The Evolutionary Modeling and Short-range Climatic Prediction for Meteorological Element Time Series

The time series of precipitation in flood season (May-September) at WuhanStation, which is set as an example of the kind of time series with chaos characters, is split intotwo parts: One includes macro climatic timescale period waves that are affected by some relativelysteady climatic factors such as astronomical factors (sunspot, etc.), some other known and/orunknown factors, and the other includes micro climatic timescale period waves superimposed on themacro one. The evolutionary modeling (EM), which develops from genetic programming (GP), is supposedto be adept at simulating the former part because it creates the nonlinear ordinary differentialequation (NODE) based upon the data series. The natural fractals (NF) are used to simulate thelatter part. The final prediction is the sum of results from both methods, thus the model canreflect multi-time scale effects of forcing factors in the climate system. The results of thisexample for 2002 and 2003 are satisfactory for climatic prediction operation. The NODE can suggestthat the data vary with time, which is beneficial to think over short-range climatic analysis andprediction. Comparison in principle between evolutionary modeling and linear modeling indicates thatthe evolutionary one is a better way to simulate the complex time series with nonlinearcharacteristics.

Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models

The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R2 of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.

Application of Predictive Model for Efficient Cassava (Manihot esculenta Crantz) Yield in the Face of Climate Variability in Enugu State, Nigeria

Climate variability as occasioned by conditions such as extreme rainfall and temperature, rainfall cessation, and irregular temperatures has considerable impact on crop yield and food security. This study develops a predictive model for cassava yield (Manihot esculenta Crantz) amidst climate variability in rainfed zone of Enugu State, Nigeria. This study utilized data of climate variables and tonnage of cassava yield spanning from 1971 to 2012;as well as information from a questionnaire and focus group discussion from farmers across two seasons in 2023 respectively. Regression analysis was employed to develop the predictive model equation for seasonal climate variability and cassava yield. The rainfall and temperature anomalies, decadal change in trend of cassava yield and opinion of farmers on changes in rainfall season were also computed in the study. The result shows the following relationship between cassava and all the climatic variables: R2 = 0.939;P = 0.00514;Cassava and key climatic variables: R2 = 0.560;P = 0.007. The result implies that seasonal rainfall, temperature, relative humidity, sunshine hours and radiation parameters are key climatic variables in cassava production. This is supported by computed rainfall and temperature anomalies which range from −478.5 to 517.8 mm as well as −1.2˚C to 2.3˚C over the years. The questionnaire and focus group identified that farmers experienced at one time or another, late onset of rain, early onset of rain or rainfall cessation over the years. The farmers are not particularly sure of rainfall and temperature characteristics at any point in time. The implication of the result of this study is that rainfall and temperature parameters determine the farming season and quantity of productivity. Hence, there is urgent need to address the situation through effective and quality weather forecasting network which will help stem food insecurity in the study area and Nigeria at large. The study made recommendations such as a comprehensive early warning system on climate variability incidence which can be communicated to local farmers by agro-meteorological extension officers, research on crops that can grow with little or no rain, planning irrigation scheme, and improving tree planting culture in the study area.

Decadal prediction skill for Eurasian surface air temperature in CMIP6 models

评估CMIP6年代际预测试验对季节平均SAT的预测技巧的结果表明:模式不能有效预测冬季和秋季SAT的年代际变率.IPSL-CM6A-LR和多模式集合平均对于春季SAT展现了预测技巧,其中对于变率的预测技巧好于振幅的结果.基于蒙古和我国华北地区的显著预测技巧,模式对于夏季SAT表现出最佳的预测水平.与外部强迫相比,模式对于SAT的预测技巧可能来自初始化.模式中的一个明显系统性误差值得注意,即模式中冬季SAT的变率可以持续到其他季节,而在观测中其他季节的SAT变化与冬季SAT相对独立.

