Drought episodes over Greece as simulated by dynamical and statistical downscaling approaches

Drought over the Greek region is characterized by a strong seasonal cycle and large spatial variability. Dry spells longer than 10 consecutive days mainly characterize the duration and the intensity of Greek drought. Moreover, an increasing trend of the frequency of drought episodes has been observed, especially during the last 20 years of the 20th century. Moreover, the most recent regional circulation models (RCMs) present discrepancies compared to observed precipitation, while they are able to reproduce the main patterns of atmospheric circulation. In this study, both a statistical and a dynamical downscaling approach are used to quantify drought episodes over Greece by simulating the Standardized Precipitation Index (SPI) for different time steps (3, 6, and 12 months). A statistical downscaling technique based on artificial neural network is employed for the estimation of SPI over Greece, while this drought index is also estimated using the RCM precipitation for the time period of 1961–1990. Overall, it was found that the drought characteristics (intensity, duration, and spatial extent) were well reproduced by the regional climate models for long term drought indices (SPI12) while ANN simulations are better for the short-term drought indices (SPI3).

     

Calibration of weather radar using region probability matching method (RPMM)

In this paper, spatiotemporal variability of drought in Xilingol grassland during pasture growing season (from April to September) was investigated, using 52 years (1961–2012) of precipitation data recorded at 14 rain gauge stations in the study area. The Standardized Precipitation Index was used to compute the severity of drought. The Mann-Kendall test, the linear trend, and the sequential Mann-Kendall test were applied to standardized precipitation index (SPI) time series. The results indicate that drought has become increasingly serious on the region scale during pasture growing season, and the rate of SPI decreases ranged from ?0.112 to ?0.013 per decade. As for the MK test, most of the stations, the Z value range is from ?1.081 to ?0.005 and Kendall’s τ varies from ?0.104 to ?0.024. Meanwhile, drought is increased obviously from the northwest to the southeast region. Meanwhile, the occurrence probability of each severity class, times for reaching different drought class from any drought severity state, and residence times in each drought class have been obtained with Markov chain. Furthermore, the drought severities during pasture growing season in 2013–2016 are predicted depending on the weighted Markov chain. The results may provide a scientific basis for preventing and mitigating drought disaster.     

Improved tropical cyclone track predictions using error recycling

Summary Errors produced by a nonlinear predictive scheme contain information about both the observations and the prediction system. Therefore, its error history would be expected to contribute to increasing the skill of the predictions if it is included in the forecast. In this study an error recycling procedure is developed for tropical cyclone track prediction. Errors are defined here as differences between the model forecast and the best track position. Error histories are incorporated into a nonlinear analogue, or simplex, forecast scheme and applied to tropical cyclone track prediction, using the archives of observed position data associated with the forecast errors. Various forecast experiments of the cyclone tracks are performed: standard simplex predictions using observed positions only; simplex predictions improved by error forecasts based on libraries of both observations and the recycled forecast errors; and, finally, predictions that include NWP-model forecasts and their errors as predictors. The resulting gains in skill of predictions out to 72 hours ahead are found to be substantial. Received August 12, 1999 Revised November 5, 1999     

Evolution of meteorological drought characteristics in Vietnam during the 1961–2007 period

The drought conditions over the seven sub-climatological regions in Vietnam are examined using three meteorological drought indices: de Martonne J, PED, and Standardized Precipitation Index (SPI). According to the seasonal probabilities of drought occurrence estimated by the de Martonne index, droughts mainly occur between November and March in all the sub-regions. The PED index and the SPI index generally show high probabilities of drought occurrence from April to August and from May to October, respectively. In the southern sub-regions of Vietnam, droughts more frequently occur in El Niño years and wet conditions are more frequently observed in La Niña years. However, such El Niño–Southern Oscillation influences are not clearly observed in the northern sub-regions. During 1961–2007, droughts significantly increased in the northern part of Vietnam. In the southern regions, PED shows increasing drought conditions while J and SPI show decreasing drought trends for almost all the stations.     

