Total precipitable water (TPW), a column of water vapor content in the atmosphere, provides information on the spatial distribution of moisture. The high-resolution TPW, together with atmospheric stability indices such as convective available potential energy (CAPE), is an effective indicator of severe weather phenomena in the pre-convective atmospheric condition. With the advent of high performing imaging instrument onboard geostationary satellites such as Advanced Himawari Imager (AHI) onboard Himawari-8 of Japan and Advanced Meteorological Imager (AMI) onboard GeoKompsat-2A of Korea, it is expected that unprecedented spatiotemporal resolution data (e.g., AMI plans to provide 2 km resolution data at every 2 min over the northeast part of East Asia) will be provided. To derive TPW from such high-resolution data in a timely fashion, an efficient algorithm is highly required. Here, machine learning approaches—random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN)—are assessed for the TPW retrieved from AHI over the clear sky in Northeast Asia area. For the training dataset, the nine infrared brightness temperatures (BT) of AHI (BT8 to 16 centered at 6.2, 6.9, 7.3, 8.6, 9.6, 10.4, 11.2, 12.4, and 13.3 $\mathsf{\mu }\mathrm{m}$ , respectively), six dual channel differences and observation conditions such as time, latitude, longitude, and satellite zenith angle for two years (September 2016 to August 2018) are used. The corresponding TPW is prepared by integrating the water vapor profiles from InterimEuropean Centre for Medium-Range Weather Forecasts Re-Analysis data (ERA-Interim). The algorithm performances are assessed using the ERA-Interim and radiosonde observations (RAOB) as the reference data. The results show that the DNN model performs better than RF and XGB with a correlation coefficient of 0.96, a mean bias of 0.90 mm, and a root mean square error (RMSE) of 4.65 mm when compared to the ERA-Interim. Similarly, DNN results in a correlation coefficient of 0.95, a mean bias of 1.25 mm, and an RMSE of 5.03 mm when compared to RAOB. Contributing variables to retrieve the TPW in each model and the spatial and temporal analysis of the retrieved TPW are carefully examined and discussed.

The Geostationary Operational Environmental Satellite‐R (GOES‐R) series started a new era for the U.S. geostationary satellite observing system. The Advanced Baseline Imager (ABI) onboard the GOES‐R series has fine temporal (30 s to 10 min) and spatial resolutions (0.5–2 km), and 16 spectral bands. However, due to the lack of an infrared sounder, the ABI is used to continue the legacy atmospheric profile (LAP) products that the previous GOES Sounder has, including the legacy atmospheric moisture profile, legacy atmospheric temperature profile, total precipitable water, layered precipitable water, and derived atmospheric stability indices. The ABI LAP retrieval algorithms have been developed under the GOES‐R series Algorithm Working Group (AWG) program funded by the GOES‐R Program Office. The LAP products from GOES‐16 have been operational and validated with a series of reference data sets including radiosonde observations, the Global Positioning System from SuomiNet, the Advanced Microwave Scanning Radiometer 2 total precipitable water measurements, as well as global operational analysis from National Oceanic and Atmospheric Administration and European Centre for Medium‐Range Weather Forecasts models, for almost a year (from 2017 to 2018) to assure the data quality for applications. In addition, the LAP products have been successfully demonstrated at the Hazardous Weather Testbed experiments in the summer of 2017 and the spring of 2018. Both validation results and Hazardous Weather Testbed demonstrations indicate that the GOES‐R series LAP products meet the product requirements and provide added value over NWP short‐range forecasts, especially for middle‐upper tropospheric moisture, in situation awareness and nowcasting.

In preparation for the 2nd geostationary multi-purpose satellite of Korea with a 16-channel Advanced Meteorological Imager; an algorithm has been developed to retrieve clear-sky vertical profiles of temperature (T) and humidity (Q) based on a nonlinear optimal estimation method. The performance and characteristics of the algorithm have been evaluated using the measured data of the Advanced Himawari Imager (AHI) on board the Himawari-8 of Japan, launched in 2014. Constraints for the optimal estimation solution are provided by the forecasted T and Q profiles from a global numerical weather prediction model and their error covariance. Although the information contents for temperature is quite low due to the limited number of channels used in the retrieval; the study reveals that useful moisture information (2~3 degrees of freedom for signal) is provided from the three water vapor channels; contributing to the increase in the moisture retrieval accuracy upon the model forecast. The improvements are consistent throughout the tropospheric atmosphere with almost zero mean bias and 9% (relative humidity) of root mean square error between 100 and 1000 hPa when compared with the quality-controlled radiosonde data from 2016 August.

