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.

Evaluating the Capabilities of Soil Enthalpy, Soil Moisture and Soil Temperature in Predicting Seasonal Precipitation

Soil enthalpy （H） contains the combined effects of both soil moisture （w） and soil temperature （T） in the land surface hydrothermal process. In this study, the sensitivities of H to w and T are investigated using the multi-linear regression method. Results indicate that T generally makes positive contributions to H, while w exhibits different （positive or negative） impacts due to soil ice effects. For example, w negatively contributes to H if soil contains more ice; however, after soil ice melts, w exerts positive contributions. In particular, due to lower w interannual variabilities in the deep soil layer （i.e., the fifth layer）, H is more sensitive to T than to w. Moreover, to compare the potential capabilities of H, w and T in precipitation （P） prediction, the Huanghe-Huaihe Basin （HHB） and Southeast China （SEC）, with similar sensitivities of H to w and T, are selected. Analyses show that, despite similar spatial distributions of H-P and T-P correlation coefficients, the former values are always higher than the latter ones. Furthermore, H provides the most effective signals for P prediction over HHB and SEC, i.e., a significant leading correlation between May H and early summer （June） P. In summary, H, which integrates the effects of T and w as an independent variable, has greater capabilities in monitoring land surface heating and improving seasonal P prediction relative to individual land surface factors （e.g., T and w）.

Time lag effect of vegetation response to seasonal precipitation in the Mara River Basin

Background Mara River Basin is an ecologically fragile area in East Africa,with a pattern of alternating wet and dry seasons shaped by periodic precipitation.Considering the regional biological traits and climatic change,the vegetation’s response to seasonal variation is complicated and frequently characterized by time lags.This study analyzed the variation of the Normalized Difference Vegetation Index(NDVI)and investigated its time lag to precipitation at the monthly scale.NDVI characteristic peaks were proposed from the perspective of seasonal mechanisms and were quantified to assess the lag effect.Results The results showed that the Anomaly Vegetation Index could identify low precipitation in 2006,2009,and 2017.The NDVI showed an increasing trend in 75%of areas of the basin,while showed a decreased significance in 3.5%of areas,mainly in savannas.As to the time lag,the 1-month lag effect dominated most months,and the spatiotemporal disparities were noticeable.Another method considering the alternations of wet and dry seasons found that the time lag was approximately 30 days.Based on the time distribution of NDVI characteristic peaks,the average time lag was 35.5 days and increased with the range of seasons.Conclusions The findings confirmed an increasing trend of NDVI in most regions from 2001 to 2020,while the trends were most obvious in the downstream related to human activities.The results could reflect the time lag of NDVI response to precipitation,and the 1-month lag effect dominated in most months with spatial heterogeneity.Four NDVI characteristic peaks were found to be efficient indicators to assess the seasonal characteristics and had a great potential to quantify vegetation variation.

Seasonal Forecasts of Precipitation during the First Rainy Season in South China Based on NUIST-CFS1.0

Current dynamical models experience great difficulties providing reliable seasonal forecasts of regional/local rainfall in South China.This study evaluates seasonal forecast skill for precipitation in the first rainy season(FRS,i.e.,April–June)over South China from 1982 to 2020 based on the global real-time Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously known as SINTEX-F).The potential predictability and the practical forecast skill of NUIST-CFS1.0 for FRS precipitation remain low in general.But NUIST-CFS1.0 still performs better than the average of nine international models in terms of correlation coefficient skill in predicting the interannual precipitation anomaly and its related circulation index.NUIST-CFS1.0 captures the anomalous Philippines anticyclone,which transports moisture and heat northward to South China,favoring more precipitation in South China during the FRS.By examining the correlations between sea surface temperature(SST)and FRS precipitation and the Philippines anticyclone,we find that the model reasonably captures SST-associated precipitation and circulation anomalies,which partly explains the predictability of FRS precipitation.A dynamical downscaling model with 30-km resolution forced by the large-scale circulations of the NUIST-CFS1.0 predictions could improve forecasts of the climatological states and extreme precipitation events.Our results also reveal interesting interdecadal changes in the predictive skill for FRS precipitation in South China based on the NUIST-CFS1.0 hindcasts.These results help improve the understanding and forecasts for FRS precipitation in South China.