Drought strategies in East Africa: The climatologist's role

The structures of the links in the chain drought, harvest failure, famine are examined. Possible interventions are feasible at the link between drought and harvest failure, to lessen or avert the impact. Such interventions, based upon knowledge of the climatology of drought-prone regions, may be discussed under three headings: short-term predictions, long-term predictions, and the analysis of spatial patterns of drought.Basically, there are three climate-based methods of producing a short-term (within growing-season) forecast of crop yield. The first is to use a water balance model to estimate soil moisture deficits. The second approach applies regression analysis techniques to the development of models of crop-climate relationships. The third is the possibility of long-range weather forecasts.Long-term predictions rely on the fact that drought occurrence is a function of the balance between rainfall and evapotranspiration. It is possible to develop scenarios of future trends in these two variables, arising from both physical and anthropogenic forcing. Some potential forcing mechanisms are discussed.Finally, analysis of spatial patterns of drought at the national level will permit deeper insight into the phenomenon. This has potential benefits for the planning process, such as the identification of drought-prone areas and of spatial compensation zones (regions commonly drought-free when the principal grain-producing areas are affected).Together, these three items form a comprehensive strategy for applied research into the physical and human factors underlying drought.     

Standardized Precipitation Index Reconstructed from Turkish Tree-Ring Widths

May–July Standardized Precipitation Index (SPI) for the land area of most of Turkey and some adjoining regions are reconstructed from tree rings for the period 1251–1998. The reconstruction was developed from principal components analysis (PCA) of four Juniperus excelsa chronologies from southwestern and south-central Turkey and is based on reliable and replicable statistical relationships between climate and tree ring growth. The SPI reconstruction shows climate variability on both interannual and interdecadal time scales. The longest period of consecutive drought years in the reconstruction (SPI threshold ≤−1) is 2 yr. These occur in 1607–1608, 1675–1676, and 1907–1908. There are five wet events (SPI threshold ≥+1) of two consecutive years each (1330–1331, 1428–1429, 1503–1504, 1629–1630, and 1913–1914). A 5-yr moving average of the reconstructed SPI shows that two sustained drought periods occurred from the mid to late 1300s and the early to mid 1900s. Both episodes are characterized by low variability.     

Probabilistic assessment of projected climatological drought characteristics over the Southeast USA

The study makes a probabilistic assessment of drought risks due to climate change over the southeast USA based on 15 Global Circulation Model (GCM) simulations and two emission scenarios. The effects of climate change on drought characteristics such as drought intensity, frequency, areal extent, and duration are investigated using the seasonal and continuous standard precipitation index (SPI) and the standard evapotranspiration index (SPEI). The GCM data are divided into four time periods namely Historical (1961–1990), Near (2010–2039), Mid (2040–2069), and Late (2070–2099), and significant differences between historical and future time periods are quantified using the mapping model agreement technique. Further, the kernel density estimation approach is used to derive a novel probability-based severity-area-frequency (PBS) curve for the study domain. Analysis suggests that future increases in temperature and evapotranspiration will outstrip increases in precipitation and significantly affect future droughts over the study domain. Seasonal drought analysis suggest that the summer season will be impacted the most based on SPI and SPEI. Projections based on SPI follow precipitation patterns and fewer GCMs agree on SPI and the direction of change compared to the SPEI. Long-term and extreme drought events are projected to be affected more than short-term and moderate ones. Based on an analysis of PBS curves, especially based on SPEI, droughts are projected to become more severe in the future. The development of PBS curves is a novel feature in this study and will provide policymakers with important tools for analyzing future drought risks, vulnerabilities and help build drought resilience. The PBS curves can be replicated for studies around the world for drought assessment under climate change.     

