NCEP Final Operational Global Analysis Research Articles

Influence of meteorological reanalysis field on air quality modeling in the Yangtze River Delta, China

The meteorological reanalysis field is one of the key inputs to drive mesoscale meteorology simulations. To investigate the impacts of different reanalysis data on predictions of the meteorology parameters, as well as predictions of air quality, this study utilized the Weather Research and Forecasting/Community Multiscale Air Quality modeling system (WRF/CMAQ) with two sets of meteorological reanalysis inputs, the NCEP Final Operational Global Analysis (FNL) and the ECMWF Reanalysis v5.0 (ERA5) to predict the concentrations of ozone (O3) and fine particulate matter (PM2.5) in the Yangtze River Delta (YRD) in 2018. The results showed that ERA5 outperformed FNL in simulating 2-m temperature and relative humidity, while FNL demonstrated better accuracy in predicting wind fields. Both FNL and ERA5 underestimated the PM2.5 in spring and summer while overestimating it in autumn and winter. Furthermore, the Normalized mean bias (NMB) value for ERA5 exceeded the standard in winter (0.34), and the Normalized mean error (NME) value exceeded the standard in autumn (0.54). Both FNL and ERA5 underestimated the PM2.5 concentration during the pollution episodes, and FNL's simulations of PM2.5 concentrations were closer to observations than ERA5's as pollution levels intensified. Regarding O3 concentrations, ERA5 exhibited better performance than FNL, with the NMB (0.30, 0.27) and NME (0.39, 0.37) of FNL and ERA5 both exceeding the standard only in wintertime.

Role of changing land use and land cover (LULC) on the 2018 megafloods over Kerala, India

The state of Kerala, India, experienced massive flooding in August 2018. Unprecedented extreme rainfall left the region with huge losses of infrastructure. Several studies reported the role of improper dam operations and climate change in this region. However, changing land use/land cover (LULC) is an important driver of flood modulation; we therefore studied regional LULC changes (over 4 decades) and their impacts on flooding during this event using the Weather Research Forecasting (WRF)-Hydro model. We downscaled the NCEP final operational global analysis data using WRF to provide meteorological inputs for the gridded WRF-Hydro model. WRF downscaled forcing, along with observed discharge, were used to calibrate WRF-Hydro, which was run with the meteorological forcing of the August 2018 floods using LULC corresponding to 1985, 1995, 2005, and 2018. Analysis of LULC change indicated a significant loss of evergreen forest and reductions in shrubland during 1995-2005 and considerable loss of mixed forest during 2005-2018. The most extensive changes in flooding attributes (discharge, inundation area, and flood surface height) were found for LULC changes from 1995 to 2005. Specifically, comparing 2005 to 1995, we found that high flows (estimated by Q10) increased by >10% at many stations (up to ~50% for several locations). The increase in surface water head was ~40% over some parts with increased inundation. During 2005-2018, afforestation measures reduced the steep decline in LC observed during 1995-2005. Thus, the contribution of LULC changes to the August 2018 flooding is mainly due to the deforestation-related changes during 1995-2005. This substantial role of LC in enhancing flood hazards highlights the need to better manage LC in watersheds.

Impacts of Chemical Initial Conditions in the WRF-CMAQ Model on the Ozone Forecasts in Eastern China

Ozone (O3) has become the major factor for exceeding air pollution standards in many Chinese cities, especially in the more economically developed and densely populated regions, such as eastern China. In this study, we applied the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) model to predict the air quality, and evaluated the influences of different chemical initial conditions on the O3 forecasts with observations in Tai’an and other 13 cities in eastern China in June 2021. The influences of different chemical initial conditions on the O3 forecasts are presented by using two sets of meteorological data (NCEP Final Operational Global Analysis [FNL] and Global Forecast System [GFS]) as initial conditions (IC) and boundary conditions (BC) to drive the WRF/CMAQ model. It was found that the O3 concentrations forecasted by FNL-GFS, in which the chemical IC derived from the CMAQ simulation results by using the FNL data as IC and BC, were closer to observations in all cities than GFS-GFS, in which the chemical IC derived from the CMAQ simulation results by using the GFS data as IC and BC. The normalized mean bias (NMB) values of FNL-GFS for O3 met the benchmark (±15%), while the NMB values of GFS-GFS in Hangzhou and Shijiazhuang did not meet the benchmark. The model performances in Tai’an city were similar to those in 13 cities with better results for FNL-GFS than GFS-GFS. The comparisons of contributions of source regions to O3 in the receptor Tai’an city indicate that different episodes had different relative contributions of source regions and that the simulations of FNL-GFS were more similar to the retrospective simulations than GFS-GFS. The comparisons of contributions of different source sectors to O3 in Tai’an city show that industry emissions are the largest contributor, followed by transportation, power plants and residential emissions.