Spatiotemporal characteristics of seasonal precipitation and their relationships with ENSO in Central Asia during 1901–2013

The vulnerable ecosystem of the arid and semiarid region in Central Asia is sensi- tive to precipitation variations. Long-term changes of the seasonal precipitation can reveal the evolution rules of the precipitation climate. Therefore, in this study, the changes of the sea- sonal precipitation over Central Asia have been analyzed during the last century （1901-2013） based on the latest global monthly precipitation dataset Global Precipitation Climatology Centre （GPCC） Full Data Reanalysis Version 7, as well as their relations with El Ni~_o- Southern Oscillation （ENSO）. Results show that the precipitation in Central Asia is mainly concentrated in spring and summer seasons, especially in spring. For the whole study period, increasing trends were found in spring and winter, while decreasing trends were detected in summer and fall. Inter-annual signals with 3-7 years multi-periods were derived to explain the dominant components for seasonal precipitation variability. In terms of the dominant spatial pattern, Empirical orthogonal function （EOF） results show that the spatial distribution of EOF-1 mode in summer is different from those of the other seasons during 1901-2013. Moreover, significant ENSO-associated changes in precipitation are evident during the fall, winter, spring, and absent during summer. The lagged associations between ENSO and seasonal precipitation are also obtained in Central Asia. The ENSO-based composite analy- ses show that these water vapor fluxes of spring, fall and winter precipitation are mainly generated in Indian and North Atlantic Oceans during El Nino. The enhanced westerlies strengthen the western water vapor path for Central Asia, thereby causing a rainy winter.

Deep Learning for Seasonal Precipitation Prediction over China

Despite significant progress having been made in recent years,the forecast skill for seasonal precipitation over China remains limited.In this study,a deep-learning-based statistical prediction model for seasonal precipitation over China was developed.The model was trained to learn the distribution of the seasonal precipitation using simultaneous general circulation data.First,it was pre-trained with the hindcasts of several general circulation models(GCMs),and evaluation of the test set suggested that the pre-trained model could basically reproduce the GCM-predicted precipitation,with the anomaly pattern correlation coefficients(PCCs)greater than 0.80.Then,transfer learning was applied by using ECMWF Reanalysis v5(ERA5)data and gridded precipitation observational data over China,to further correct the systemic errors in the model.As a result,using general circulation fields from reanalysis as the input,this hybrid model performed reasonably well in simulating the seasonal precipitation over China,with the PCC reaching 0.71.In addition,the results using the circulation fields predicted by GCMs as the input were also assessed.In general,the proposed model improves the PCC over China by 0.10-0.13,as compared to the raw GCM outputs,for lead times of 1-4 months.This deep learning model has been used at the National Climate Center of China Meteorological Administration for the past two years to provide guidance for summer precipitation prediction over China and has performed extremely well.

Seasonal Prediction of Summer Precipitation over East Africa Using NUIST-CFS1.0

East Africa is particularly vulnerable to precipitation variability, as the livelihood of much of the population depends on rainfed agriculture. Seasonal forecasts of the precipitation anomalies, when skillful, can therefore improve implementation of coping mechanisms with respect to food security and water management. This study assesses the performance of Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUISTCFS1.0) on forecasting June–September(JJAS) seasonal precipitation anomalies over East Africa. The skill in predicting the JJAS mean precipitation initiated from 1 May for the period of 1982–2019 is evaluated using both deterministic and probabilistic verification metrics on grid cell and over six distinct clusters. The results show that NUIST-CFS1.0 captures the spatial pattern of observed seasonal precipitation climatology, albeit with dry and wet biases in a few parts of the region. The model has positive skill across a majority of Ethiopia, Kenya, Uganda, and Tanzania, whereas it doesn’t exceed the skill of climatological forecasts in parts of Sudan and southeastern Ethiopia. Positive forecast skill is found over regions where the model shows better performance in reproducing teleconnections related to oceanic SST. The prediction performance of NUIST-CFS1.0 is found to be on a level that is potentially useful over a majority of East Africa.

