Analysis on Change Tendency of the Precipitation Resource during Growth Period of the Conventional Crops in Plain Area before Taihang Mountains

[Objective] The aim was to study change tendency of the precipitation resource during growth period of the conventional crops in plain area before Taihang Mountains. [ Method] Based on daily precipitation data at Shijiazhuang meteorological station in recent 51 years, average rainfall dudng growth periods of the 9 kinds of conventional crops was obtained. Precipitation tendency dudng growth periods of the 9 kinds of conventional crops in plain area before Taihang Mountains was analyzed by Mann-Kendall nonparametric test. [ Result] Seen from rainfall during growth pedods of the different crops, rainfall was the least during the growth period of winter wheat, followed by summer corn. Rainfall during growth peri- ods of the cotton, oil plant, vegetable, fruit tree, potato, rice and legumes was more. Under different guaranteed rates, precipitation change also had difference. Rainfall change during growth periods of the wheat and corn was bigger, and rainfall change during growth period of the rice was smaller. Change degree of the precipitation during growth periods of the cotton, oil plant, vegetable, fruit tree and legumes was equivalent, while precipitation change during growth period of the potato was the biggest. Seen from change tendency of the precipitation during growth periods of the different crops, precipitation in the growth period of winter wheat was increasing at a speed of 0.62 mm/a. However, precipitation in growth periods of the other crops had a decreasing tendency. Precipitation in the growth periods of summer corn and legumes decreased at the same speed which was 2.11 mm/a, while precipitation in growth periods of the cotton, oil plant, vegetable, fruit tree, potato and rice decreased insignificantly. [ Con dusion] The study laid foundation for determination of the agricultural irrigation water and provided theoretical reference for regional agricultural water-saving.

The Variation Rule and Anomaly Features of the Available Precipitation in the Growth Period of Crops in the Middle and Southern Part of Ningxia

[ Objective] The aim was to study the available precipitation and its abnormal characteristics during the growth period of crops in the mid- dle and southern part of Ningxia. [ Methed] Through Takahashi＇s evaporation equation, linear trend analysis, Mann -Kendall, Lepage, wavelet analysis, the characteristics of available precipitation and its abnormal characteristics during its growth period in Ningxia were analyzed based on monthly precipitation （March- September）, temperature, and NCAR/NCEP reanalysis data in 9 observation stations from 1961 to 2010. E Result l In recent 50 years, the available precipitation during the growth period of crops in central drought area and south hilly area varied a lot, more in the south and less in the north. The available precipitation in these two areas was reducing and varied a lot in different ages. The available precipitation was less in the middle drought region after 1970s and in the southern hilly region after 1990s and the available precipitation in middle drought area changed significantly. The available precipitation in the two areas appeared in July, August, and September, above 76% of general growth period, while that in March, April and May was 14% lower than that in growth period. No abrupt changes in central drought area and south hilly area. There were the periods of 5 -7 a and 2 -3 a oscillations in the middle drought region, 2 -3 a and 10 -12 a oscillations in the southern hilly region. There were negative anomaly field at 500hPa height in high-precipitation years between the Baikal and the China＇s northwestern, and there were positive anomaly field in low-precipitation years. [ Cenclusion] The study provided reference for the reasonable utilization of available water resources in cen- tral and south Ningxia.

Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach

Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final(IMERG-F)precipitation product,as well as hydrological simulations in the Ebinur Lake Basin,further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region.The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas,and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.

Differences in spring precipitation over southern China associated with multiyear La Ni?a events

Composite analyses were performed in this study to reveal the difference in spring precipitation over southern China during multiyear La Ni?a events during 1901 to 2015. It was found that there is significantly below-normal precipitation during the first boreal spring, but above-normal precipitation during the second year. The difference in spring precipitation over southern China is correlative to the variation in western North Pacific anomalous cyclone(WNPC), which can in turn be attributed to the different sea surface temperature anomaly(SSTA) over the Tropical Pacific. The remote forcing of negative SSTA in the equatorial central and eastern Pacific and the local air-sea interaction in the western North Pacific are the usual causes of WNPC formation and maintenance.SSTA in the first spring is stronger than those in the second spring. As a result, the intensity of WNPC in the first year is stronger, which is more likely to reduce the moisture in southern China by changing the moisture transport, leading to prolonged precipitation deficits over southern China. However, the tropical SSTA signals in the second year are too weak to induce the formation and maintenance of WNPC and the below-normal precipitation over southern China. Thus, the variation in tropical SSTA signals between two consecutive springs during multiyear La Ni?a events leads to obvious differences in the spatial pattern of precipitation anomaly in southern China by causing the different WNPC response.