Study on Ann-Based Multi-Step Prediction Model of Short-Term Climatic Variation

In the context of 1905–1995 series from Nanjing and Hangzhou, study is undertaken of estab-lishing a predictive model of annual mean temperature in 1996–2005 to come over the Changjiang (Yangtze River) delta region through mean generating function and artificial neural network in combination. Results show that the established model yields mean error of 0.45°C for their abso-lute values of annual mean temperature from 10 yearly independent samples (1986–1995) and the difference between the mean predictions and related measurements is 0.156°C. The developed model is found superior to a mean generating function regression model both in historical data fit-ting and independent sample prediction. Key words Climate trend prediction. Mean generating function (MGF) - Artificial neural network (ANN) - Annual mean temperature (AMT)

Distribution Prediction of Suitable Growth Area for Eucommia ulmoides in China under Climatic Change Background

[ Objective] The research aimed to study distribution prediction of suitable growth area for Eucommia ulmoides in China under climatic change background. [ Method] By using the maximum entropy model and many kinds of climate change scenarios, we predicted current and future distribution pattems of suitable growth area for Eucommia ulmoides in China and its change process. [ Result ] At present, highly suitable growth area of E. ulmoides mainly distributed in Sichuan, Shaanxi and Chongqing, Under climate change background, total suitable growth areas in future three decades all drastically reduced when compared with that at present. It was noteworthy that moderately and highly suitable growth areas of wild E. ulmoides all disappeared, and junction between Shaanxi and Gansu and Taibai Mountain would be stable suitable growth area of wild E. ulmoides. [ Condusioa] The research could provide useful reference data for investigation, protection and sustainable development of the wild E. ulmoides resources.

Temporal-spatial Distribution and Short-range Prediction Indicators of Hail Weather in East Central Haixi Prefecture of Qinghai Province

[ Objective] The study aimed to discuss the temporal-spatial distribution and short-range prediction indicators of hail weather in east central Haixi Prefecture of Qinghai Province. [Method] Using hail data of six stations in east central Haixi Prefecture from 1960 to 2010, the temporal and spatial distribution of hail weather was analyzed firstly. Afterwards, based on the high-altitude factual data of 30 case studies of hail during 2006 -2010, its high-altitude and ground weather situation and physical quantity field were studied to summarize short-term circulation pattern and shod- range prediction characteristics of hail weather. [ Result] In east central Haixi, hail appeared from April to September, and it was most frequently from May to August. Meanwhile, hail was frequent from 14：00 to 20：00. Among the six stations, hail was most frequent in Tianjun but least frequent in Wulan. Moreover, hail disaster mainly occurred in Wulan and Tianjun. In addition, there were three typos of circulation pattern of hail weather at 500 hPa. Hail mainly occurred under the effect of northwest airflow, and it had shortwave trough, cold center or trough, jet stream core or one of the three. Hail appeared frequently under the situation of upper-level divergence and low-level convergence, and abundant water vapor and water vapor flux convergence at low levels were important conditions for hailing. [ Conclusion] The research could provide scientific references for improving the accuracy of hail forecast.

Prediction of Climate Change in Yangtze-Huaihe Region under the Background of Global Warming

Based on the prediction results of over twenty new climate models provided by Intergovernmental Panel on Climate Change(IPCC) ,the climate change trends in Yangtze-Huaihe region during 2011-2100 were analyzed under the SRES A1B scenario. The results showed that annual mean temperature in Yangtze-Huaihe region would go up gradually under the background of global warming,and temperature increase rose from southeast to northwest,while annual average temperature would increase by 3.3 ℃ in the late 20th century. Meanwhile,annual average precipitation would rise persistently,and precipitation increase would go up with the increase of latitude and the lapse of time,being obviously strengthened after 2041.

Analysis and prediction of global vegetation dynamics:past variations and future perspectives

Spatiotemporal dynamic vegetation changes affect global climate change,energy balances and the hydrological cycle.Predicting these dynamics over a long time series is important for the study and analysis of global environmental change.Based on leaf area index(LAI),climate,and radiation flux data of past and future scenarios,this study looked at historical dynamic changes in global vegetation LAI,and proposed a coupled multiple linear regression and improved gray model(CMLRIGM)to predict future global LAI.The results show that CMLRIGM predictions are more accurate than results predicted by the multiple linear regression(MLR)model or the improved gray model(IGM)alone.This coupled model can effectively resolve the problem posed by the underestimation of annual average of global vegetation LAI predicted by MLR and the overestimate predicted by IGM.From 1981 to 2018,the annual average of LAI in most areas covered by global vegetation(71.4%)showed an increase with a growth rate of 0.0028 a-1;of this area,significant increases occurred in 34.42%of the total area.From 2016 to 2060,the CMLRIGM model has predicted that the annual average global vegetation LAI will increase,accounting for approximately 68.5%of the global vegetation coverage,with a growth rate of 0.004 a-1.The growth rate will increase in the future scenario,and it may be related to the driving factors of the high emission scenario used in this study.This research may provide a basis for simulating spatiotemporal dynamic changes in global vegetation conditions over a long time series.