Quantifying the Spatial Characteristics of the Moisture Transport Affecting Precipitation Seasonality and Recycling Variability in Central Asia

Moisture contribution and transport pathways for Central Asia(CA)are quantitatively examined using the Lagrangian water cycle model based on reanalysis and observational data to explain the precipitation seasonality and the moisture transport variation during 1979-2015.Westerly-related(northwesterly and westerly)transport explains 42%of CA precipitation and dominates in southwest CA,where precipitation is greatest in the cold season.Southeast CA,including part of Northwest China,experiences its maximum precipitation in the warm season and is solely dominated by southerly transport,which explains about 48%of CA precipitation.The remaining 10%of CA precipitation is explained by northerly transport,which steadily impacts north CA and causes a maximum in precipitation in the warm season.Most CA areas are exposed to seasonally varying moisture transport,except for southeast and north CA,which are impacted by southerly and northerly transport year-round.In general,the midlatitude westerlies-driven transport and the Indian monsoon-driven southerly-related transport explain most of the spatial differences in precipitation seasonality over CA.Moreover,the contribution ratio of local evaporation in CA to precipitation exhibits significant interdecadal variability and a meridionally oriented tripole of moisture transport anomalies.Since the early 2000s,CA has experienced a decade of anomalously low local moisture contribution,which seems jointly determined by the weakened moisture contribution from midlatitudes(the Atlantic,Europe,and CA itself)and the enhanced contribution from high latitudes(West Siberia and the Arctic)and tropical areas(South Asia and the Indian Ocean).

Effects of precipitation variation on severe acid rain in southern China

Acid rain has been recognized as a serious environmental problem in China since the 1980s, but little is known about the effects of the climatic change in regional precipitation on the temporal and spatial variability of severe acid rain. We present the effects of the regional precipitation trend change on the area and intensity of severe acid rain in southern China, and the spatio-temporal distribution characteristics of SO2 and NO2 concentrations are analyzed on the basis of SO2 and NO2 column concentration data. The results are as follows. （1） The emission levels of SO2 and NO2 have reached or passed the precipitation scavenging capacity in parts of southern China owing to the emission totals of SOz and NO2 increasing from 1993 to 2004. （2） Notable changes in the proportion of cities subject to severe acid rain occurred mainly in the south of the middle-lower reaches of the Yangtze River during 1993-2004. With an abrupt change in 1999, the severe acid rain regions were mainly located in central and western China during 1993-1999 and moved obviously eastward to the south of the lower-middle reaches of the Yangtze River with the proportion of cities subject to severe acid rain increasing significantly from 2000 to 2004. （3） The spatial distribution and variation in the seasonal precipitation change rate of more than 10 mm/10a are similar to those of severe acid rain in southern China. An abrupt change in 1999 is seen for winter and summer precipitation, the same as for the proportion of cities subject to severe acid rain in southern China. The significant increase in summer storm precipitation from 1991 to 1999 mitigated the annual precipitation acidity in the south of the Yangtze River and reduced the area of severe acid rainfall. On the other hand, the decrease in storm rainfall in summer expanded the area of severe acid rainfall in the south of the Yangtze River in 2000-2006. Therefore, the change in seasonal precipitation is an important factor in the severe acid rain regions moving eastward and expanding in southern China.

Temporal and Spatial Variation Characteristics of Precipitation during Crop Growing Season in Northeast China

[Objective] The aims were to understand variation characteristics of water resources and provide theoretical guidance for the formulation of agricultural irrigation methods.[Method] Taking the precipitation records during crop growing season（from April to September）observed by 177 weather stations from 1971 to 2008 in the three provinces of Northeast China（Heilongjiang,Jilin and Liaoning）as research data,annual change and spatial distribution characteristics of precipitation during crop growing season were analyzed by means of small grid interpolation and climatic trend rate.[Result] The precipitation during crop growing season general exhibited the decreasing trend and the precipitation trend rate was-8.6 mm/10a in Northeast China.In addition,there was lack of rain from 1971 to 1980 and relatively abundant of rain during 1981 and 1990 respectively.Moreover,the precipitation obviously exhibited decreasing trend from 1991 to 2008.But the decreasing trend was inconsistent in spatial distributions,that was,the precipitation slightly increased in relatively rainless areas and obviously decreased in relatively rainy areas.[Conclusion] The areas with obvious decreasing trend of precipitation during crop growing season are the main grain producing zones in Northeast China,so the problem of food production security caused by the precipitation changes should be paid enough attention.

Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia

Global warming and climate change is one of the most extensively researched and discussed topical issues affecting the environment.Although there are enough historical evidence to support the theory that climate change is a natural phenomenon,many research scientists are widely in agreement that the increase in temperature in the 20 th century is anthropologically related.The associated effects are the variability of rainfall and cyclonic patterns that are being observed globally.In Southeast Asia the link between global warming and the seasonal atmospheric flow during the monsoon seasons shows varying degree of fuzziness.This study investigates the impact of climate change on the seasonality of monsoon Asia and its effect on the variability of monsoon rainfall in Southeast Asia.The comparison of decadal variation of precipitation and temperature anomalies before the 1970 s found general increases which were mostly varying.But beyond the 1970 s,global precipitation anomalous showed increases that almost corresponded with increases in global temperature anomalies for the same period.There are frequent changes and a shift westward of the Indian summer monsoon.Although precipitation is observed to be 70%below normal levels,in some areas the topography affects the intensity of rainfall.These shifting phenomenon of other monsoon season in the region are impacting on the variability of rainfall and the onset of monsoons in Southeast Asia and is predicted to delay for 15 days the onset of the monsoon in the future.The variability of monsoon rainfall in the SEA region is observed to be decadal and the frequency and intensity of intermittent flooding of some areas during the monsoon season have serious consequences on the human,financial,infrastructure and food security of the region.

Ground-Based Radar Reflectivity Mosaic of Mei-yu Precipitation Systems over the Yangtze River–Huaihe River Basins

The 3D radar reflectivity produced by a mosaic software system, with measurements from 29 operational weather radars in the Yangtze River–Huaihe River Basins（YRHRB） during the mei-yu season of 2007, is compared to coincident TRMM PR observations in order to evaluate the value of the ground-based radar reflectivity mosaic in characterizing the 3D structures of mei-yu precipitation. Results show reasonable agreement in the composite radar reflectivity between the two datasets,with a correlation coefficient of 0.8 and a mean bias of -1 dB. The radar mosaic data at constant altitudes are reasonably consistent with the TRMM PR observations in the height range of 2–5 km, revealing essentially the same spatial distribution of radar echo and nearly identical histograms of reflectivity. However, at altitudes above 5 km, the mosaic data overestimate reflectivity and have slower decreasing rates with height compared to the TRMM PR observations. The areas of convective and stratiform precipitation, based on the mosaic reflectivity distribution at 3-km altitude, are highly correlated with the corresponding regions in the TRMM products, with correlation coefficients of 0.92 and 0.97 and mean relative differences of -7.9% and -2.5%, respectively. Finally, the usefulness of the mosaic reflectivity at 3-km altitude at 6-min intervals is illustrated using a mesoscale convective system that occurred over the YRHRB.

RELATIONSHIP BETWEEN DIPOLE OSCILLATION OF SSTA OF INDIAN OCEAN REGION AND PRECIPITATION AND TEMPERATURE IN CHINA

The work is a general survey using SSTA data of the Indian Ocean and of precipitation at 160Chinese weather stations over 1951～1997 (47 years). It reveals that the dipole oscillation of SST, especially the dipole index of March～May, in the eastern and western parts of the ocean correlates well with the precipitation during the June～August raining season in China. As shown in analysis of 500-hPa Northern Hemisphere geopotential height height by NCEP for 1958～1995, the Indian Ocean dipole index (IODI) is closely related with geopotential height anomalies in the middle- and higher- latitudes in the Eurasian region. As a negative phase year of IODI corresponds to significant Pacific-Japan (P J) wavetrain, it is highly likely that the SST for the dipole may affect the precipitation in China through the wavetrain. Additionally, correlation analysis of links between SST dipole index of the Indian Ocean region and air temperature in China also shows good correlation between the former and wintertime temperature in southern China.

IMPACT OF SSTA OF SOUTHERN HEMISPHERE ON FLOOD SEASON PRECIPITATION ANOMALIES IN YUNNAN

Based on the reanalysis data of monthly mean global SST and wind from the NCEP/NCAR and the observation data of rain seasons in 124 stations of Yunnan province from 1961 to 2006, we applied the analytical methods of correlation analysis and composite analysis and a significance testing method to two sets of samples of average differences. The goal is to investigate into the influence of the Southern Hemispheric(SH) SST on the summer precipitation in Yunnan from January to May so as to identify the key time and marine regions. Physical mechanisms are obtained by analyzing the influence of sea level wind and the key marine regions on the precipitation during Yunnan's rain season.Results show that there is indeed significant relationship between the SST in SH and summer precipitation in Yunnan.The key areas for influencing the summer precipitation are mainly distributed in a region called "West Wind Drift" in the SH, including the Southeast Indian, southern Australia, west coast of eastern Pacific off Chile, Peru and the southwest Atlantic Magellan. Besides, the most significant marine region is the west coast of Chile and Peru(cold-current areas of the eastern Pacific). Diagnostic analysis results also showed that monsoons in the Bay of Bengal, a cross-equatorial flow in the Indian Ocean near the equator and southwest monsoon in India weaken during the warm phase of the Peruvian cold current in the eastern Pacific. Otherwise, they strengthen.