Drought analysis in Jordan under current and future climates

Droughts have adverse socioeconomic, agricultural, and environmental impacts that can be reduced by assessing and forecasting drought behavior. The paper presents detailed analyses of both meteorological and vegetative droughts over the period from 1970 to 2005. Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) have been used to quantify drought according to severity, magnitude and spatial distribution at the Hashemite Kingdom of Jordan. Results suggest that the country faced during the past 35 years frequent non-uniform drought periods in an irregular repetitive manner. Drought severity, magnitudes and life span increased with time from normal to extreme levels especially at last decade reaching magnitudes of more than 4. Generated NDVI maps spatial analyses estimate crop-area percentage damage due to severe and extremely severe drought events occurred during October, December, and February of 2000 to be about 10%, 45%, and 30%, respectively. In response to drought spatial extent, the paper suggest the presence of two drought types, local drought acting on one or more geographical climatic parts and national drought, of less common but more severe, that extend over the whole country. Droughts in Jordan act intensively during January, February and March and tend to shift position with time by alternative migrations from southern desert parts to northern desert parts and from the eastern desert parts to highlands and Jordan Rift Valley (JRV) at the west. The paper also investigates the potential use of Global Climate Model’s (GCM) to forecast future drought events from 2010 till 2040. Tukey HSD test indicates that ECHAM5OM GCM is capable to predicted rainfall variation at the country and suggests future droughts to become more intensive at the northern and southern desserts with 15% rainfall reduction factor, followed by 10% reduction at the JRV, and 5% at the highlands.     

MLP-based drought forecasting in different climatic regions

Water resources management is a complex task and is further compounded by droughts. This study applies a multilayer perceptron network optimized using Levenberg–Marquardt (MLP) training algorithm with a tangent sigmoid activation function to forecast quantitative values of standardized precipitation index (SPI) of drought at five synoptic stations in Iran. The study stations are located in different climatic regions based on De Martonne aridity index. In this study, running series of total precipitation corresponding to 3, 6, 9, 12, and 24?months were used and the corresponding SPIs were calculated: SPI3, SPI6, SPI9, SPI12, and SPI24. The multilayer perceptrons (MLPs) for SPIs with the 1-month lead time forecasting, were tested and validated. Four different input vectors were considered during network development. In the first model, MLP constructed by importing antecedent SPI with 1-, 2-, 3-, and 4-month time lags and antecedent precipitation with 1- and 2-month time lags (MLP1). Addition of antecedent North Atlantic Oscillation or antecedent Southern Oscillation Index with 1-month time lag or both of them to MLP1 led to MLP2, MLP3, and MLP4, respectively. The MLP models were evaluated using the root mean square error (RMSE) and the coefficient of determination (R ²). The results showed that MLP4 had a higher prediction efficiency than the other MLPs. The more satisfactory results of RMSE and R ² values of MLP4 for 1-month lead time for validation phase were equal to 0.35 and 0.92, respectively. Also, results indicated that MLPs can forecast SPI24 and SPI12 more accurately than the other SPIs.     

The role of temperature in drought projections over North America

The effects of future temperature and hence evapotranspiration increases on drought risk over North America, based on ten current (1970–1999) and ten corresponding future (2040–2069) Regional Climate Model (RCM) simulations from the North American Regional Climate Change Assessment Program, are presented in this study. The ten pairs of simulations considered in this study are based on six RCMs and four driving Atmosphere Ocean Coupled Global Climate Models. The effects of temperature and evapotranspiration on drought risks are assessed by comparing characteristics of drought events identified on the basis of Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspration Index (SPEI). The former index uses only precipitation, while the latter uses the difference (DIF) between precipitation and potential evapotranspiration (PET) as input variables. As short- and long-term droughts impact various sectors differently, multi-scale (ranging from 1- to 12-month) drought events are considered. The projected increase in mean temperature by more than 2 °C in the future period compared to the current period for most parts of North America results in large increases in PET and decreases in DIF for the future period, especially for low latitude regions of North America. These changes result in large increases in future drought risks for most parts of the USA and southern Canada. Though similar results are obtained with SPI, the projected increases in the drought characteristics such as severity and duration and the spatial extent of regions susceptible to drought risks in the future are considerably larger in the case of SPEI-based analysis. Both approaches suggest that long-term and extreme drought events are affected more by the future increases in temperature and PET than short-term and moderate drought events, particularly over the high drought risk regions of North America.     