Effect of drying-wetting cycles on pore characteristics and mechanical properties of enzyme-induced carbonate precipitation-reinforced sea sand

Enzyme-induced carbonate precipitation(EICP)is an emanating,eco-friendly and potentially sound technique that has presented promise in various geotechnical applications.However,the durability and microscopic characteristics of EICP-treated specimens against the impact of drying-wetting(D-W)cycles is under-explored yet.This study investigates the evolution of mechanical behavior and pore charac-teristics of EICP-treated sea sand subjected to D-W cycles.The uniaxial compressive strength(UCS)tests,synchrotron radiation micro-computed tomography(micro-CT),and three-dimensional(3D)recon-struction of CT images were performed to study the multiscale evolution characteristics of EICP-reinforced sea sand under the effect of D-W cycles.The potential correlations between microstructure characteristics and macro-mechanical property deterioration were investigated using gray relational analysis(GRA).Results showed that the UCS of EICP-treated specimens decreases by 63.7% after 15 D-W cycles.The proportion of mesopores gradually decreases whereas the proportion of macropores in-creases due to the exfoliated calcium carbonate with increasing number of D-W cycles.The micro-structure in EICP-reinforced sea sand was gradually disintegrated,resulting in increasing pore size and development of pore shape from ellipsoidal to columnar and branched.The gray relational degree suggested that the weight loss rate and UCS deterioration were attributed to the development of branched pores with a size of 100-1000 m m under the action of D-W cycles.Overall,the results in this study provide a useful guidancee for the long-term stability and evolution characteristics of EICP-reinforced sea sand under D-W weathering conditions.

A dual-RPA based lateral flow strip for sensitive,on-site detection of CP4-EPSPS and Cry1Ab/Ac genes in genetically modified crops

Traditional transgenic detection methods require high test conditions and struggle to be both sensitive and efficient.In this study,a one-tube dual recombinase polymerase amplification(RPA)reaction system for CP4-EPSPS and Cry1Ab/Ac was proposed and combined with a lateral flow immunochromatographic assay,named“Dual-RPA-LFD”,to visualize the dual detection of genetically modified(GM)crops.In which,the herbicide tolerance gene CP4-EPSPS and the insect resistance gene Cry1Ab/Ac were selected as targets taking into account the current status of the most widespread application of insect resistance and herbicide tolerance traits and their stacked traits.Gradient diluted plasmids,transgenic standards,and actual samples were used as templates to conduct sensitivity,specificity,and practicality assays,respectively.The constructed method achieved the visual detection of plasmid at levels as low as 100 copies,demonstrating its high sensitivity.In addition,good applicability to transgenic samples was observed,with no cross-interference between two test lines and no influence from other genes.In conclusion,this strategy achieved the expected purpose of simultaneous detection of the two popular targets in GM crops within 20 min at 37°C in a rapid,equipmentfree field manner,providing a new alternative for rapid screening for transgenic assays in the field.

Cloud-Type-Dependent 1DVAR Algorithm for Retrieving Hydrometeors and Precipitation in Tropical Cyclone Nanmadol from GMI Data

Understanding the structure of tropical cyclone(TC)hydrometeors is crucial for detecting the changes in the distribution and intensity of precipitation.In this study,the GMI brightness temperature and cloud-dependent 1DVAR algorithm were used to retrieve the hydrometeor profiles and surface rain rate of TC Nanmadol(2022).The Advanced Radiative Transfer Modeling System(ARMS)was used to calculate the Jacobian and degrees of freedom(△DOF)of cloud water,rainwater,and graupel for different channels of GMI in convective conditions.The retrieval results were compared with the Dual-frequency Precipitation Radar(DPR),GMI 2A,and IMERG products.It is shown that from all channels of GMI,rain water has the highest△DOF,at 1.72.According to the radiance Jacobian to atmospheric state variables,cloud water emission dominates its scattering.For rain water,the emission of channels 1–4 dominates scattering.Compared with the GMI 2A precipitation product,the 1DVAR precipitation rate has a higher correlation coefficient(0.713)with the IMERG product and can better reflect the location of TC precipitation.Near the TC eyewall,the highest radar echo top indicates strong convection.Near the melting layer where Ka-band attenuation is strong,the double frequency difference of DPR data reflects the location of the melting.The DPR drop size distribution(DSD)product shows that there is a significant increase in particle size below the melting layer in the spiral rain band.Thus,the particle size may be one of the main reasons for the smaller rain water below the melting layer retrieved from 1DVAR.