PRELIMINARY DISCUSSIONS OF BASIC CLIMATIC CHARACTERISTICS OF PRECIPITATION DURING RAINING SEASONS IN REGIONS SOUTH OF CHANGJIANG RIVER AND ITS RELATIONSHIP WITH SST ANOMALIES

Basic climatic characteristics are analyzed concerning the precipitation anomalies in raining seasons over regions south of the Changjiang River (the Yangtze). It finds that the regions are the earliest in eastern China where raining seasons begin and end. Precipitation there tends to decrease over the past 50 years. Waters bounded by 9(S -1(S, 121(E - 129(E are the key zones of SST anomalies that affect the precipitation in these regions over May ~ July in preceding years. Long-term air-sea interactions make it possible for preceding SST anomalies to affect the general circulation that come afterwards, causing precipitation anomalies in the raining seasons in regions south of the Changjiang River in subsequent years.

A SEASONAL INFLUENZA THEORY AND MATHEMATICAL MODEL INCORPORATING METEOROLOGICAL AND SOCIO-BEHAVIORAL FACTORS

On the basis of a comprehensive literature review and data analysis of global influenza surveillance, a transmission theory based numerical model is developed to understand the causative factors of influenza seasonality and the biodynamical mechanisms of seasonal flu. The model is applied to simulate the seasonality and weekly activity of influenza in different areas across all continents and climate zones around the world. Model solution and the good matches between model output and actual influenza indexes affirm that influenza activity is highly auto-correlative and relies on determinants of a broad spectrum. Internal dynamic resonance; variations of meteorological elements (solar radiation, precipitation and dewpoint); socio-behavioral influences and herd immunity to circulating strains prove to be the critical explanatory factors of the seasonality and weekly activity of influenza. In all climate regions, influenza activity is proportional to the exponential of the number of days with precipitation and to the negative exponential of quarter power of sunny hours. Influenza activity is a negative exponential function of dewpoint in temperate and arctic regions and an exponential function of the absolute deviation of dewpoint from its annual mean in the tropics. Epidemics of seasonal influenza could be deemed as the consequence of the dynamic resonance and interactions of determinants. Early interventions (such as opportune vaccination, prompt social distancing, and maintaining incidence well below a baseline) are key to the control and prevention of seasonal influenza. Moderate amount of sunlight exposure or Vitamin D supplementation during rainy and short-day photoperiod seasons, more outdoor activities, and appropriate indoor dewpoint deserve great attention in influenza prevention. To a considerable degree, the study reveals the mechanism of influenza seasonality, demonstrating a potential for influenza activity projection. The concept and algorithm can be explored for further applications.

THE INTERANNUAL AND DECADAL VARIABILITY OF PRECIPITATION FOR YUNNAN PROVINCE IN RAINY SEASON AND ITS RELATIONSHIP WITH TROPICAL UPPER LAYER TEAT CONTENT

Based on the monthly precipitation data of 126 observation stations from 1961 to 2000 in Yunnan Province, the interannual and decadal variability of precipitation in rainy seasons are studied by using wavelet analysis. It is shown that there is a 2-6 year oscillation at the interannual time scales and a quasi-30 year oscillation at the decadal time scales. These periodic oscillations relate to the distribution of tropical heat content. When the precipitation is much more (less) than normal, the upper seawater is colder (warmer) in almost all the tropical Indian Ocean, and warmer (colder) in the western Pacific as well as colder (warmer) in the eastern Pacific. The key areas of the anomaly heat content distribution that have significant correlation to the Yunnan precipitation in rainy season are in the southern hemispheric Indian Ocean with a dipole pattern in the winter as well as in the deep basin of the South China Sea (SCS) before the Yunnan rainy season begins. Therefore, the anomalous distributions of the heat content in the southern Indian Ocean and the SCS In winter are good indicators for predicting drought or flood in Yunnan Province in the following rainy season.