The Impact of Microphysics Parameterization in the Simulation of Two Convective Rainfall Events over the Central Andes of Peru Using WRF-ARW

The present study explores the cloud microphysics (MPs) impact on the simulation of two convective rainfall events (CREs) over the complex topography of Andes mountains, using the Weather Research and Forecasting- Advanced Research (WRF-ARW) model. The events occurred on December 29 2015 (CRE1) and January 7 2016 (CRE2). Six microphysical parameterizations (MPPs) (Thompson, WSM6, Morrison, Goddard, Milbrandt and Lin) were tested, which had been previously applied in complex orography areas. The one-way nesting technique was applied to four domains, with horizontal resolutions of 18, 6, and 3 km for the outer ones, in which cumulus and MP parameterizations were applied, while for the innermost domain, with a resolution of 0.75 km, only MP parameterization was used. It was integrated for 36 h with National Centers for Environmental Prediction (NCEP Final Operational Global Analysis (NFL) initial conditions at 00:00 UTC (Coordinated Universal Time). The simulations were verified using Geostationary Operational Environmental Satellites (GOES) brightness temperature, Ka band cloud radar, and surface meteorology variables observed at the Huancayo Observatory. All the MPPs detected the surface temperature signature of the CREs, but for CRE2, it was underestimated during its lifetime in its vicinity, matching well after the simulated event. For CRE1, all the schemes gave good estimations of 24 h precipitation, but for CRE2, Goddard and Milbrandt underestimated the 24 h precipitation in the inner domain. The Morrison and Lin configurations reproduced the general dynamics of the development of cloud systems for the two case studies. The vertical profiles of the hydrometeors simulated by different schemes showed significant differences. The best performance of the Morrison scheme for both case studies may be related to its ability to simulate the role of graupel in precipitation formation. The analysis of the maximum reflectivity field, cloud top distribution, and vertical structure of the simulated cloud field also shows that the Morrison parameterization reproduced the convective systems consistently with observations.

Using the inverse of expected error variance to determine weights of individual ensemble members: Application to temperature prediction

The inverse of expected error variance is utilized to determine weights of individual ensemble members based on the THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) forecast datasets. The weights of all ensemble members are thus calculated for summer 2012, with the NCEP final operational global analysis (FNL) data as the truth. Based on the weights of all ensemble members, the variable weighted ensemble mean (VWEM) of temperature of summer 2013 is derived and compared with that from the simple equally weighted ensemble mean. The results show that VWEM has lower root-mean-square error (RMSE) as well as absolute error, and has improved the temperature prediction accuracy. The improvements are quite notable over the Tibetan Plateau and its surrounding areas; specifically, a relative improvement rate of RMSE of more than 24% in 2-m temperature is demonstrated. Moreover, the improvement rates vary slightly with the prediction lead-time (24–96 h). It is suggested that the VWEM approach be employed in operational ensemble prediction to provide guidance for weather forecasting and climate prediction.

Mesoscale modeling study of severe convection over complex terrain

Short squall lines that occurred over Lishui, southwestern Zhejiang Province, China, on 5 July 2012, were investigated using the WRF model based on 1°×1° gridded NCEP Final Operational Global Analysis data. The results from the numerical simulations were particularly satisfactory in the simulated radar echo, which realistically reproduced the generation and development of the convective cells during the period of severe convection. The initiation of this severe convective case was mainly associated with the uplift effect of mesoscale mountains, topographic convergence, sufficient water vapor, and enhanced low-level southeasterly wind from the East China Sea. An obvious wind velocity gradient occurred between the Donggong Mountains and the southeast coastline, which easily enabled wind convergence on the windward slope of the Donggong Mountains; both strong mid–low-level southwesterly wind and low-level southeasterly wind enhanced vertical shear over the mountains to form instability; and a vertical coupling relation between the divergence on the upper-left side of the Donggong Mountains and the convergence on the lower-left side caused the convection to develop rapidly. The convergence centers of surface streams occurred over the mountain terrain and updrafts easily broke through the lifting condensation level (LCL) because of the strong wind convergence and topographic lift, which led to water vapor condensation above the LCL and the generation of the initial convective cloud. The centers of surface convergence continually created new convective cells that moved with the southwest wind and combined along the Donggong Mountains, eventually forming a short squall line that caused severe convective weather.

