Prediction and predictability of a catastrophic local extreme precipitation event through cloud-resolving ensemble analysis and forecasting with Doppler radar observations

Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately. In this paper, we simulate an extreme precipitation event with ensemble Kalman filter(En KF) assimilation of Doppler radial-velocity observations, and analyze the uncertainties of the assimilation. The results demonstrate that, without assimilation radar data, neither a single initialization of deterministic forecast nor an ensemble forecast with adding perturbations or multiple physical parameterizations can predict the location of strong precipitation. However, forecast was significantly improved with assimilation of radar data, especially the location of the precipitation. The direct cause of the improvement is the buildup of a deep mesoscale convection system with En KF assimilation of radar data. Under a large scale background favorable for mesoscale convection, efficient perturbations of upstream mid-low level meridional wind and moisture are key factors for the assimilation and forecast. Uncertainty still exists for the forecast of this case due to its limited predictability. Both the difference of large scale initial fields and the difference of analysis obtained from En KF assimilation due to small amplitude of initial perturbations could have critical influences to the event's prediction. Forecast could be improved through more cycles of En KF assimilation. Sensitivity tests also support that more accurate forecasts are expected through improving numerical models and observations.

Doppler Radar Analysis on A Local Heavy Precipitation in Jieyang Area of Guangdong

By using Doppler weather radar data,the meso-scale characteristics of extremely heavy rainstorm process which happened suddenly in Jieyang urban area on July 31,2008 were analyzed.The results showed that the radar echo only needed 20 minutes from the generation to the strong echo which quickly strengthened above 50 dBz.The storm center went down south and went up north near Jieyang City all the time.The component which moved eastward was very tiny,and the heavy precipitation echo stagnated.In this heavy precipitation process,the characteristics types of radial velocity which were favorable to the generation and development of heavy precipitation echo appeared alternately each other.The radial velocity's characteristics types were the first type headwind zone,the second type headwind zone,meso-scale convergence type and cyclonic convergence and so on.Thus,this heavy precipitation process which broke the record happened.The analyses showed that the headwind zone which developed vigorously and the convergence which had influx and outflux airflow in the vertical direction of headwind zone made obvious contributions to the precipitation.

Expression of transforming growth factor-β in local bony callus in traumatic brain injury combined with extremity fracture in rats

Objective To investigate gene expression of transforming growth factor-β(TGF-β)in local bony callus in tracumatic brain in jury combined with extremity long bone fracture in rats.Methods Eighty male SD rats were randomized into 2 even

Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection

Central Asia(CA)is highly sensitive and vulnerable to changes in precipitation due to global warming,so the projection of precipitation extremes is essential for local climate risk assessment.However,global and regional climate models often fail to reproduce the observed daily precipitation distribution and hence extremes,especially in areas with complex terrain.In this study,we proposed a statistical downscaling(SD)model based on quantile delta mapping to assess and project eight precipitation indices at 73 meteorological stations across CA driven by ERA5 reanalysis data and simulations of 10 global climate models(GCMs)for present and future(2081-2100)periods under two shared socioeconomic pathways(SSP245 and SSP585).The reanalysis data and raw GCM outputs clearly underestimate mean precipitation intensity(SDII)and maximum 1-day precipitation(RX1DAY)and overestimate the number of wet days(R1MM)and maximum consecutive wet days(CWD)at stations across CA.However,the SD model effectively reduces the biases and RMSEs of the modeled precipitation indices compared to the observations.Also it effectively adjusts the distributional biases in the downscaled daily precipitation and indices at the stations across CA.In addition,it is skilled in capturing the spatial patterns of the observed precipitation indices.Obviously,SDII and RX1DAY are improved by the SD model,especially in the southeastern mountainous area.Under the intermediate scenario(SSP245),our SD multi-model ensemble projections project significant and robust increases in SDII and total extreme precipitation(R95PTOT)of 0.5 mm d^(-1) and 19.7 mm,respectively,over CA at the end of the 21st century(2081-2100)compared to the present values(1995-2014).More pronounced increases in indices R95PTOT,SDII,number of very wet days(R10MM),and RX1DAY are projected under the higher emission scenario(SSP585),particularly in the mountainous southeastern region.The SD model suggested that SDII and RX1DAY will likely rise more rapidly than those projected by previous model simulations over CA during the period 2081-2100.The SD projection of the possible future changes in precipitation and extremes improves the knowledge base for local risk management and climate change adaptation in CA.

Effects of corner modifications on wind loads and local pressures on walls of tall buildings

In this study, the aerodynamic characteristics of tall buildings with corner modifications (e.g., local wind force coefficients, mean pressure distributions, normalized power spectrum density, and extreme local pressure) were examined. Wind tunnel experiments were conducted to measure the wind pressures on building models with different heights and recessed corners of different ratios. At a wind direction of a = 0° (i.e., wind blowing on the front of a building), corner modifications effectively reduced wind forces in all cases. Specifically, small corner modification ratios reduced wind forces more effectively than their larger counterparts. However, corner modifications resulted in extreme local pressure on building surfaces. In addition, for small corner modification ratios, the probability of extreme local pressure occurring at a = 0° was high. This probability was also high for large corner modification ratios at a = 15° (i.e., wind blowing slightly obliquely on the front of a building) because wind blowing obliquely creates substantial vortex shedding on one side surface and extreme negative pressure over one building side surface. Results of computational fluid dynamic modeling were adopted to determine details of the aerodynamic characteristics of tall buildings with corner modifications.

Assessment of Central Asian heat extremes by statistical downscaling:Validation and future projection for 2015-2100

Increasing heatwaves and extreme temperatures have recently been observed across Central Asia(CA).Accurately assessing and projecting the changing climate extremes at the local(station)scale required for climate risk management are therefore highly important.However,global and regional climate models often fail to represent the statistical distributions of observed daily extreme variables and hence extremes in complex terrain.In this work,we developed a statistical downscaling(SD)model to project summer daily maximum temperature(Tmax)and heatwave indices for 65 meteorological stations in CA toward 2100.The SD model involves first-order autoregression and multiple linear regression using large-scale Tmax and circulation indices(Cis)as predictors,and the model is cross-validated against historical observations.The local Tmax and heatwave indices are then projected for 2015-2100 driven by the output of a global climate model(CNRM-CM6-1)under four Shared Socioeconomic Pathways(SSP126,SSP245,SSP370,and SSP585).The application of the SD model significantly improves forecasting of the probability distribution(10th/90th percentiles)of Tmax at stations,particularly across mountainous regions.The model also captures interannual variability and the long-term trend in Tmax,consistent with synoptic-scale inputs.SD projections demonstrate strong warming trends of summer Tmax in CA toward 2100 with rates between 0.35-0.64℃ per decade based on the SSP245 and SSP370 seenarios.Consequently,heatwave occurrence is projected to rise by 1.0-5.0 and 2.0-7.0 d per decade under the SSP245 and SSP370 scenarios,respectively,by 2100.Duration,intensity,and amplitude of heatwaves rise at greater rates under higher-emission scenarios,particularly in southeastern CA.The proposed SD model serves as a useful tool for assessing local climate extremes,which are needed for regional risk management and policymaking for adaption to climate change.