Regional Model Simulations Research Articles

El cambio climático en los andes y su impacto en la agricultura: una revisión sistemática

In recent years, agriculture in the Andes has shown greater sensitivity to climate change, favoring processes of soil erosion, retreat of glaciers, loss of vegetation cover, increased intensity of rainfall and alteration in the dynamics of crops in the region such as: potato, quinoa, corn, among others. This motivated many authors to develop regional model simulations to estimate the vulnerability index of these agricultural systems to these climatic events, allowing them to provide a more reliable climatological data in the presence of hot and dry winds. In this review article, the main contributions provided by various researchers regarding the impact of climate change on Andean agriculture are detailed. According to the collected information, it is concluded that climate change in the Andes will cause countries such as Brazil, Bolivia, Ecuador, Venezuela, Guyana and Colombia, to increase their local temperature, potential for evapotranspiration and water scarcity, causing the loss of important crops such as rice. In contrast, countries such as Peru, Argentina, Chile, Bolivia and Uruguay will register lower temperatures that will affect their production and yield in crops such as quinoa, potatoes, tarwi, among others.

Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

In this paper, we present the estimation of surface NO2 concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO2 vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO2 concentrations. The neural network model is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations. Neural network estimation of surface NO2 concentrations show good agreement with in situ monitor data with Pearson correlation coefficient (R) of 0.80. The results also show that the machine learning approach is performing better than regional CTM simulations in predicting surface NO2 concentrations. We also performed a sensitivity analysis for each input parameter of the neural network model. The validated neural network model is then used to estimate surface NO2 concentrations over Germany from 2018 to 2020. Estimated surface NO2 concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO2 in Germany. The estimated surface NO2 concentrations provide comprehensive information of NO2 spatial distribution which is very useful for exposure estimation. We estimated the annual average NO2 exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 µµg/m3, respectively. While the annual average NO2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 µµg/m3. In addition, we used the surface NO2 data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO2 levels in Germany. In general, 10–30% lower surface NO2 concentrations are observed in 2020 compared to 2018 and 2019, indicating the significant impacts of a series of restriction measures to reduce the spread of the virus.

Regional Ground Motion Prediction Equation Developed for the Korean Peninsula Using Recorded and Simulated Ground Motions

ABSTRACT The Korean peninsula is in a stable continental region with moderate seismicity. The first and second largest instrumented earthquakes occurred in the peninsula on September 12, 2016 and November 15, 2017, respectively, which inflicted major damage on structures in nearby populated cities. This study aims to propose a ground motion prediction equation (GMPE) for the peninsula using both ground motions recorded during earthquakes in Korean and those simulated using a regional ground motion simulation (GMS) model. The procedure used in this study can be applied to other low-to-moderate seismic regions with few strong ground motion records.

Historical and future climates over the upper and middle reaches of the Yellow River Basin simulated by a regional climate model in CORDEX

Despite the importance of the Yellow River to China, climate change for the middle reaches of the Yellow River Basin (YRB) has been investigated far less than for other regions. This work focuses on future changes in mean and extreme climate of the YRB for the near-term (2021–2040), mid-term (2041–2060), and far-term (2081–2100) future, and assesses these with respect to the reference period (1986–2005) using the latest REgional MOdel (REMO) simulations, driven by three global climate models (GCMs) and assuming historical and future [Representative Concentration Pathway (RCP) 2.6 and 8.5] forcing scenarios, over the CORDEX East Asia domain at 0.22° horizontal resolution. The results show that REMO reproduces the historical mean climate state and selected extreme climate indices reasonably well, although some cold and wet biases exist. Increases in mean temperature are strongest for the far-term in winter, with an average increase of 5.6 °C under RCP 8.5. As expected, the future temperatures of the warmest day (TXx) and coldest night (TNn) increase and the number of frost days (FD) declines considerably. Changes to mean temperature and FD depend on elevation, which could be explained by the snow-albedo feedback. A substantial increase in precipitation (34%) occurs in winter under RCP 8.5 for the far-term. Interannual variability in precipitation is projected to increase, indicating a future climate with more extreme events compared to that of today. Future daily precipitation intensity and maximum 5-day precipitation would increase and the number of consecutive dry days would decline under RCP 8.5. The results highlight that pronounced warming at high altitudes and more intense rainfall could cause increased future flood risk in the YRB, if a high GHG emission pathway is realized.