Precipitation and anthropogenic activities regulate the changes of NDVI in Zhegucuo Valley on the southern Tibetan Plateau

Whether climate change or anthropogenic activities play a more pivotal role in regulating vegetation growth on the Tibetan Plateau is still controversial.A better understanding on grassland changes at a fine scale may provide important guidance for local government policy and grassland management.Using two of the most reliable satellite NDVI products(MODIS NDVI and SPOT NDVI),we evaluated the dynamic of grasslands in the Zhegucuo valley on the southern Tibetan Plateau from 2000 to 2020,and analyzed its driving factors and relative influences of climate change and anthropogenic activities.Here,the key indicators of climate change were assumed to be precipitation and temperature.The main results were:(1)the grassland NDVI in Zhegucuo valley did not reflect a significant temporal change during the last 21 years.The variation of precipitation during the early growing season(GSP)resembled that of NDVI,and the GSP was positively correlated with NDVI.At the pixel level,the partial correlation analysis showed that 37.79%of the pixels depicted a positive relationship between GSP and NDVI,while 11.32%of the pixels showed a negative relationship between temperature during the early growing season(GST)and NDVI.(2)In view of the spatial distribution,the areas mainly controlled by GSP were generally distributed in the southern part,while those affected by GST stood in the eastern part,mainly around the Zhegucuo lake where most population in Cuomei County settled down.(3)Decreasing NDVI trends were mainly occurred in alpine steppe at lower elevations rather than alpine meadow at higher elevations.(4)The residual trend(RESTREND)analysis further indicated that the anthropogenic activities played a more pivotal role in regulating the annual changes of NDVI rather than climate factors in this area.Future studies should pay more attention on climate extremes rather than the simple temporal trends.Also,the influence of human activities on alpine grassland needs to be accessed and fully considered in future sustainable management.

Synergistic Interdecadal Evolution of Precipitation over Eastern China and the Pacific Decadal Oscillation during 1951-2015

By using the multi-taper method(MTM)of singular value decomposition(SVD),this study investigates the interdecadal evolution(10-to 30-year cycle)of precipitation over eastern China from 1951 to 2015 and its relationship with the North Pacific sea surface temperature(SST).Two significant interdecadal signals,one with an 11-year cycle and the other with a 23-year cycle,are identified in both the precipitation and SST fields.Results show that the North Pacific SST forcing modulates the precipitation distribution over China through the effects of the Pacific Decadal Oscillation(PDO)-related anomalous Aleutian low on the western Pacific subtropical high(WPSH)and Mongolia high(MH).During the development stage of the PDO cold phase associated with the 11-year cycle,a weakened WPSH and MH increased the precipitation over the Yangtze River Basin,whereas an intensified WPSH and MH caused the enhanced rain band to move northward to North China during the decay stage.During the development stage of the PDO cold phase associated with the 23-year cycle,a weakened WPSH and MH increased the precipitation over North China,whereas an intensified WPSH and the weakened MH increased the precipitation over South China during the decay stage.The 11-year and 23-year variabilities contribute differently to the precipitation variations in the different regions of China,as seen in the 1998flooding case.The 11-year cycle mainly accounts for precipitation increases over the Yangtze River Basin,while the 23-year cycle is responsible for the precipitation increase over Northeast China.These results have important implications for understanding how the PDO modulates the precipitation distribution over China,helping to improve interdecadal climate prediction.