PREDICTION OF FLOOD SEASON PRECIPITATION IN SOUTHWEST CHINA BASED ON IMPROVED PSO-PLS

In order to achieve the best predictive effect of the Partial Least Squares(PLS) regression model, Particle Swarm Optimization(PSO) algorithm is applied to automatically filter the optimal subset of a set of candidate factors of PLS regression model in this study. An improved version of the Particle Swarm Optimization-Partial Least Squares(PSO-PLS) regression model is applied to the station data of precipitation in Southwest China during flood season.Using the PSO-PLS regression method, the prediction of flood season precipitation in Southwest China has been studied. By introducing the precipitation period series of the mean generating function(MGF) extension as an alternative factor, the MGF improved PSO-PLS regression model was also built up to improve the prediction results.Randomly selected 10%, 20%, 30% of the modeling samples were used as a test trial; random cross validation was conducted on the MGF improved PSO-PLS regression model. The results show that the accuracy of PSO-PLS regression model and the MGF improved PSO-PLS regression model are better than that of the traditional PLS regression model.The training results of the three prediction models with regard to the regional and single station precipitation are considerable, whereas the forecast results indicate that the PSO-PLS regression method and the MGF improved PSO-PLS regression method are much better than the traditional PLS regression method. The MGF improved PSO-PLS regression model has the best forecast performance on precipitation anomaly during the flood season in the southwest of China among three models. The average precipitation(PS score) of 36 stations is 74.7. With the increase of the number of modeling samples, the PS score remained stable. This shows that the PSO algorithm is objective and stable. The MGF improved PSO-PLS regression prediction model is also showed to have good prediction stability and ability.

The Operational Forecasting of Total Precipitation in Flood Seasons (April to September) of 5 Years (1983-1987)

Ⅰ.INTRODUCTION We have discovered that there exists a good corresponding relationship between theanomalous axes of soil temperature at a depth of 1.6m in winter (December to February) andprecipitations in following flood season (Tang et al., 1982a). We have also designed a simplethermodynamical model and applied it to the forecasting of precipitations in the flood season(Tang et al., 1982 b,c). The practical forecast started from 1975. Before 1980, however, therewere only 40-50 stations in China for measuring the soil temperature at a 1.6m depth. Since1980, the stations have been increased to a total of about 180, but no available mean valueshad been obtained from newly added stations before 1982. Therefore the analysis and map-ping of anomalies of soil temperature was not performed until 1983, and from then on theprecision of analysis has been greatly improved. The following is the actual situation of forecast in five years from 1983 to 1987.

Analysis on the Precipitation Characteristics in the Rainy Season in Liupanshui City in Recent 50 Years

[Objective] The research aimed to study the variation rule of precipitation in the rainy season in Liupanshui City in recent 50 years. [Method] Based on the monthly precipitation data from three observatories (Liuzhi, Panxian and Shuicheng) of Liupanshui City from May to September during 1960-2009, the interannual, interdecadal variation and mutation characteristics of precipitation in the rainy season in Liupanshui City in recent 50 years were analyzed by using the linear tendency estimation, sliding T-test and Morlet wavelet analysis method. [Result] The rainfall in the rainy season in Liupanshui City in recent 50 years presented the decline trend, and the linear tendency rate was -15.4 mm/10 a. The precipitation in the rainy season in Liupanshui City had the obvious interannual and interdecadal variation characteristics. It was the obvious rainless period in the metaphase of 1960s, and the precipitation was comparatively more in late 1960s. It was the relatively rainless period in the whole 1970s. From late 1970s to late 1980s, the precipitation in the rainy season entered into the pluvial period, and it was the period when the precipitation was the most in recent 50 years. The precipitation was relatively less from late 1980s to metaphase of 1990s. It was the pluvial period in the middle and late periods of 1990s, and it was the rainless period when entered into the 21st century. The sliding T-test showed that the precipitation mutation point in the rainy season in Liupanshui City in recent 50 years was in 2002. The wavelet analysis showed that the precipitation in the rainy season in Liupanshui City had the significant multiple time scale characteristic. In the interdecadal scale, the precipitation had the significant 16-year periodic oscillation which stably existed in 50 years. In the interannual scale, the precipitation had the quasi-8-year periodic oscillation. [Conclusion] The research provided the scientific basis for the accurate forecast of drought and flood disasters, disaster prevention and reduction in the city.