Myoung-Hwan Ahn

A new algorithm for sea fog/stratus detection using GMS-5 IR data

A new algorithm for the detection of fog/stratus over the ocean from the GMS-5 infrared (IR) channel data is presented. The new algorithm uses a clear-sky radiance composite map (CSCM) to compare the hourly observations of the IR radiance. The feasibility of the simple comparison is justified by the theoretical simulations of the fog effect on the measured radiance using a radiative transfer model. The simulation results show that the presence of fog can be detected provided the visibility is worse than 1 km and the background clear-sky radiances are accurate enough with known uncertainties. For the current study, an accurate CSCM is constructed using a modified spatial and temporal coherence method, which takes advantage of the high temporal resolution of the GMS-5 observations. The new algorithm is applied for the period of 10–12 May 1999, when heavy sea fog formed near the southwest coast of the Korean Peninsula. Comparisons of the fog/stratus index, defined as the difference between the measured and clear-sky brightness temperature, from the new algorithm to the results from other methods, such as the dual channel difference of NOAA/AVHRR and the earth albedo method, show a good agreement. The fog/stratus index also compares favorably with the ground observations of visibility and relative humidity. The general characteristics of the fog/stratus index and visibility are relatively well matched, although the relationship among the absolute values, the fog/stratus index, visibility, and relative humidity, varies with time. This variation is thought to be due to the variation of the atmospheric conditions and the characteristics of fog/stratus, which affect the derived fog/stratus index.

An exploratory study of cloud remote sensing capabilities of the Communication, Ocean and Meteorological Satellite (COMS) imagery

The present study documents optimal methods for the retrieval of cloud properties using five channels (0.6, 3.7, 6.7, 10.8 and 12.0 µm) that are used in many geostationary meteorological satellite observations. Those channels are also to be adopted for the Communication, Ocean and Meteorological Satellite (COMS) scheduled to be launched in 2008. The cloud properties focused on are cloud thermodynamic phase, cloud optical thickness, effective particle radius and cloud‐top properties with specific uncertainties. Discrete ordinate radiative transfer models are simulated to build up the retrieval algorithm. The cloud observations derived from the Moderate‐resolution Imaging Spectroradiometer (MODIS) are compared with the results to assess the validity of the algorithm. The preliminary validation indicates that the additional use of a band at 6.7 µm would be better in discriminating the cloud ice phase. Cloud optical thickness and effective particle radius can also be produced up to, respectively, 64 and 32 µm by functionally eliminating both ground‐reflected and cloud‐ and ground‐thermal radiation components at 0.6 and 3.7 µm. Cloud‐top temperature (pressure) in ±3 K (±50 hPa) uncertainties can be estimated by a simple 10.8‐µm method for opaque clouds, and by an infrared ratioing method using 6.7 and 10.8 µm for semitransparent clouds.

Use of Numerical Forecasts for Improving TMI Rain Retrievals over the Mountainous Area in Korea

Abstract 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 temperat...

Operational implementation of the ATOVS processing procedure in KMA and its validation

The Korea Meteorological Administration (KMA) has processed the data from the advanced TOVS (ATOVS) onboard NOAA-16 satellite since May 2001. The operational production utilizes the AAPP (ATOVS and AVHRR Processing Package) of EUMETSAT and IAPP (International ATOVS Processing Package) of the University of Wisconsin. For the initial guess profiles, the predicted fields (usually 6 to 12 hour forecasted fields) from the global aviation model of NOAA/NCEP are used. The average number of profiles retrieved from the ATOVS data is about 1,300 for each morning and afternoon orbit at about 18 and 06 UTC, respectively. The retrieved temperature and dew point temperatures are provided to forecasters in real time and used for initialization of prediction models. With the advanced microwave sensor (AMSU; Advanced Microwave Sounding Unit), accuracy of the ATOVS products is expected to be better than that of the TOVS products, especially in cloudy conditions. Indeed, the preliminary results from a validation study with the collocated radiosonde data during a 8-month period, from May to December 2001, for the East Asia region show an improved accuracy of the ATOVS products for cloudy skies versus the TOVS, especially for higher altitudes. The RMS (Root Mean Square) difference between the ATOVS products and radiosonde data is about 1.3°C for both clear and cloudy conditions, except for near the ground and at higher altitudes, at around 200 hPa. There is no significant temporal variation of the error statistics at all pressure levels. In case of the water vapor mixing ratio, the largest difference is shown at lower altitudes, while the accuracy is much better for the clear sky cases than the cloudy sky cases. The bias and RMSE at lower altitudes is about 0.557 g kg−1 and 2.5 g kg−1 and decrease significantly with increasing altitude.

Evaluating the calibration of MTSAT-1R infrared channels using collocated Terra MODIS measurements

The calibration of four MTSAT‐1R infrared channels was evaluated by comparing MTSAT measurements with Terra/MODIS inferred MTSAT‐equivalent brightness temperatures during August 2005 and August 2006. Theoretical relationships converting MODIS brightness temperatures to MTSAT‐equivalent values were obtained and used for the comparison. Results indicate that MTSAT two split window channels are well calibrated, and no serious systematic errors or biases are found; and the MTSAT water‐vapour channel shows a good linear relationship but with a warm bias up to 2 K. The significant cold bias of MTSAT 3.7 µm channel about −6.7 K in August 2005 is found to be much removed in August 2006, after correction of the electrical crosstalk between MTSAT‐1R SWIR channel and WV channel starting from March 2006. Since then, calibration performances of MTSAT‐1R split window channels and shortwave IR channel seem to be comparable with MODIS calibration, while the water‐vapour channel shows more uncertainties up to 2 K of bias.