Hybrid Seasonal Prediction of Meridional Temperature Gradient Associated with“Warm Arctic-Cold Eurasia”

The meridional gradient of surface air temperature associated with“Warm Arctic–Cold Eurasia”(GradTAE)is closely related to climate anomalies and weather extremes in the mid-low latitudes.However,the Climate Forecast System Version 2(CFSv2)shows poor capability for GradTAE prediction.Based on the year-to-year increment approach,analysis using a hybrid seasonal prediction model for GradTAE in winter(HMAE)is conducted with observed September sea ice over the Barents–Kara Sea,October sea surface temperature over the North Atlantic,September soil moisture in southern North America,and CFSv2 forecasted winter sea ice over the Baffin Bay,Davis Strait,and Labrador Sea.HMAE demonstrates good capability for predicting GradTAE with a significant correlation coefficient of 0.84,and the percentage of the same sign is 88%in cross-validation during 1983−2015.HMAE also maintains high accuracy and robustness during independent predictions of 2016−20.Meanwhile,HMAE can predict the GradTAE in 2021 well as an experiment of routine operation.Moreover,well-predicted GradTAE is useful in the prediction of the large-scale pattern of“Warm Arctic–Cold Eurasia”and has potential to enhance the skill of surface air temperature occurrences in the east of China.

Multi-Year Simulations and Experimental Seasonal Predictions for Rainy Seasons in China by Using a Nested Regional Climate Model (RegCM_NCC) Part Ⅱ:The Experimental Seasonal Prediction

A nested regional climate model has been experimentally used in the seasonal prediction at the China National Climate Center （NCC） since 2001. The NCC/IAP （Institute of Atmospheric Physics） T63 coupled GCM （CGCM） provides the boundary and initial conditions for driving the regional climate model （RegCM_NCC）. The latter has a 60-km horizontal resolution and improved physical parameterization schemes including the mass flux cumulus parameterization scheme, the turbulent kinetic energy closure scheme （TKE） and an improved land process model （LPM）. The large-scale terrain features such as the Tibetan Plateau are included in the larger domain to produce the topographic forcing on the rain-producing systems. A sensitivity study of the East Asian climate with regard to the above physical processes has been presented in the first part of the present paper. This is the second part, as a continuation of Part Ⅰ. In order to verify the performance of the nested regional climate model, a ten-year simulation driven by NCEP reanalysis datasets has been made to explore the performance of the East Asian climate simulation and to identify the model＇s systematic errors. At the same time, comparative simulation experiments for 5 years between the RegCM2 and RegCM_NCC have been done to further understand their differences in simulation performance. Also, a ten-year hindcast （1991-2000） for summer （June-August）, the rainy season in China, has been undertaken. The preliminary results have shown that the RegCM_NCC is capable of predicting the major seasonal rain belts. The best predicted regions with high anomaly correlation coefficient （ACC） are located in the eastern part of West China, in Northeast China and in North China, where the CGCM has maximum prediction skill as well. This fact may reflect the importance of the largescale forcing. One significant improvement of the prediction derived from RegCM_NCC is the increase of ACC in the Yangtze River valley where the CGCM has a very low, even a negative, ACC. The reason behind this improvement is likely to be related to the more realistic representation of the large-scale terrain features of the Tibetan Plateau. Presumably, many rain-producing systems may be generated over or near the Tibetan Plateau and may then move eastward along the Yangtze River basin steered by upper-level westerly airflow, thus leading to enhancement of rainfalls in the mid and lower basins of the Yangtze River. The real-time experimental predictions for summer in 2001, 2002, 2003 and 2004 by using this nested RegCM-NCC were made. The results are basically reasonable compared with the observations.