Non-stationary Markov chains for modelling daily sunshine at São Paulo, Brazil

Summary Non-stationary, four-state Markov chains were used to model the sunshine daily ratios at São Paulo, Brazil. Fourier series were used to account for the periodic seasonal variations in the transition probabilities. All the regressions and tests, based on Generalized Linear Models, were made through the software GLIM. Significant seasonal variations were detected in the conditional probabilities, especially for the extreme categories of overcast and clear days. The fitted model provided a concise summary of the daily data, useful for short-range (1 day) sunshine predictions and simulations.With 6 Figures     

Probabilistic analysis of extreme regional meteorological droughts by L-moments in a semi-arid environment

Regional extreme value analyses of drought characteristics provide information on probabilistic nature of drought occurrence, viewed as an essential tool in drought mitigation and planning. In this paper, L-moments are used to investigate the regional characteristics and probabilistic behavior of drought severity levels, represented by the Standardized Precipitation Index (SPI) annual minima (the minimum monthly SPI value). Rainfall data of 3, 6, 12, and 24 month time scales are investigated. A regional watershed in southwestern Iran is used as a case study area. The semi-arid nature of the study area requires appropriate selection of rainfall data. The boxplot approach is used to select those months with adequate data time series for the SPI analysis. Appropriateness of the suggested data time series is discussed in the context of the research by Wu et al. (2007). Based on the results, all of the suggested time scales are found appropriate for SPI investigations. For each time scale of interest regional homogeneity is evaluated and the best regional/sub-regional probability distribution function is selected. Regional quantiles are estimated for different time scales and their variability with respect to return period is discussed.     

Technical note on a track-pattern-based model for predicting seasonal tropical cyclone activity over the western North Pacific

Recently, the National Typhoon Center (NTC) at the Korea Meteorological Administration launched a track-pattern-based model that predicts the horizontal distribution of tropical cyclone (TC) track density from June to October. This model is the first approach to target seasonal TC track clusters covering the entire western North Pacific (WNP) basin, and may represent a milestone for seasonal TC forecasting, using a simple statistical method that can be applied at weather operation centers. In this note, we describe the procedure of the track-pattern-based model with brief technical background to provide practical information on the use and operation of the model. The model comprises three major steps. First, long-term data of WNP TC tracks reveal seven climatological track clusters. Second, the TC counts for each cluster are predicted using a hybrid statistical-dynamical method, using the seasonal prediction of large-scale environments. Third, the final forecast map of track density is constructed by merging the spatial probabilities of the seven clusters and applying necessary bias corrections. Although the model is developed to issue the seasonal forecast in mid-May, it can be applied to alternative dates and target seasons following the procedure described in this note. Work continues on establishing an automatic system for this model at the NTC.     

甘肃中部春夏干旱气候分析与预测系统

利用甘肃中部13个测站历年4～6月的旬降水、温度资料，分析了干旱年、月、(旬)降水量、平均温度等气候变化特征及其空间分布。在此基础上，建立了相应的春、夏干旱短期气候预测系统模型。经业务使用，准确率较高。     

The day-to-day monitoring of the 2011 severe drought in China

Dry/wet condition has a large interannual variability. Decision-makers need to know the onset, duration, and intensity of drought, and require droughts be monitored at a daily to weekly scale. However, previous tools cannot monitor drought well at this short timescale. The Palmer Drought Severity Index has been found dissatisfactory in monitoring because of its complexity and numerous limitations. The Standardized Precipitation Index (SPI) always asks for a timescale, and precipitation is averaged over the period of the scale. Because of this, the SPI cannot be used for short scales, e.g., several days, and what it tells is the overall drought situation of the period. The weighted average of precipitation (WAP) developed by Lu (Geophys Res Lett 36:L12707, 2009) overcomes the deficiency of the SPI; it does not require a timescale, and can provide the drought (and flood) extent of each day. Therefore, the WAP can monitor drought at scales from daily to weekly, monthly, and any longer scale, and is really “flexible and versatile for all timescales”. In this study, the standardized WAP (SWAP) is used to monitor the 2011 drought over China. Drought swept the country during the year from north to south and from east to west. In spring, a once-in-a-fifty-year drought occurred over the Yangtze River basin and the southern region, causing serious shortage of drinking water for people and livestock, as well as tremendous losses in agriculture and the shipping industry. Results show that the SWAP, with its monthly mean plots, can well reproduce the seasonal shift of the 2011 drought across the country. The animation of daily plots demonstrates that the SWAP would have been able to monitor the day-to-day variation of the spring drought around the Yangtze River basin. It can provide the details of the drought, such as when the drought emerged over the region, how long it maintained there (though drought area may move back and forth with extension and contraction of the area), and when the drought relieved over the basin.     