Coastal Upwelling Limitation by Onshore Geostrophic Flow in the Gulf of Guinea Around the Niger River Plume

Wind-driven coastal upwelling can be compensated by onshore geostrophic flow, and river plumes are associated with such flow. We investigate possible limitation of the northeast Gulf of Guinea upwelling by the Niger River plume, using regional ocean model simulations with or without river and dynamical upwelling indices. Here, the upwelling is weakened by 50% due to an onshore geostrophic flow equally controlled by alongshore thermosteric and halosteric sea-level changes. The river contributes to only 20% of this flow, as its plume is shallow while upwelling affects coastal temperature and salinity over a larger depth. Moreover, the river-induced mixed-layer thinning compensates the current increase, with no net effect on upwelling. The geostrophic compensation is due to an abrupt change in coastline orientation that creates the upwelling cross-shore front. The river nonetheless warms the upwelling tongue by 1°C, probably due to induced changes in horizontal advection and/or stratification.

Projected changes in heat load in Carpathian Basin cities during the 21st century

In this study the changes in the nighttime heat load in Carpathian Basin cities during the 21st century were examined. To quantify the heat load, the tropical night climate index was used. The MUKLIMO_3 local scale climate model was used to describe the urban processes and the land use classes were defined by the local climate zones. The expected change was examined over three periods: the 1981–2010 was taken as reference period using the Carpatclim database and the 2021–2050 and 2071–2100 future periods using EURO-CORDEX regional model simulation data for two scenarios (RCP4.5 and RCP8.5). To combine the detailed spatial resolution and the long time series, a downscaling method was applied. Our results show that spectacular changes could be in the number of tropical nights during the 21st century and the increasing effect of the urban landform is obvious. In the near future, a slight increase can be expected in the number of tropical nights, which magnitude varies from city to city and there is no major difference between the scenarios. However, at the end of the century the results of the two scenarios differ: the values can be 15-25 nights in case of RCP4.5 and 30-50 nights in case of RCP8.5. The results show that dwellers could be exposed to high heat load in the future, as the combined effect of climate change and urban climate, thus developing various mitigation and adaptation strategies is crucial.

Data for the geoecology of solution karst dolines, with particular attention to climatic, soil and biogenic environment

In this study the changes in the nighttime heat load in Carpathian Basin cities during the 21st century were examined. To quantify the heat load, the tropical night climate index was used. The MUKLIMO_3 local scale climate model was used to describe the urban processes and the land use classes were defined by the local climate zones. The expected change was examined over three periods: the 1981–2010 was taken as reference period using the Carpatclim database and the 2021–2050 and 2071–2100 future periods using EURO-CORDEX regional model simulation data for two scenarios (RCP4.5 and RCP8.5). To combine the detailed spatial resolution and the long time series, a downscaling method was applied. Our results show that spectacular changes could be in the number of tropical nights during the 21st century and the increasing effect of the urban landform is obvious. In the near future, a slight increase can be expected in the number of tropical nights, which magnitude varies from city to city and there is no major difference between the scenarios. However, at the end of the century the results of the two scenarios differ: the values can be 15-25 nights in case of RCP4.5 and 30-50 nights in case of RCP8.5. The results show that dwellers could be exposed to high heat load in the future, as the combined effect of climate change and urban climate, thus developing various mitigation and adaptation strategies is crucial.

Data for the geoecology of solution karst dolines, with particular attention to climatic, soil and biogenic environment

In this study the changes in the nighttime heat load in Carpathian Basin cities during the 21st century were examined. To quantify the heat load, the tropical night climate index was used. The MUKLIMO_3 local scale climate model was used to describe the urban processes and the land use classes were defined by the local climate zones. The expected change was examined over three periods: the 1981–2010 was taken as reference period using the Carpatclim database and the 2021–2050 and 2071–2100 future periods using EURO-CORDEX regional model simulation data for two scenarios (RCP4.5 and RCP8.5). To combine the detailed spatial resolution and the long time series, a downscaling method was applied. Our results show that spectacular changes could be in the number of tropical nights during the 21st century and the increasing effect of the urban landform is obvious. In the near future, a slight increase can be expected in the number of tropical nights, which magnitude varies from city to city and there is no major difference between the scenarios. However, at the end of the century the results of the two scenarios differ: the values can be 15-25 nights in case of RCP4.5 and 30-50 nights in case of RCP8.5. The results show that dwellers could be exposed to high heat load in the future, as the combined effect of climate change and urban climate, thus developing various mitigation and adaptation strategies is crucial.