Interactions between aerosol absorption, thermodynamics, dynamics, and microphysics and their impacts on a multiple-cloud system

This study investigates how the increasing concentration of black carbon aerosols, which act as radiation absorbers as well as agents for the cloud-particle nucleation, affects stability, dynamics and microphysics in a multiple-cloud system using simulations. Simulations show that despite increases in stability due to increasing concentrations of black carbon aerosols, there are increases in the averaged updraft mass fluxes (over the whole simulation domain and period). This is because aerosol-enhanced evaporative cooling intensifies convergence near the surface. This increase in the intensity of convergence induces an increase in the frequency of updrafts with the low range of speeds, leading to the increase in the averaged updraft mass fluxes. The increase in the frequency of updrafts induces that in the number of condensation entities and this leads to more condensation and cloud liquid that acts to be a source of the accretion of cloud liquid by precipitation. Hence, eventually, there is more accretion that offsets suppressed autoconversion, which results in negligible changes in cumulative precipitation as aerosol concentrations increase. The increase in the frequency of updrafts with the low range of speeds alters the cloud-system organization (represented by cloud-depth spatiotemporal distributions and cloud-cell population) by supporting more low-depth clouds. The altered organization in turn alters precipitation spatiotemporal distributions by generating more weak precipitation events. Aerosol-induced reduction in solar radiation that reaches the surface induces more occurrences of small-value surface heat fluxes, which in turn supports the more low-depth clouds and weak precipitation together with the greater occurrence of low-speed updrafts.

Application of an artificial neural network for a direct estimation of atmospheric instability from a next-generation imager

Atmospheric instability information derived from satellites plays an important role in short-term weather forecasting, especially the forecasting of severe convective storms. For the next generation of weather satellites for Korea’s multi-purpose geostationary satellite program, a new imaging instrument has been developed. Although this imaging instrument is not designed to perform full sounding missions and its capability is limited, its multi-spectral infrared channels provide information on vertical sounding. To take full advantage of the observation data from the much improved spatiotemporal resolution of the imager, the feasibility of an artificial neural network approach for the derivation of the atmospheric instability is investigated. The multi-layer perceptron model with a feed-forward and back-propagation training algorithm shows quite a sensitive response to the selection of the training dataset and model architecture. Through an extensive performance test with a carefully selected training dataset of 7197 independent profiles, the model architectures are selected to be 12, 5000, and 0.3 for the number of hidden nodes, number of epochs, and learning rate, respectively. The selected model gives a mean absolute error, RMSE, and correlation coefficient of 330 J kg-1, 420 J kg-1, and 0.9, respectively. The feasibility is further demonstrated via application of the model to real observation data from a similar instrument that has comparable observation channels with the planned imager.

Intercomparison of Shortwave Radiative Transfer Models for a Rayleigh Atmosphere

Intercomparison between eight radiative transfer codes used for the studies of COMS (Communications, Ocean, and Meteorological Satellite) in Korea was performed under pure molecular, i.e., Rayleigh atmospheres in four shortwave fluxes: 1) direct solar irradiance at the surface, 2) diffuse irradiance at the surface, 3) diffuse upward flux at the surface, and 4) diffuse upward flux at the top of the atmosphere. The result (hereafter called the H15) from Halthore et al.`s study (2005) which intercompared and averaged 15 codes was used as a benchmark to examine the COMS models. Uncertainty of the seven COMS models except STREAMER was with respect to the H15, comparable with of Halthore et al.`s (2005). The uncertainty increased under a large

Estimation of uncertainties in the spectral response function of the water vapor channel of a meteorological imager

SZA

Inter-comparison of the Infrared Channels of the Meteorological Imager Onboard COMS and Hyperspectral IASI Data

The five channel meteorological imager (MI) on-board the geostationary Communication, Ocean, and Meteorological Satellite (COMS) of Korea has been operationally used since April 2011. For a better utilization of the MI data, a rigorous characterization of the four infrared channel data has been conducted using the GSICS (Global Space-based Inter-Calibration System) approach with the IASI (Infrared Atmospheric Sounding Interferometer) on-board the European Metop satellite as the reference instrument. Although all four channels show the uncertainty characteristics that are in line with the results from both the ground tests and the in-orbit-test, there shows an unexpected systematic bias in the water vapor channel of MI, showing a cold bias at the warm target temperature and a warm bias with the cold target temperature. It has been shown that this kind of systematic bias could be introduced by the uncertainties in the spectral response function (SRF) of the specific channel which is similar to the heritage instruments on-board GOES series satellite. An extensive radiative transfer simulation using a radiative transfer model has confirmed that the SRF uncertainty could indeed introduce such a systematic bias. By using the collocated data set consisting of the MI data and the hyperspectral IASI data, the first order correction value for the SRF uncertainty is estimated to be about 2.79 cm-1 shift of the central position of the current SRF.