Observations from geostationary (GEO) satellites with high-spatial and temporal resolutions have been playing a key role in building and improving global weather monitoring systems. In support of the calibration of GEO satellites, the bias characteristics in the infrared channels of four operational GEO imagers—the Advanced Meteorological Imager (AMI) on board the Geostationary Korea Multi Purpose Satellite-2A, the Advanced Himawari Imager (AHI) on the Himawari-8, the Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite-16, and the Spinning Enhanced Visible and Infrared Imager flying with the Meteosat-11—are examined using two operational numerical weather prediction (NWP) models. The analysis fields from the Unified Model (UM) employed at the Korea Meteorological Administration, Seoul, South Korea, as well as the ECMWF model reanalysis (ERA5) fields are utilized for the simulation of multiple satellites using the same radiative transfer model (RTM) and the observation minus simulation statistics of the four imagers are analyzed over the clear-sky ocean as a function of time, space, observation angles, and scene temperatures. Overall, the biases of the four advanced GEO imagers demonstrate very similar characteristics to each other, although there are a few rather notable exceptions such as a striping issue in AMI, AHI, and ABI. Additionally, this study highlights the synergistic benefits of the intercalibration by revealing features specific to a particular instrument and also by indicating uncertainties of the RTM and the NWP models. It also reveals the relative performance of NWP models, showing that UM fields are overall wetter than ERA5.

Local air quality is greatly influenced by large- and small-scale weather systems through transport, deposition, and chemical transformation of emissions. Local circulation, in particular, can play a significant role under weak synoptic-scale forcing. To examine the influence of local circulation on daily ground-level PM2.5 concentrations, we utilized surface wind features observed at coastal stations in Midwest Korea, which hosts large industrial complexes and is located downwind of the Seoul Metropolitan area. Using K-means clustering, three circulation patterns were identified for the winter of 2021–2022, including one pattern under strong synoptic-scale forcing (Synoptic Cluster) and two local patterns (Sea Breeze Cluster and Stagnation Cluster). Each cluster is characterized by its unique wind patterns and different contributions to local air quality. The Stagnation Cluster, characterized by weak north-easterly circulation with a comparatively short transport distance, was found to be most strongly linked to high PM2.5 levels, accounting for 57% of the high PM2.5 days (> 35 μg m-3) during the 2021–2022 winter. Additionally, we discovered that the three most extreme PM2.5 events were all members of the Stagnation Cluster and that several consecutive stagnant days preceded each of these cases, facilitating local accumulations of nearby anthropogenic emissions. Overall, our findings emphasize that local air quality cannot be fully explained by synoptic-scale analysis, but can be better understood through the analysis of local circulation patterns. The study also highlights the importance of utilizing surface measurements and selecting features that can best describe the local circulation patterns in the region for the classification of local circulation, which contributes to better capturing both daily and hourly variability in PM2.5 concentrations under different weather regimes.

Taking the slanted satellite viewing geometry into account is important in the simulation of satellite radiances, which vary with the atmospheric conditions along the line of sight. As a first step to take the slanted satellite viewing geometry into account in the numerical weather prediction system operated in the Korean Meteorological Administration, the slant-path modeling is applied for the simulation of clear-sky thermal radiances of a geostationary satellite imager, the Advanced Meteorological Imager (AMI) on board the Geo-KOMPSAT-2A (GK2A). The observations minus simulations (O − B) and the Jacobians before and after the slant-path calculation are compared. Since most infrared channels of AMI are not sensitive to the atmosphere above the tropopause, the size of slant-path effect for AMI is overall smaller than the effect shown in the microwave sounders. Still, the slant-path modeling is found to have a noticeable effect on the three water vapor absorption channels of AMI peaking between 300 and 600 hPa, particularly at large satellite zenith angles and on the regions with high water vapor variabilities in the model field along the line of sight. On average, the slant-path interpolation reduces the standard deviation of O − B of the water vapor channels by around 2.0% on land and 1.4% over the ocean for zenith angles 40°–60°, and it also influences not only the shape and magnitude but also the height of the peak of the Jacobians. In addition, the retrieval experiment based on the optimal estimation also demonstrates the impact of this new approach in the retrieved moisture field, though the improvement is not as significant as shown in the simulation experiment.