Projected changes in heat load in Carpathian Basin cities during the 21st century

In this study the changes in the nighttime heat load in Carpathian Basin cities during the 21st century were examined. To quantify the heat load, the tropical night climate index was used. The MUKLIMO_3 local scale climate model was used to describe the urban processes and the land use classes were defined by the local climate zones. The expected change was examined over three periods: the 1981–2010 was taken as reference period using the Carpatclim database and the 2021–2050 and 2071–2100 future periods using EURO-CORDEX regional model simulation data for two scenarios (RCP4.5 and RCP8.5). To combine the detailed spatial resolution and the long time series, a downscaling method was applied. Our results show that spectacular changes could be in the number of tropical nights during the 21st century and the increasing effect of the urban landform is obvious. In the near future, a slight increase can be expected in the number of tropical nights, which magnitude varies from city to city and there is no major difference between the scenarios. However, at the end of the century the results of the two scenarios differ: the values can be 15-25 nights in case of RCP4.5 and 30-50 nights in case of RCP8.5. The results show that dwellers could be exposed to high heat load in the future, as the combined effect of climate change and urban climate, thus developing various mitigation and adaptation strategies is crucial.

Incorporating Spatial Variations in Parameters for Improvements of an Evapotranspiration Model

AbstractEcosystem models are important tools for exploring the temporal and spatial patterns of ecosystem processes and their responses to climate change. However, the implications of uncertainty in model parameters are often overlooked, especially in regional ecosystem model simulations. Here, we use eddy‐covariance observations to estimate parameters in an ecosystem model, which was developed from Shuttleworth‐Wallace model, and examine the effect on estimates of evapotranspiration (ET). Using a simple ecosystem model as an example, we use Monte Carlo techniques to optimize key model parameters using eddy covariance (EC) data from 163 FLUXNET sites. The optimization process revealed a strong spatial correlation between key parameters and environmental variables, particularly leaf area index (LAI) and soil characteristics (e.g., clay fraction). The optimization of parameters related to canopy conductance and soil surface resistance greatly improved model performance, particularly when incorporating the identified spatial variation of parameters. The improved model agreed well with the measurements with an increase in the coefficient of determination (R2) from 73% to 80% in the 8‐day averaged ET estimation and a decrease in the root mean square error (RMSE) from 130.2 to 104.3 mm year−1 compared with the original model. The results suggest the potential of eddy‐covariance flux observations to identify predictable spatial variations of key parameters, which can be used to better constrain ecosystem models. And in this case, a universal and efficient method for reducing the uncertainties in key parameters across different PFTs and ecosystem applications is suggested.

Productive detours – Atlantic water inflow and acoustic backscatter in the major troughs along the Svalbard shelf

Atlantic Water (AW) flowing along the western and northern Svalbard shelf-break extends the Atlantic domain into the Arctic and is the region’s major source of heat, nutrients and advected plankton. We investigated the inflow and recirculation of AW into four major troughs that cut into the Svalbard shelf, the Isfjorden, Kongsfjorden, Hinlopen and Kvitøya Troughs, and related the circulation patterns to acoustic backscatter observed with echosounders and Acoustic Doppler Current Profilers. The acoustic observations showed higher levels of backscatter from fish in the Hinlopen Trough compared to the shelf and shelf-break north of Svalbard. This coincides with a steady inflow of nutrients, biomass and heat into the trough with the AW. Trough circulation was characterized using output from a high-resolution regional ocean model and particle tracking simulations. All four troughs experience topographically steered recirculation (in-and-outflow) of AW, but the troughs on the western Svalbard shelf showed a stronger seasonality than the troughs on the northern shelf. The Hinlopen Trough receives the strongest AW inflow and the most direct inflow from the shelf-break boundary current. The troughs form hybrid habitats between the shelf and shelf-break that extend the Atlantic advective domain closer to the Svalbard coastline.