A Review of Seasonal Climate Prediction Research in China

The ultimate goal of climate research is to produce climate predictions on various time scales. In China, efforts to predict the climate started in the 1930 s. Experimental operational climate forecasts have been performed since the late 1950 s,based on historical analog circulation patterns. However, due to the inherent complexity of climate variability, the forecasts produced at that time were fairly inaccurate. Only from the late 1980 s has seasonal climate prediction experienced substantial progress, when the Tropical Ocean and Global Atmosphere project of the World Climate Research program（WCRP） was launched. This paper, following a brief description of the history of seasonal climate prediction research, provides an overview of these studies in China. Processes and factors associated with the climate variability and predictability are discussed based on the literature published by Chinese scientists. These studies in China mirror aspects of the climate research effort made in other parts of the world over the past several decades, and are particularly associated with monsoon research in East Asia. As the climate warms, climate extremes, their frequency, and intensity are projected to change, with a large possibility that they will increase. Thus, seasonal climate prediction is even more important for China in order to effectively mitigate disasters produced by climate extremes, such as frequent floods, droughts, and the heavy frozen rain events of South China.

The Prediction of Non-stationary Climate Series Based on Empirical Mode Decomposition

This paper proposes a new approach which we refer to as ＂segregated prediction＂ to predict climate time series which are nonstationary. This approach is based on the empirical mode decomposition method （EMD）, which can decompose a time signal into a finite and usually small number of basic oscillatory components. To test the capabilities of this approach, some prediction experiments are carried out for several climate time series. The experimental results show that this approach can decompose the nonstationarity of the climate time series and segregate nonlinear interactions between the different mode components, which thereby is able to improve prediction accuracy of these original climate time series.

The Predictability Problems in Numerical Weather and Climate Prediction

The uncertainties caused by the errors of the initial states and the parameters in the numerical model are investigated. Three problems of predictability in numerical weather and climate prediction are proposed, which are related to the maximum predictable time, the maximum prediction error, and the maximum admissible errors of the initial values and the parameters in the model respectively. The three problems are then formulated into nonlinear optimization problems. Effective approaches to deal with these nonlinear optimization problems are provided. The Lorenz’ model is employed to demonstrate how to use these ideas in dealing with these three problems.

Probabilistic Multimodel Ensemble Prediction of Decadal Variability of East Asian Surface Air Temperature Based on IPCC-AR5 Near-term Climate Simulations

Based on near-term climate simulations for IPCC-AR5 （The Fifth Assessment Report）, probabilistic multimodel ensemble prediction （PMME） of decadal variability of surface air temperature in East Asia （20°-50°N, 100°- 145°E） was conducted using the multivariate Gaussian ensemble kernel dressing （GED） methodology. The ensemble system exhibited high performance in hindcasting the deeadal （1981-2010） mean and trend of temperature anomalies with respect to 1961-90, with a RPS of 0.94 and 0.88 respectively. The interpretation of PMME for future decades （2006-35） over East Asia was made on the basis of the bivariate probability density of the mean and trend. The results showed that, under the RCP4.5 （Representative Concentration Pathway 4.5 W m-2） scenario, the annual mean temperature increases on average by about 1.1-1.2 K and the temperature trend reaches 0.6-0.7 K （30 yr）-1. The pattern for both quantities was found to be that the temperature increase will be less intense in the south. While the temperature increase in terms of the 30-yr mean was found to be virtually certain, the results for the 30-yr trend showed an almost 25% chance of a negative value. This indicated that, using a multimodel ensemble system, even if a longer-term warming exists for 2006-35 over East Asia, the trend for temperature may produce a negative value. Temperature was found to be more affected by seasonal variability, with the increase in temperature over East Asia more intense in autumn （mainly）, faster in summer to the west of 115°E, and faster still in autumn to the east of 115°E.

