Tuning of length-scale and observation-error for radar data assimilation using four dimensional variational (4D-Var) method

The effects of tuning of length-scale and observation-error on heavy rainfall forecasts are investigated. Length scale and observation error are tuned based on observation minus background (O - B) covariances and theoretically expected cost function values, respectively. Tuned length scale and observation error are applied to radar data assimilation using the Four Dimensional Variational (4D-Var) method. Length-scale tuning leads to improved Quantitative Precipitation Forecast (QPF) skill for heavy precipitation, better analyses, and reduced errors of wind, temperature, humidity, and hydrometeor forecasts. The effects of observation-error tuning are not as significant as those of length-scale tuning, and they are limited to improvements in QPF skill. This is because tuned observation errors are close to pre-assumed values. Proper tuning of length-scale and observation-error is essential for radar data assimilation using the 4D-Var method

Satellite radiance data assimilation for binary tropical cyclone cases over the western North Pacific

A total of three binary tropical cyclone (TC) cases over the Western North Pacific are selected to investigate the effects of satellite radiance data assimilation on analyses and forecasts of binary TCs. Two parallel cycling experiments with a 6 h interval are performed for each binary TC case, and the difference between the two experiments is whether satellite radiance observations are assimilated. Satellite radiance observations are assimilated using the Weather Research and Forecasting Data Assimilation (WRFDA)'s three-dimensional variational (3D-Var) system, which includes the observation operator, quality control procedures, and bias correction algorithm for radiance observations. On average, radiance assimilation results in slight improvements of environmental fields and track forecasts of binary TC cases, but the detailed effects vary with the case. When there is no direct interaction between binary TCs, radiance assimilation leads to better depictions of environmental fields, and finally it results in improved track forecasts. However, positive effects of radiance assimilation on track forecasts can be reduced when there exists a direct interaction between binary TCs and intensities/structures of binary TCs are not represented well. An initialization method (e.g., dynamic initialization) combined with radiance assimilation and/or more advanced DA techniques (e.g., hybrid method) can be considered to overcome these limitations

Global assessment of sand and dust storms

The specific objectives of the assessment are to: 1) Synthesise and highlight the environmental and socio-economic causes and impacts of SDS, as well as available technical measures for their mitigation, at the local, regional and global levels; 2) Show how the mitigation of SDS can yield multiple sustainable development benefits; 3) Synthesize information on current policy responses for mitigating SDS and 4) Present options for an improved strategy for mitigating SDS at the local, regional and global levels, building on existing institutions and agreements

Impact of Spectral Nudging on Real‐Time Tropical Cyclone Forecast

Regional models often face a chronic problem caused by inconsistencies between simulated model fields and large‐scale forcing data, which could limit predictability of regional models. Spectral nudging is a technique which can ameliorate this issue by providing large‐scale forcing in the regional model interior. In this study, spectral nudging was applied to tropical cyclone (TC) track and intensity forecasts, and its effects were investigated using a high‐resolution regional model. Case studies on Typhoon Neoguri and Vongfong showed that spectral nudging was effective at enhancing track forecast by improving large‐scale winds and the western North Pacific subtropical high. However, simulated TC intensity was underestimated without the optimization of spectral nudging. Sensitivity tests on spectral nudging options were performed to improve TC intensity while maintaining forecast track improvements. By reducing the nudging coefficient, intensity errors were reduced with a more reasonably developed TC structure. A total of 51 experiments for 18 TCs were conducted with revised spectral nudging options to investigate the general effects of spectral nudging on TC forecasting. The track forecast was generally improved by spectral nudging, especially after 96 hr, and forecast intensity was also improved as compared to experiments without nudging. Furthermore, spectral nudging was especially effective for TCs which occurred to the east of the western North Pacific and turned near Northeast Asia. Therefore, the impact of spectral nudging can be increased by selectively applying spectral nudging based on TC characteristics

Impacts of the East Asian Winter Monsoon and Local Sea Surface Temperature on Heavy Snowfall over the Yeongdong Region