Microclimatic Effects of a Forest-to-Peatland Transition on Aerodynamic Resistance to Water Vapour Transfer in the Sub-humid Boreal Plains

Peatlands often experience turbulent sheltering from their surrounding upland forests, which results in spatially variable surface–atmosphere exchanges of momentum, heat, and moisture produced by flow-separation dynamics, which suppresses the transport of such scalars in the sheltered region while promoting transport in the reattachment zone. With evapotranspiration being the dominant source of water loss in the Boreal Plains, it is necessary to understand the dynamics and controls on evapotranspiration within these peatlands. We used the regional atmospheric forest large-eddy simulation (RAFLES) model to study the impact of flow separation and surface roughness on microclimates leeward of a forest-to-peatland roughness transition. We parametrized our simulation with observed vegetation characteristics and meteorological data from three natural peatlands to accurately estimate natural ranges of peatland roughnesses and energy dynamics. Our simulations show that changes to peatland roughness do not affect the distances required for flow reattachment, and therefore the size of the sheltered region. However, increasing the surface roughness produces greater surface turbulence, quicker flow recovery, and decreased flow reversals within the sheltered region. Further, substantial microclimatic differences are observed throughout the flow regions of the roughness transition. Our results show that turbulence, aerodynamic resistance, and the microclimate vary throughout the backward-facing step transition and should be taken into account when estimating the spatial dynamics of evaporative demand leeward of a roughness transition. Furthermore, increasing the surface roughness of a peatland minimizes the spatial variability of turbulent drivers of evapotranspiration across a roughness transition. That is, flow separation and the surface roughness of the peatland should be accounted for when estimating the spatial variability and total evaporative potential across a peatland.

Transition of Low Clouds in the East China Sea and Kuroshio Region in Winter: A Regional Atmospheric Model Study

AbstractBias in simulating the stratocumulus‐to‐cumulus transition remains a main source of uncertainties in climate projection and can significantly affect the energy budget in climate models. To gain insights into the transition, this study investigates the cloud transition forced by the sea surface temperature (SST) front and synoptic disturbances in the East China Sea and Kuroshio region in winter based on both observations and regional atmospheric model simulations. The Kuroshio SST front greatly accelerates cloud transition by enhancing surface latent heat flux, marine atmospheric boundary layer (MABL) dynamical adjustment, and cloud top entrainment. With the sharp SST increase from the cold flank to the Kuroshio SST warm tongue (KWT), surface wind convergence (SWC) over the KWT induced by the SST front and synoptic disturbances enhances the coupling between the cloud layer and subcloud layer. An underlying positive feedback between the SWC and latent heating in the cloud layer can enhance abrupt change in cloud properties and maintain cloud band over the KWT against the decoupling through the so‐called “Deepening‐Warming” mechanism induced by latent heating. From the KWT downwind southward, the surface layer turbulent mixing weakens, while latent heating in the cloud layer and cloud top longwave radiative cooling enhance buoyancy and vertical mixing in the cloud layer. This difference in vertical mixing between the cloud layer and subcloud layer facilitates the MABL decoupling and impedes upward moisture transport. Meanwhile, decreasing lower tropospheric stability is conducive to the entrainment of drier and warmer air from above into the cloud layer, strengthening cloud evaporation.

Rain-on-Snow events in Japan as projected by a large ensemble of regional climate simulations

Rain-on-Snow (ROS) events can cause severe snowmelt hazards such as river flooding, avalanches, and landslides that have significant impacts on various sectors. The influence of climate change on the frequency of ROS events in Japan was investigated using climate projections obtained from the Database for Policy Decision making for Future climate change (d4PDF). The projected future climate in the regional model simulations showed an increase in the ROS events over the mountainous areas in Hokuriku (Sea of Japan side of Central Japan) and Hokkaido (Northern Japan) regions, where a higher amount of snowfall will still occur in the future. Characteristics of ROS events such as rainfall, snowmelt, and related runoff were also enhanced in these regions. Self-organizing maps (SOMs) were applied using the surface atmospheric circulation data to determine the dominant ROS-related weather patterns (WPs) in the present and future climate. The SOMs showed that some WPs had a significant effect on the cause of the ROS events. The differences in the impacts of climate change between the WPs were evaluated to understand the future changes in runoff and snowmelt associated with ROS events. The SOM analysis suggests that the increase in the occurrence of ROS events and the resultant enhancement in their characteristics in the future-climate projection can be attributed to the changes in the dominant ROS-related WPs (from cyclonic to cold-surge type) corresponding to variations in the freezing point line. These findings can inform water hazard and water resource management plans that aim to withstand regional climate change.

