estimate the snowfall rate over land using the AMSU-B measured brightness temperatures.1

Abstract. In this study, optimal parameter estimations are performed for both physical and computational parameters in a mesoscale meteorological model, and their impacts on the quantitative precipitation forecasting (QPF) are assessed for a heavy rainfall case occurred at the Korean Peninsula in June 2005. Experiments are carried out using the PSU/NCAR MM5 model and the genetic algorithm (GA) for two parameters: the reduction rate of the convective available potential energy in the Kain-Fritsch (KF) scheme for cumulus parameterization, and the Asselin filter parameter for numerical stability. The fitness function is defined based on a QPF skill score. It turns out that each optimized parameter significantly improves the QPF skill. Such improvement is maximized when the two optimized parameters are used simultaneously. Our results indicate that optimizations of computational parameters as well as physical parameters and their adequate applications are essential in improving model performance.

Topographical influences on the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain retrievals over the terrain area of the Korean peninsula were examined using a training dataset constructed from numerical mesoscale model simulations in conjunction with radiative transfer calculations. By relating numerical model outputs to rain retrievals from simulated brightness temperatures, a positive relationship between topographically forced vertical motion and rain retrievals in the upstream region over the mountainous area was found. Based on the relationship obtained, three topographical correction methods were developed by incorporating slope-forced vertical motion and its associated upward vapor flux, and vapor flux convergence in the surface boundary layer into a scattering-based TMI rain retrieval algorithm. The developed correction methods were then applied for the rain retrievals from simulated TMI brightness temperatures with model outputs and measured TMI brightness temperatures. Results showed that orographic influences on the rain formation can be included in the TMI rainfall algorithms, which tend to underestimate rainfall over the complex terrain area. It was shown that topographical corrections surely improve the rain retrieval when a strong rain event is present over the upslope region. Among various elements, moisture convergence in the boundary layer appears to be an important factor needed in the topographical correction. Overall topography-corrected estimates of rainfall showed a better agreement with ground measurements than those without correction, suggesting that satellite rain retrieval over the terrain area can be improved when accurate numerical forecast outputs are incorporated into the rain retrieval algorithm.

The MAPLE system has been implemented in real-time in Korea since June 2008, producing forecasts up to 6 hours every 10 minutes. An object-oriented verification method has been applied for the summer season (June-July-August) over the Korean Peninsula to evaluate and understand the characteristics of the forecast results. The CRA (contiguous rain area) approach is used to decompose the total error into the different error components; location, pattern, and volume errors. The mean displacement error is smaller than 20 km up to the 3-h forecasts and increases with forecast time. The ratio between the displacement (location) error and the total error is less than 7% even for a 3-h forecast. This result indicates that MAPLE produces reliable forecast in terms of precipitation location. However, the pattern error is larger than 90% of the total error. Contingency scores that are defined with different categories of rain intensity and displacement errors show the outstanding performance up to 2.5 hours. MAPLE overpredicts rain areas with the threshold of 1 mm h −1 rain intensity throughout forecast periods. However, the heavy rainfall events are poorly predicted due to the inherent limitation of extrapolation-based nowcasting technique.

In this study, fogs are classified based on the spatial and temporal characteristics over South Korea using the visibility data and the empirical orthogonal function (EOF) and wavelet analyses. With fog defined in terms of visibility (<1 km), the EOF analysis is performed to extract spatial distribution characteristics via dimension reduction, whereas the space‐time wavelet expansion is applied to the EOF time series to specify the fog characteristics in the space of time versus scale (i.e., period in this study). The first EOF mode occupies 48.9% of total variance and shows the fog distribution covering almost entire areas of South Korea with one sign (+), except at the eastern coast and western part of the southern coast. The wavelet analysis reveals that this fog occurs based on meteorological conditions of various scales from daily to seasonal, thus classified as mixed fog. The second EOF mode, which occupies 19.5% of total variance, shows distinct separation of spatial distribution of fog, with a negative (−) sign in winter over northwestern coastal/inland, western coastal, and south central mountain areas of South Korea and a positive (+) sign in other seasons elsewhere. With cycles of 1–2 weeks and 1–2 months being dominant in the wavelet analysis, this fog is considered to be strongly affected by synoptic scale weather systems and monsoon. Fog over the positive area is mostly affected by monsoon and/or cyclonic frontal systems, thus classified as frontal fog, whereas that over the negative area is affected by the cold‐core anticyclones moving over warm sea surface in winter or by radiative cooling, thus classified as steam fog (coastal/sea) or radiation fog (inland), respectively. The mountain area may have upslope fog because of orographic lifting. The third EOF mode, occupying 6.7% of total variance, depicts distinct spatial separation of fog distribution around the coastal areas with a negative (−) sign and in the inland areas with a positive (+) sign. The former, with a dominant 1–2 week cycle, is classified as sea fog affected by migratory anticyclones and monsoon in late spring and summer, while the latter, with a dominant diurnal variation, represents radiation fog under clear sky in autumn. It turns out that the combined EOF and wavelet analyses are useful to assess the detailed spatial and temporal characteristics of various types of fog occurrence in South Korea.

The THORPEX-Pacific Asian Regional Campaign 2008 (T-PARC 2008) was performed during the period of August 1 through October 4, 2008, and mainly focused on the genesis, intensification, recurvature, and extra-tropical transition over the western North Pacific in collaboration with TCS-08 and DOTSTAR. This study investigates the impact of dropsonde observations on the improvement of predictive skills for Typhoon Sinlaku (0813) and Jangmi (0815) during T-PARC 2008. Twelve and six cases were selected for Sinlaku and Jangmi, respectively. The dropsonde data were assimilated by the Weather Research and Forecasting (WRF)-Three-Dimensional Variational system (3DVAR), and then the typhoon track was obtained by running a WRF model for up to 72 hours. Consequently, the assimilation of the dropsonde data had positive impacts on the typhoon track forecast and lead to mean track error reductions of 22.5% and 17.0% for Typhoon Sinlaku and Jangmi, respectively. Subsequent experiments were also conducted to determine the sensitivities of storm activity in the horizontal and vertical distributions and the dynamic and thermodynamic variables using the dropsonde data. The results show that sondes released south of storms around the middle troposphere (500~850 hPa) are more effective in improving the track forecast. The dynamic variables mainly affect the storm tracks, while the thermodynamic variables mainly affect the central pressure of the storm.