The Uncertain Value of Perfect ENSO Phase Forecasts: Stochastic Agricultural Prices and Intra-Phase Climatic Variations

Climate and crop yield variability associated with El Niño—Southern Oscillation (ENSO) are now predictable within limits. This predictability suggests a potential to tailor agricultural management to mitigate impacts of adverse conditions and to take advantage of favorable conditions. However, improved climate predictions may benefit society only with parallel advances in our ability to use this knowledge. We show that the value that will accrue to any given actor from an ENSO phase forecast should be viewed not as a known number but instead as a random draw from a distribution, even when the forecast is always correct. Forecast value depends on the highly variable contexts in which forecasts are used. Randomness in forecast value has significant implications for choices made by forecasters, forecast users and policy makers. To show randomness, we estimate potential economic values of ENSO forecasts for agricultural producers based on two realistic assumptions: the crop prices farmers receive are uncertain; and within an ENSO phase, the actual climate is variable in ways that affect profits. The use of synthetic weather and crop price series, with crop simulation models, helps show the range and likelihood of climate forecast value.     

Estimating crop top microclimates from weather station data

Abstract

A mathematical model (Microsim) was developed to estimate the microclimate at the top of nearby crops using inputs of weather station data and some knowledge about crop characteristics, such as height, albedo, and leaf area index. The model was tested using data measured simultaneously over a weather station and over each of two crops ‐ corn and soybean. Temperatures at the top of unstressed, uniform crops on level terrain within 1600 m of a recording weather station were estimated within 1.0° C 96% of the time for a corn crop and 92% of the time for a soybean crop. Winds at crop top were estimated within 0.4 m s^?1 92% of the time for corn and 100% of the time for soybean. Energy balance flux density estimates for the corn crop resulted in correlation coefficients of r > 0.89 for each of R_n, LE, H and G. microsim worked well under atmospheric conditions that ranged from very stable to unstable.

An enhancement was made to the model to describe wind and temperature profiles based on the complete fetch characteristics of the sites. This resulted in significantly better wind estimates, but had the disadvantage of requiring more information about the crop and weather station surroundings.     

Interactions Between the Atmospheric Sciences and Insurers in the United States

Insurance and insurers are directly affected and concerned about climate fluctuations in the United States. Growing losses in the 1990s awakened many in the insurance business to the enormity of weather-related problems they faced and a need for better information about climate and its fluctuations including a new potential problem, climate change. This paper presents the views of experts from the crop insurance sector, the property-casualty insurance sector, and from the atmospheric sciences sector based on atmospheric research accomplished for the insurance industry for decades. The paper addresses how climatologists and insurers can and need to work together to effectively bring understanding and wise consideration of climate conditions and their future fluctuations and extremes to insurers. Considerable climatological analyses have been employed in the past by the crop insurance industry, but less by property insurers. Insurers can adjust to a change in climate but to do so will require clear evidence, which does not exist now for the United States, and an understanding as well as wide acceptance of the on-going change by regulators and the buying public. Atmospheric scientists can help insurers to mitigate weather losses, to assess risks, to measure critical perils, to educate about risks, and to learn about critical issues like climate change and long-range forecasts.     

Temporal statistical downscaling of precipitation and temperature forecasts using a stochastic weather generator

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society(IRI). In conjunction with the GLM(generalized linear modeling) weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts(the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.     

中国当代强对流天气研究与业务进展

对当代中国几十年来强对流天气研究和业务进展做了阐述，主要包括强对流系统产生的环境背景和主要组织形态，以及具体强对流天气的有利环境条件、触发机制、卫星云图特征、多普勒天气雷达回波特征以及预报、预警技术等诸方面。总体来看，中国学者对强对流以及不同类型强对流天气（强冰雹、龙卷、雷暴大风）发生、发展的环流背景以及通过雷达和卫星观测到的组织结构及其演变特征都已有了明确认识，研究了对流系统的多种触发机制，深入认识了超级单体、飑线等对流系统的环境条件、组织结构特征和维持机制，了解了中国中尺度对流系统的组织形态和气候分布特征，获得了强冰雹、龙卷、下击暴流和雷暴大风等的雷达、卫星和闪电等的多尺度观测特征、形成机制和现场灾害调查特征，发展了各类强对流天气识别、监测和分析方法以及基于“配料法”和深度学习方法等的预报、预警技术等。因此，强对流天气业务预报水平已得到显著提升。      

强对流天气监测预报预警技术进展

强对流天气预报业务包括监测、分析、预报、预警和检验等方面。对流初生识别、对流系统强度识别和对流天气类型识别等监测技术取得新进展,综合多源资料的监测技术已应用于中国气象局中央气象台业务。对流系统的触发、发展和维持机制等获得了新认识,我国不同类型强对流天气及其环境条件统计气候特征、分析规范及相应业务产品等为业务预报提供了必要基础和技术支撑。光流法、多尺度追踪技术以及应用模糊逻辑方法的临近预报技术等有明显进展,融合短时预报技术得到广泛应用,对流可分辨高分辨率数值 (集合) 预报及其后处理产品预报试验取得了显著成效,基于数值 (集合) 预报应用模糊逻辑方法的分类强对流天气短期预报技术为业务预报提供了技术支撑。强对流天气综合监测和多尺度自适应临近预报技术、多尺度分析技术以及融合短时预报技术、发展并应用模糊逻辑等方法的、基于高分辨率数值 (集合) 模式的区分不同强度等级和极端性的分类强对流天气精细化 (概率) 预报技术等是未来发展的主要方向。     