Assessing the sensitivity of lower atmospheric characteristics to agricultural land use classification over the Lower Mississippi River Alluvial Valley

The Lower Mississippi River Alluvial Valley (LMRAV) is a key agricultural area within the USA, and although it receives a substantial level of annual rainfall, irrigation remains a requirement to sustain high productivity. Since a large percentage of irrigation comes from limited groundwater sources, predictions of surface and lower atmospheric characteristics associated with convective rainfall processes are critical for planning and managing water resources. While numerical weather models are a key tool in this prediction effort, there is considerable error in the models associated with the correct categorization of regional land use. This is especially true for the LMRAV, where most agricultural land is defined as dry cropland despite the extensive use of irrigation. To improve the accuracy of regional model simulations over the LMRAV, this study investigates the sensitivity of changing the dominate land use category from dry to irrigated cropland within a high-resolution Weather Research and Forecasting (WRF) model simulation. Based on a 5-month simulation (May–Sept. 2016) over the LMRAV, results of the study show that replacing dry cropland with irrigated cropland leads to substantial variations in the low-level thermal and moisture balances. In general, the northern LMRAV was shown to have the greatest increase (decrease) in latent (sensible) heat flux in August and September, with a corresponding increase in 2-m dew point temperature where latent heat flux increased. Additionally, boundary layer heights were shown to decrease over the northern LMRAV over the simulation, likely a result of decreased temperatures resulting from a dampened sensible heat flux.

Rapid reduction in black carbon emissions from China: evidence from 2009–2019 observations on Fukue Island, Japan

Abstract. A long-term, robust observational record of atmospheric black carbon (BC) concentrations at Fukue Island for 2009–2019 was produced by unifying the data from a continuous soot monitoring system (COSMOS) and a Multi-Angle Absorption Photometer (MAAP). This record was then used to analyze emission trends from China. We identified a rapid reduction in BC concentrations of (-5.8±1.5) % yr−1 or −48 % from 2010 to 2018. We concluded that an emission change of (-5.3±0.7) % yr−1, related to changes in China of as much as −4.6 % yr−1, was the main underlying driver. This evaluation was made after correcting for the interannual meteorological variability (IAV) by using the regional atmospheric chemistry model simulations from the Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) models (collectively WRF/CMAQ) with the constant emissions. This resolves the current fundamental disagreements about the sign of the BC emissions trend from China over the past decade as assessed from bottom-up emission inventories. Our analysis supports inventories reflecting the governmental clean air actions after 2010 (e.g., MEIC1.3, ECLIPSE versions 5a and 6b, and the Regional Emission inventory in ASia (REAS) version 3.1) and recommends revisions to those that do not (e.g., Community Emissions Data System – CEDS). Our estimated emission trends were fairly uniform across seasons but diverse among air mass origins. Stronger BC reductions, accompanied by a reduction in carbon monoxide (CO) emissions, occurred in regions of south-central East China, while weaker BC reductions occurred in north-central East China and northeastern China. Prior to 2017, the BC and CO emissions trends were both unexpectedly positive in northeastern China during winter months, which possibly influenced the climate at higher latitudes. The pace of the estimated emissions reduction over China surpasses the Shared Socioeconomic Pathways (SSPs with reference to SSP1, specifically) scenarios for 2015–2030, which suggests highly successful emission control policies. At Fukue Island, the BC fraction of fine particulate matter (PM2.5) also steadily decreased over the last decade. This suggests that reductions in BC emissions started without significant delay when compared to other pollutants such as NOx and SO2, which are among the key precursors of scattering PM2.5.

Study of SO Pollution in the Middle East Using MERRA‐2, CAMS Data Assimilation Products, and High‐Resolution WRF‐Chem Simulations

AbstractOil recovery, power generation, water desalination, gas flaring, and traffic are the main contributors to SO emissions in the Middle East (ME). Satellite observations suggest that the traditional emission inventories do not account for multiple SO emission sources in the ME. This study aims to evaluate the most frequently used SO emission data sets over the ME by comparing high‐resolution regional model simulations and meteorology/chemistry assimilation products, MERRA‐2 and CAMS, with satellite and available ground‐based air‐quality observations. Here, we employ the WRF‐Chem‐3.7.1 regional meteorology‐chemistry model and conduct simulations for the period 2015–2016 with 10 km grid spacing using HTAP‐2.2 emission data sets and the new OMI‐HTAP data, which is based on the combination of the near‐surface SO emissions taken from the HTAP‐2.2 inventory with strong (>30 kt/year) SO point sources obtained from the satellite Ozone Monitoring Instrument (OMI) observations. We find that conventional emission inventories (EDGAR‐4.2, MACCity, and HTAP‐2.2) have uncertainties in the location and magnitude of SO sources in the ME and significantly underestimate SO emissions in the Arabian Gulf. The WRF‐Chem, run in conjunction with the new OMI‐HTAP emissions, improves comparisons between the satellite and ground‐based SO observations. Our simulations show that SO surface concentrations in Jeddah and Riyadh frequently exceed European air‐quality limits. The ME generates about 10% of global anthropogenic SO emissions, on par with India. Therefore, the development of effective emission controls and improvement of air‐quality monitoring in the ME are urgently needed.