CLIMATE PREDICTION EXPERIMENT FOR TROPICAL CYCLONE GENESIS FREQUENCY USING THE LARGE-SCALE CIRCULATION FORECAST BY A COUPLED GLOBAL CIRCULATION MODEL

Based on an analysis of the relationship between the tropical cyclone genesis frequency and large-scale circulation anomaly in NCEP reanalysis, large-scale atmosphere circulation information forecast by the JAMSTEC SINTEX-F coupled model is used to build a statistical model to predict the cyclogenesis frequency over the South China Sea and the western North Pacific. The SINTEX-F coupled model has relatively good prediction skill for some circulation features associated with the cyclogenesis frequency including sea level pressure, wind vertical shear, Intertropical Convergence Zone and cross-equatorial air flows. Predictors derived from these large-scale circulations have good relationships with the cyclogenesis frequency over the South China Sea and the western North Pacific. A multivariate linear regression(MLR) model is further designed using these predictors. This model shows good prediction skill with the anomaly correlation coefficient reaching, based on the cross validation, 0.71 between the observed and predicted cyclogenesis frequency. However, it also shows relatively large prediction errors in extreme tropical cyclone years(1994 and 1998, for example).

A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.

A Review on Seasonal Climate Prediction

The characters of experiments of prediction on monthly mean atmospheric circulation, seasonal predic-tion and seasonal forecast of summer rainfall over China are summarized in the present paper. The results demonstrate that climate prediction can be made only if the time average is taken. However, the improvement of the skill score of seasonal forecasts depends on the studies on physical parameters and mechanisms that are responsible for seasonal anomaly. Finally, the predictability of seasonal forecast of temperature and precipitation is discussed, including effectiveness and accuracy. Key words Seasonal climate prediction - Summer rainfall over China - Predictability Supported by “ National Key Programme for Developing Basic Sciences”—Research on the Forma tion Mechanism and Prediction Theory of Severe Climate Disasters in China (G199804900) and “ National Key Project”—Studies on Short Term Climate Prediction System in China

A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China

A new nudging scheme is proposed for the operational prediction system of the National Marine Environmental Forecasting Center(NMEFC)of China,mainly aimed at improving El Niño–Southern Oscillation(ENSO)and Indian Ocean Dipole(IOD)predictions.Compared with the origin nudging scheme of NMEFC,the new scheme adds a nudge assimilation for wind components,and increases the nudging weight at the subsurface.Increasing the nudging weight at the subsurface directly improved the simulation performance of the ocean component,while assimilating low-level wind components not only affected the atmospheric component but also benefited the oceanic simulation.Hindcast experiments showed that the new scheme remarkably improved both ENSO and IOD prediction skills.The skillful prediction lead time of ENSO was up to 11 months,1 month longer than a hindcast using the original nudging scheme.Skillful prediction of IOD could be made 4–5 months ahead by the new scheme,with a 0.2 higher correlation at a 3-month lead time.These prediction skills approach the level of some of the best state-of-the-art coupled general circulation models.Improved ENSO and IOD predictions occurred across all seasons,but mainly for target months in the boreal spring for the ENSO and the boreal spring and summer for the IOD.

ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system:comparison of full-field and anomaly initialization

Model initialization is a key process of climate predictions using dynamical models. In this study, the authors evaluated the performances of two distinct initialization approaches--anomaly and full-field initializations--in ENSO predictions conducted using the IAP-DecPreS near-term climate prediction system developed by the Institute of Atmospheric Physics （lAP）. IAP-DecPreS is composed of the FGOALS-s2 coupled general circulation model and a newly developed ocean data assimilation scheme called＇ensemble optimal interpolation-incremental analysis update＇ （EnOI-IAU）. It was found that, for IAP-DecPreS, the hindcast runs using the anomaly initialization have higher predictive skills for both conventional ENSO and El Nino Modoki, as compared to using the full-field initialization. The anomaly hindcasts can predict super El Nino/La Nina 10 months in advance and have good skill for most moderate and weak ENSO events about 4-7 months in advance.The predictive skill of the anomaly hindcasts for El Nino Modoki is close to that for conventional ENSO. On the other hand, the anomaly hindcasts at 1- and 4-month lead time can reproduce the major features of large-scale patterns of sea surface temperature, precipitation and atmospheric circulation anomalies during conventional ENSO and El Nino Modoki winter.