Satellite SAR observation of the sea surface wind field caused by rain cells

Rain cells or convective rain, the dominant form of rain in the tropics and subtropics, can be easy detected by satellite Synthetic Aperture Radar (SAR) images with high horizontal resolution. The footprints of rain cells on SAR images are caused by the scattering and attenuation of the rain drops, as well as the downward airflow. In this study, we extract sea surface wind field and its structure caused by rain cells by using a RADARSAT-2 SAR image with a spatial resolution of 100 m for case study. We extract the sea surface wind speeds from SAR image by using CMOD4 geophysical model function with outside wind directions of NCEP final operational global analysis data, Advance Scatterometer (ASCAT) onboard European MetOp-A satellite and microwave scatterometer onboard Chinese HY-2 satellite, respectively. The root-mean-square errors (RMSE) of these SAR wind speeds, validated against NCEP, ASCAT and HY-2, are 1.48 m/s, 1.64 m/s and 2.14 m/s, respectively. Circular signature patterns with brighter on one side and darker on the opposite side on SAR image are interpreted as the sea surface wind speed (or sea surface roughness) variety caused by downdraft associated with rain cells. The wind speeds taken from the transect profile which superposes to the wind ambient vectors and goes through the center of the circular footprint of rain cell can be fitted as a cosine or sine curve in high linear correlation with the values of no less than 0.80. The background wind speed, the wind speed caused by rain cell and the diameter of footprint of the rain cell with kilometers or tens of kilometers can be acquired by fitting curve. Eight cases interpreted and analyzed in this study all show the same conclusion.

The Impact of Forcing Datasets on the High-Resolution Simulation of Tropical Storm Ivan (2004) in the Southern Appalachians

Abstract The influence of large-scale forcing on the high-resolution simulation of Tropical Storm Ivan (2004) in the southern Appalachians was investigated using the Weather Research and Forecasting model (WRF). Two forcing datasets were employed: the North American Regional Reanalysis (NARR; 32 km × 32 km) and the NCEP Final Operational Global Analysis (NCEP FNL; 1° × 1°). Simulated fields were evaluated against rain gauge, radar, and satellite data; sounding observations; and the best track from the National Hurricane Center (NHC). Overall, the NCEP FNL forced simulation (WRF_FNL) captures storm structure and evolution more accurately than the NARR forced simulation (WRF_NARR), benefiting from the hurricane initialization scheme in the NCEP FNL. Further, the performance of WRF_NARR is also negatively affected by a previously documented low-level warm bias in NARR. These factors lead to excessive precipitation in the Piedmont region, delayed rainfall in Alabama, as well as spatially displaced and unrealistically extreme rainbands during its passage over the southern Appalachians. Spatial filtering of the simulated precipitation fields confirms that the storm characteristics inherited from the forcing are critical to capture the storm’s impact at local places. Compared with the NHC observations, the storm is weaker in both NARR and NCEP FNL (up to Δp ~ 5 hPa), yet it is persistently deeper in all WRF simulations forced by either dataset. The surface wind fields are largely overestimated. This is attributed to the underestimation of surface roughness length over land, leading to underestimation of surface drag, reducing low-level convergence, and weakening the dissipation of the simulated cyclone.

Diagnostic Study of the Effects of a Large City on Heavy Rainfall as Revealed by an Ensemble Simulation: A Case Study of Central Tokyo, Japan

Abstract This study was undertaken to investigate the effects of a large city on heavy rainfall in Tokyo, Japan, based on an ensemble simulation with a large number of members. An ensemble simulation (24 members) of eight brief heavy rainfall events that occurred from 1999 to 2007 was performed. The ensemble simulation was performed using five objective analysis datasets [Japanese 25-yr Reanalysis (JRA-25), Regional Analysis (RANAL), NCEP Final Operational Global Analysis (NCEP-FNL), NCEP–Department of Energy Global Reanalysis 2 (NCEP/DOE-R2), and Global Analysis (GANAL)]. Land-use distributions of two types were prepared for numerical simulations: actual land use and virtual land use, in which all urban land use was converted to vegetation. Each member was simulated using actual land use and virtual land use. The effects of the urban area were then assessed by comparing the results of these simulations. Results indicate no large differences in the wind systems of the Kanto plain (roughly 100 km × 100 km), where Tokyo is located, even if cities were converted entirely to vegetation. The influence of cities on wind systems of this scale was negligible. However, changes of wind convergence were found leeward of the urban area, which increased the horizontal vapor flux there. The precipitation also increased there due to the urban effects, despite the decrease in vertical vapor flux from the land surface. It is concluded that urban effects for Tokyo alter the wind characteristics leeward of the urban area and develop wind convergence and rainfall there.