Lightning peak current estimation using a system identification approach

Summary A system identification-based lightning peak current estimation algorithm using upper-air radiosonde observations is developed. The preceding convective and precipitative process leading to thunder cloud formation followed by the cloud electrification and the leader processes together with return stroke and the discharge process, is identified by considering it as a deterministic dynamic system, whose undisturbed and unmeasurable output signal – the lightning peak current, is contaminated with a stochastic disturbance. The model parameters determined thus, are used to predict the likely temporal lightning return stroke peak current magnitudes. Two alternative parametric estimation models namely Autoregressive with Exogeneous Input (ARX) and Autoregressive with Moving-Average Exogeneous Input (ARMAX) are used to estimate model parameters of the pilot study area and predict the likely lightning return stroke peak current in each case. The relative performances of the models are compared to determine the best model for application in 12-hour and 24-hour ahead predictions. For a short-term (12 hour) prediction, ARMAX2921 giving a best fit of 78.8429% turns out to be the most suitable model. For a longer (24 hour) prediction, the ARX291, giving a best fit of 75.0181% emerges to be the suitable model. These preliminary results indicate that lightning peak current may be estimated to a good performance using upper-air radiosonde observations.     

Seasonal forecasting of tropical cyclones in the North Indian Ocean region: the role of El Niño-Southern Oscillation

In this study, we have investigated the contribution of El Niño-Southern Oscillation (ENSO) to the North Indian Ocean (NIO) tropical cyclone (TC) activity and seasonal predictability. A statistical seasonal prediction model was developed for the NIO region tropical cyclone genesis, trajectories and landfalls using the Southern Oscillation index (SOI: as a metric of ENSO) as a predictor. The forecast model utilised kernel density estimation (KDE), a generalised additive model (GAM), Euler integration, and a country mask. TCs from the Joint Typhoon Warning Centre were analysed over the 35-year period from 1979 to 2013. KDE was used to model the distribution of cyclone genesis points and the cyclone tracks were estimated using the GAM, with velocities fit as smooth functions of location according to ENSO phase and TC season. The best predictor lead time scales for TC forecast potential were assessed from 1 to 6 months. We found that the SOI (as a proxy for ENSO) is a good predictor of TC behaviour 2-months in advance (70% skill). Two hindcast validation methods were applied to assess the reliability of the model. The model was found to be skillful in hindcasting NIO region TC activity for the pre and post monsoon season. The distribution of TC genesis, movement and landfall probabilities over the study period, as well as the hindcast probabilities of TC landfall during ENSO events, matched well against observations over most of the study domain. Overall, we found that the phase of ENSO has the potential to improve NIO region TC seasonal forecast skill by about 15% over climatological persistence.     

陇东黄土高原春末夏初旱的气候特征及预测模型

应用自然正交函数分解(EOF)方法，分析了甘肃省庆阳地区春末夏初干旱的综合评价指数DH的空间分布特征及随时间演变规律；分析了春末夏初干旱对冬小麦和玉米产量的影响；应用逐步回归方法建立了春末夏初干旱的预测模型，业务使用效果良好。     

珠江三角洲降水数值预报的初步试验

以广州区域气象中心的实时资料库和大气所的细网格降水数值预报模式为基础,在VAX6220机上建立了珠江三角洲中尺度准业务降水数值预报系统,并从1990年5月20日至6月10日进行了连续22天的试验,对结果的分析表明,系统能稳定及时地提供12小时和24小时的预报产品,对短期降水预报具有一定参考价值。