Skill Assessment of an Atmosphere–Wave Regional Coupled Model over the East China Sea with a Focus on Typhoons

This study performed several sensitivity experiments to investigate the impact of atmosphere–wave coupling on the simulated wind and waves over the East China Sea (ECS) with a focus on typhoon events. These experiments include stand-alone regional atmosphere model (CCLM) simulations, stand-alone spectral wave model (WAM) simulations driven by the regional atmospheric model CCLM or ERA5 reanalysis, and two-way (CCLM-WAM) coupled simulations. We assessed the simulated wind speed and significant wave height against in situ observations and remote sensing data and focused on typhoon events in 2010. We analyzed the differences between the experiments in capturing the surface pressure, wind speed, and roughness length. Both ERA5 reanalysis data and our regional model simulations demonstrate high quality in capturing wind and wave conditions over the ECS. The results show that downscaled simulations tend to be closer to in situ observations than ERA5 reanalysis data in capturing wind variability and probability distribution, dominant wind and wave directions, strong typhoon intensity and related extreme significant wave height. In comparison with satellite observations, the CCLM-WAM simulation outperforms the CCLM in reducing wind bias. The coupled and uncoupled simulations are very similar in terms of other wind and wave statistics. Though there is much improvement in capturing typhoon intensity to ERA5, regional downscaled simulations still underestimate the wind intensity of tropical cyclones.

Polarimetric Radar Characteristics of Simulated and Observed Intense Convective Cores for a Midlatitude Continental and Tropical Maritime Environment

AbstractThis study contrasts midlatitude continental and tropical maritime deep convective cores using polarimetric radar observables and retrievals from selected deep convection episodes during the MC3E and TWPICE field campaigns. The continental convective cores produce stronger radar reflectivities throughout the profiles, while maritime convective cores produce more positive differential reflectivityZdrand larger specific differential phaseKdpabove the melting level. Hydrometeor identification retrievals revealed the presence of large fractions of rimed ice particles (snow aggregates) in the continental (maritime) convective cores, consistent with theZdrandKdpobservations. The regional cloud-resolving model simulations with bulk and size-resolved bin microphysics are conducted for the selected cases, and the simulation outputs are converted into polarimetric radar signals and retrievals identical to the observational composites. Both the bulk and the bin microphysics reproduce realistic land and ocean (L-O) contrasts in reflectivity, polarimetric variables of rain drops, and hydrometeor profiles, but there are still large uncertainties in describingZdrandKdpof ice crystals associated with the ice particle shapes/orientation assumptions. Sensitivity experiments are conducted by swapping background aerosols between the continental and maritime environments, revealing that background aerosols play a role in shaping the distinct L-O contrasts in radar reflectivity associated with raindrop sizes, in addition to the dominant role of background thermodynamics.

A Dynamic Blending Scheme to Mitigate Large‐Scale Bias in Regional Models

AbstractSeveral blending methods have been developed in dynamic downscaling and rapid cycled data assimilation. Blending the large‐scale part of the global model (GM) analysis or forecast has led to improvement in regional model (RM) simulations. However, in previous studies the blended waveband of the GM has generally been determined using a fixed, arbitrarily chosen cutoff wave number. Here we introduce a new dynamic blending (DB) scheme with a dynamic cutoff wave number computed according to the spectral characteristics of GM forecast quality and the spectral distribution of errors in the RM. The DB scheme is described and applied to eight‐day summertime and seven‐day wintertime cycled Weather Research and Forecasting Model forecasts over a regional domain in the continental United States. The scheme can determine a cutoff wave number with significant temporal variations. The temporal variation results from the error growth property of the RM and has a clear diurnal oscillation, suggesting that fewer (more) GM waves should be introduced into the RM at noon (night). The cutoff wave number difference between the two periods indicates seasonal variation of the cutoff wave number with larger day‐to‐day change in winter. Comparison among no blending experiment, two fixed wave number blending experiments, and two DB experiments with and without vertically varying cutoff wave number suggests that the DB scheme with vertically averaged but temporally varying cutoff wave number results in less model bias and less disturbance to the RM dynamic balance. By reducing the forecast background error, the DB scheme can potentially provide improved first guess for a rapid‐update‐cycle weather forecast system.