Spatio-Temporal Characteristics of Heavy Precipitation Forecasts from ECMWF in Eastern China

This study examines the spatio-temporal characteristics of heavy precipitation forecasts in eastern China from the European Centre for Medium-Range Weather Forecasts(ECMWF) using the time-domain version of the Method for Object-based Diagnostic Evaluation(MODE-TD). A total of 23 heavy rainfall cases occurring between 2018 and 2021 are selected for analysis. Using Typhoon “Rumbia” as a case study, the paper illustrates how the MODE-TD method assesses the overall simulation capability of models for the life history of precipitation systems. The results of multiple tests with different parameter configurations reveal that the model underestimates the number of objects’ forecasted precipitation tracks, particularly at smaller radii. Additionally, the analysis based on centroid offset and area ratio tests for different classified precipitation objects indicates that the model performs better in predicting large-area, fast-moving, and longlifespan precipitation objects. Conversely, it tends to have less accurate predictions for small-area, slow-moving, and shortlifespan precipitation objects. In terms of temporal characteristics, the model overestimates the forecasted movement speed for precipitation objects with small-area, slow movement, or both long and short lifespans while underestimating it for precipitation with fast movement. In terms of temporal characteristics, the model tends to overestimate the forecasted movement speed for precipitation objects with small-area, slow movement, or both long and short lifespans while underestimating it for precipitation with fast movement. Overall, the model provides more accurate predictions for the duration and dissipation of precipitation objects with large-area or long-lifespan(such as typhoon precipitation) while having large prediction errors for precipitation objects with small-area or short-lifespan. Furthermore, the model’s simulation results regarding the generation of precipitation objects show that it performs relatively well in simulating the generation of large-area and fast-moving precipitation objects. However, there are significant differences in the forecasted generation of small-area and slow-moving precipitation objects after 9 hours.

Antecedent Precipitation Index to Estimate Soil Moisture and Correlate as a Triggering Process in the Occurrence of Landslides

Landslides are highly dangerous phenomena that occur in different parts of the world and pose significant threats to human populations. Intense rainfall events are the main triggering process for landslides in urbanized slope regions, especially those considered high-risk areas. Various other factors contribute to the process;thus, it is essential to analyze the causes of such incidents in all possible ways. Soil moisture plays a critical role in the Earth’s surface-atmosphere interaction systems;hence, measurements and their estimations are crucial for understanding all processes involved in the water balance, especially those related to landslides. Soil moisture can be estimated from in-situ measurements using different sensors and techniques, satellite remote sensing, hydrological modeling, and indicators to index moisture conditions. Antecedent soil moisture can significantly impact runoff for the same rainfall event in a watershed. The Antecedent Precipitation Index (API) or “retained rainfall,” along with the antecedent moisture condition from the Natural Resources Conservation Service, is generally applied to estimate runoff in watersheds where data is limited or unavailable. This work aims to explore API in estimating soil moisture and establish thresholds based on landslide occurrences. The estimated soil moisture will be compared and calibrated using measurements obtained through multisensor capacitance probes installed in a high-risk area located in the mountainous region of Campos do Jordão municipality, São Paulo, Brazil. The API used in the calculation has been modified, where the recession coefficient depends on air temperature variability as well as the climatological mean temperature, which can be considered as losses in the water balance due to evapotranspiration. Once the API is calibrated, it will be used to extrapolate to the entire watershed and consequently estimate soil moisture. By utilizing recorded mass movements and comparing them with API and soil moisture, it will be possible to determine thresholds, thus enabling anticipation of landslide occurrences.

Adaptation analysis and fusion correction method of CMIP6 precipitation simulation data on the Qinghai-Tibetan Plateau