This research investigates the impact of local sea surface temperature (SST) on the 2-month (January and February) accumulated snowfall over the Yeongdong (YD) region. The YD region is strongly affected by synoptic-scale factors such as the East Asian winter monsoon (EAWM). The relationships of snowfall over the YD region to the EAWM and local SST are examined based on observational analyses and sensitivity experiments using a regional climate model. In the sensitivity experiments, local SST is replaced with the 33-yr mean winter SST (1982???2014). The observational analysis shows that both the synoptic environment and local SST are important factors for the occurrence of anomalous heavy snowfall over the YD region. The favorable synoptic environments can be characterized by eastward expansion of the Siberian high over Manchuria and corresponding enhancement of easterly anomalies over the YD region. These conditions are more frequently observed during the weak EAWM years than during the strong EAWM. Furthermore, warm SST over the East Sea contributes to heavy snowfall over the YD region by providing heat and moisture in the lower troposphere, which are important sources of energy for the formation of heavy snowfall. Warm SST anomalies over the East Sea enhance low-level moisture convergence over the YD region, while cold SST anomalies lead to reduced moisture convergence. Sensitivity experiments indicate that local SST can significantly affect snowfall amount over the YD region when the synoptic environments are favorable. However, without these synoptic conditions (expansion of the Siberian high and easterly inflow), the impact of local SST on the snowfall over the YD region is not significant

Effect of a Scale-Aware Convective Parameterization Scheme on the Simulation of Convective Cells-Related Heavy Rainfall in South Korea

In this study, the effect of a scale-aware convective parameterization scheme (CPS) on the simulation of heavy precipitation in the gray-zone was investigated using the Weather Research and Forecasting (WRF) model. We performed WRF simulations with the Kain-Fritsch (KF) scheme (non-scale-aware), Multiscale Kain-Fritsch (MSKF) scheme (scale-aware), and explicit convection (i.e., no CPS). The MSKF scheme uses a scale-aware parameter that modulates the convective available potential energy (CAPE) timescale and entrainment process in the KF scheme as a function of the horizontal grid spacing. The results of this study show that WRF simulations using explicitly resolved convection lead to overestimations and erroneous precipitation locations in the gray-zone because the convection and atmospheric instability cannot be appropriately triggered and reduced. The CPS without scale-awareness in the gray-zone exaggerates the convection and distorts synoptic fields, leading to the erroneous simulation of heavy precipitation at high resolution. The MSKF scheme with scale-awareness improves the simulation of convective cells-related heavy rainfall by removing the atmospheric instability in the gray-zone, reducing the role of the CPS, and increasing the role of the microphysics parameterization scheme (MPS) with decreasing grid spacing. In addition, the results of sensitivity experiments show that reducing the CAPE timescale leads to the faster development of convective cells, whereas decreasing the entrainment leads to precipitation overestimation. The modulated parameters in the scale-aware MSKF scheme play a crucial role in balancing the effects of the CPS and MPS in the gray-zone

Investigation of Added Value in Regional Climate Models for East Asian Storm Track Analysis

Studies have shown that regional climate models (RCMs) can simulate local climates at a higher resolution for specific regions compared to global climate models (GCMs), making dynamic downscaling using RCMs a more effective approach. Therefore, RCMs have become valuable tools for evaluating the potential impacts of climate change on specific regions and for informing local adaptation strategies. To fully understand the added value (AV) of RCMs, it is essential to understand how the characteristics differ between land and ocean. The complex topography of East Asia, including land and sea, makes it a suitable region for evaluating the AV of RCMs. In this study, we compared two regional simulations that integrated the same RCMs but employed different GCMs from the Coordinated Regional Climate Downscaling Experiment for their ability to simulate storm tracks in East Asia. The results of the RCMs for the period from 1982 to 2005 were compared with their host Coupled Model Intercomparison Project GCM projections and high-resolution reanalysis. In mountainous regions, the AV of the RCMs weakened the bias of the GCM and improved its agreement with the reanalysis due to the dynamical process near the high-resolution topography. In plains and coastal areas, owing to the increase in horizontal resolution and clearly determined coastline in RCMs, small-scale phenomena are well represented, and the storm track of RCMs shows similar values to that of the GCM in maritime regions. This study demonstrates the value of RCMs for improving the accuracy of climate projections in East Asia, informing adaptation strategies, and enhancing climate research