In order to obtain more accurate precipitation data and better simulate the precipitation on the Tibetan Plateau,the simulation capability of 14 Coupled Model Intercomparison Project Phase 6(CMIP6)models of historical precipitation(1982-2014)on the Qinghai-Tibetan Plateau was evaluated in this study.Results indicate that all models exhibit an overestimation of precipitation through the analysis of the Taylor index,temporal and spatial statistical parameters.To correct the overestimation,a fusion correction method combining the Backpropagation Neural Network Correction(BP)and Quantum Mapping(QM)correction,named BQ method,was proposed.With this method,the historical precipitation of each model was corrected in space and time,respectively.The correction results were then analyzed in time,space,and analysis of variance(ANOVA)with those corrected by the BP and QM methods,respectively.Finally,the fusion correction method results for each model were compared with the Climatic Research Unit(CRU)data for significance analysis to obtain the trends of precipitation increase and decrease for each model.The results show that the IPSL-CM6A-LR model is relatively good in simulating historical precipitation on the Qinghai-Tibetan Plateau(R=0.7,RSME=0.15)among the uncorrected data.In terms of time,the total precipitation corrected by the fusion method has the same interannual trend and the closest precipitation values to the CRU data;In terms of space,the annual average precipitation corrected by the fusion method has the smallest difference with the CRU data,and the total historical annual average precipitation is not significantly different from the CRU data,which is better than BP and QM.Therefore,the correction effect of the fusion method on the historical precipitation of each model is better than that of the QM and BP methods.The precipitation in the central and northeastern parts of the plateau shows a significant increasing trend.The correlation coefficients between monthly precipitation and site-detected precipitation for all models after BQ correction exceed 0.8.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Impacts of Future Changes in Heavy Precipitation and Extreme Drought on the Economy over South China and Indochina

Heavy precipitation and extreme drought have caused severe economic losses over South China and Indochina(INCSC)in recent decades.Given the areas with large gross domestic product(GDP)in the INCSC region are distributed along the coastline and greatly affected by global warming,understanding the possible economic impacts induced by future changes in the maximum consecutive 5-day precipitation(RX5day)and the maximum consecutive dry days(CDD)is critical for adaptation planning in this region.Based on the latest data released by phase 6 of the Coupled Model Intercomparison Project(CMIP6),future projections of precipitation extremes with bias correction and their impacts on GDP over the INCSC region under the fossil-fueled development Shared Socioeconomic Pathway(SSP5-8.5)are investigated.Results indicate that RX5day will intensify robustly throughout the INCSC region,while CDD will lengthen in most regions under global warming.The changes in climate consistently dominate the effect on GDP over the INCSC region,rather than the change of GDP.If only considering the effect of climate change on GDP,the changes in precipitation extremes bring a larger impact on the economy in the future to the provinces of Hunan,Jiangxi,Fujian,Guangdong,and Hainan in South China,as well as the Malay Peninsula and southern Cambodia in Indochina.Thus,timely regional adaptation strategies are urgent for these regions.Moreover,from the sub-regional average viewpoint,over two thirds of CMIP6 models agree that maintaining a lower global warming level will reduce the economic impacts from heavy precipitation over the INCSC region.

The clutch size,incubation behavior of Reeves’s Pheasant(Syrmaticus reevesii) and their responses to ambient temperature and precipitation

Weather conditions play a pivotal role in embryo development and parental incubation costs,potentially impacting the clutch size and incubation behavior of birds.Understanding these effects is crucial for bird conservation.Reeves’ s Pheasant(Syrmaticus reevesii) is a threatened species endemic to China,which is characterized by female-only incubation.However,there is a lack of information regarding the impact of weather conditions on clutch size and incubation behavior in this species.Using satellite tracking,we tracked 27 wild female Reeves’ s Pheasants from 2020 to 2023 in Hubei Province,China.We explored their clutch size and incubation behavior,as well as their responses to ambient temperature and precipitation.Clutch size averaged 7.75 ±1.36,had an association with average ambient temperature and average daily precipitation during the egglaying period,and was potentially linked to female breeding attempts.Throughout the incubation period,females took an average of 0.73 ±0.46 recesses every 24 h,with an average recess duration of 100.80 ±73.37 min and an average nest attendance of 92.98 ±5.27%.They showed a unimodal recess pattern in which nest departures peaked primarily between 13:00 and 16:00.Furthermore,females rarely left nests when daily precipitation was high.Recess duration and nest attendance were influenced by the interaction between daily mean ambient temperature and daily precipitation,as well as day of incubation.Additionally,there was a positive correlation between clutch size and recess duration.These results contribute valuable insights into the lifehistory features of this endangered species.

Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

Responses of Nutrients to the Precipitation Variation and Land Use in Subtropical Monsoonal Small Mountainous Rivers:A Case Study of Baixi Watershed

Small mountainous rivers are characterized by large instantaneous fluxes and susceptible to extreme weather events,which can rapidly transport materials into the sea and have a significant impact on the ecological environment of estuaries and bays.In order to investigate the seasonal characteristics of nutrients in small mountainous rivers in the subtropical monsoon region and the output pattern to the sea during heavy precipitation,surveys on the mountainous rivers were carried out in Baixi watershed in August 2020(wet season),March 2021(dry season)and June 2021(Meiyu period).The results showed that the dissolved inorganic nitrogen(DIN)of the rivers has an average concentration of 752μg L^(−1)in the wet season and 1472μg L^(−1)in the dry season.The concentrations of dissolved inorganic phosphorus(DIP)in wet season and dry season were 63μg L^(−1)and 51μg L^(−1),respectively.Influenced by the changes of land use in sub-watersheds,DIN concentrations in the mainstream increased from 701μg L^(−1)in the upper reaches to 1284μg L^(−1)in the middle reaches.Two rainstorms during the Meiyu period in the watershed caused the pulse runoff in the river.The maximum daily runoff reached 70 times that before rains.The maximum daily fluxes of DIN and DIP were 109 and 247 times that before rains,respectively.In view that the watershed experienced several rainstorms in the wet season,the river,with pulse runoff,carries a large amount of nutrients into the sea in a short time,which will have a significant impact on the environment of Sanmen bay and its adjacent sea.

Comparison of a Spectral Bin and Two Multi-Moment Bulk Microphysics Schemes for Supercell Simulation:Investigation into Key Processes Responsible for Hydrometeor Distributions and Precipitation

There are more uncertainties with ice hydrometeor representations and related processes than liquid hydrometeors within microphysics parameterization(MP)schemes because of their complicated geometries and physical properties.Idealized supercell simulations are produced using the WRF model coupled with“full”Hebrew University spectral bin MP(HU-SBM),and NSSL and Thompson bulk MP(BMP)schemes.HU-SBM downdrafts are typically weaker than those of the NSSL and Thompson simulations,accompanied by less rain evaporation.HU-SBM produces more cloud ice(plates),graupel,and hail than the BMPs,yet precipitates less at the surface.The limiting mass bins(and subsequently,particle size)of rimed ice in HU-SBM and slower rimed ice fall speeds lead to smaller melting-level net rimed ice fluxes than those of the BMPs.Aggregation from plates in HU-SBM,together with snow–graupel collisions,leads to a greater snow contribution to rain than those of the BMPs.Replacing HU-SBM’s fall speeds using the formulations of the BMPs after aggregating the discrete bin values to mass mixing ratios and total number concentrations increases net rain and rimed ice fluxes.Still,they are smaller in magnitude than bulk rain,NSSL hail,and Thompson graupel net fluxes near the surface.Conversely,the melting-layer net rimed ice fluxes are reduced when the fall speeds for the NSSL and Thompson simulations are calculated using HU-SBM fall speed formulations after discretizing the bulk particle size distributions(PSDs)into spectral bins.The results highlight precipitation sensitivity to storm dynamics,fall speed,hydrometeor evolution governed by process rates,and MP PSD design.

Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks

This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users.

Mechanism of Diabatic Heating on Precipitation and the Track of a Tibetan Plateau Vortex over the Eastern Slope of the Tibetan Plateau

Existing studies contend that latent heating(LH)will replace sensible heating(SH)to become the dominant factor affecting the development of the Tibetan Plateau vortex(TPV)after it moves off the Tibetan Plateau(TP).However,in the process of the TPV moving off the TP requires that the airmass traverse the eastern slope of the Tibetan Plateau(ESTP)where the topography and diabatic heating(DH)conditions rapidly change.How LH gradually replaces SH to become the dominant factor in the development of the TPV over the ESTP is still not very clear.In this paper,an analysis of a typical case of a TPV with a long life history over the ESTP is performed by using multi-sourced meteorological data and model simulations.The results show that SH from the TP surface can change the TPV-associated precipitation distribution by temperature advection after the TPV moves off the TP.The LH can then directly promote the development of the TPV and has a certain guiding effect on the track of the TPV.The SH can control the active area of LH by changing the falling area of the TPV-associated precipitation,so it still plays a key role in the development and tracking of the TPV even though it has moved out of the main body of the TP.