延伸期预报中的可预报性浅析

延伸期（10-30天）预报是无缝隙集约化预报预测业务体系的重要一环,一直以来在防灾减灾科学决策中起着重要作用。然而实际预报中,其准确率较中短期天气预报和气候预测均明显偏低,原因在于随着预报时效延长,预报结果存在明显的不确定性。因此,业务预报中需要充分考虑预报对象的可预报性。本文通过总结延伸期时效可预报性的来源、主流数值模式预报性能的现状,介绍了一线业务中的预报思路及常用的可预报性参考产品,揭示了可预报性理论在延伸期预报中的应用。同时还展望了延伸期预报未来的发展方向。     

Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites

The present article is a contribution to the CLARIS WorkPackage “Climate and Agriculture”, and aims at testing whether it is possible to predict yields and optimal sowing dates using seasonal climate information at three sites (Pergamino, Marcos Juarez and Anguil) which are representative of different climate and soil conditions in Argentina. Considering that we focus on the use of climate information only, and that official long time yield series are not always reliable and often influenced by both climate and technology changes, we decided to build a dataset with yields simulated by the DSSAT (Decision Support System for Agrotechnology Transfer) crop model, already calibrated in the selected three sites and for the two crops of interest (maize and soybean). We simulated yields for three different sowing dates for each crop in each of the three sites. Also considering that seasonal forecasts have a higher skill when using the 3-month average precipitation and temperature forecasts, and that regional climate change scenarios present less uncertainty at similar temporal scales, we decided to focus our analysis on the use of quarterly precipitation and temperature averages, measured at the three sites during the crop cycle. This type of information is used as input (predictand) for non-linear statistical methods (Multivariate Adaptive Regression Splines, MARS; and classification trees) in order to predict yields and their dependency to the chosen sowing date. MARS models show that the most valuable information to predict yield amplitude is the 3-month average precipitation around flowering. Classification trees are used to estimate whether climate information can be used to infer an optimal sowing date in order to optimize yields. In order to simplify the problem, we set a default sowing date (the most representative for the crop and the site) and compare the yield amplitudes between such a default date and possible alternative dates sometimes used by farmers. Above normal average temperatures at the beginning and the end of the crop cycle lead to respectively later and earlier optimal sowing. Using this classification, yields can be potentially improved by changing sowing date for maize but it is more limited for soybean. More generally, the sites and crops which have more variable yields are also the ones for which the proposed methodology is the most efficient. However, a full evaluation of the accuracy of seasonal forecasts should be the next step before confirming the reliability of this methodology under real conditions.     

Crop yield response to economic, site and climatic variables

This paper examines the effects of climatic and non-climatic factors on the mean and variance of corn, soybean and winter wheat yield in southwestern Ontario, Canada over a period of 26 years. Average crop yields increase at a decreasing rate with the quantity of inputs used, and decrease with the area planted to the crop. Climate variables have a major impact on mean yield with the length of the growing season being the primary determinant across all three crops. Increases in the variability of temperature and precipitation decrease mean yield and increase its variance. Yield variance is poorly explained by both seasonal and monthly climate variable models. Projections of future climate change suggest that average crop yield will increase with warmer temperatures and a longer growing season which is only partially offset by forecast increases in the variability of temperature and rainfall. The projections would also depend on future technological developments, which have generated significant increases in yield over time despite changing annual weather conditions.     

The microscale cooling effects of water sensitive urban design and irrigation in a suburban environment

There is still considerable uncertainty about precipitation at high elevation in mountain terrain due to the relatively few in situ measurements available and to the particular variability of the parameter. In this study, several spatialization techniques were tested, some for climatological time scale and others for daily fields, for precipitation over the western Alps for the period of 1990–2012. The study domain and period were chosen for the quality of available in situ observations and density of the network. First, a weather-type classification was established with a technique based on canonical correlation analysis combining large- and regional-scale data. The spatialization techniques applied for the climatological time scale were adapted from the Aurelhy method which uses elevation and principal components of the topography as predictors. The spatialization techniques applied to daily fields were based on kriging of daily rain gauges and used the climatological fields as predictors. This study aims to validate the advantage of using the climatology of the weather type of the day as predictor for daily fields over a monthly climatology. The climatology of the weather type of the day seems to demonstrate some small improvement.Finally, annual means over the period of 1990–2012 were produced using several methods, including some from accumulation of daily fields and others from the spatialization of in situ station means. Precipitation at high elevations and vertical climatological gradients were particularly scrutinized. Annual means based on sums of daily fields seem to have better performances.This paper only presents results for precipitation but temperature was also analysed.     

A decision‐analytic study of the joint value of seasonal precipitation and temperature forecasts in a choice‐of‐crop problem

Abstract

A static decision‐analytic method is used to investigate the economic value of bivariate ‐ precipitation and temperature ‐ seasonal forecasts of the form currently issued by the U.S. National Weather Service. This method is applied to a corn versus spring wheat choice‐of‐crop decision‐making problem by considering a transect of four counties across the northwestern margin of the North American corn belt. Numerical results indicate that seasonal forecasts of current quality can be of appreciable value (≥$1/ha) for some locations when the optimal action chosen on the basis of climatological information is only marginally preferred to another action. Increases in forecast value follow from hypothetical increases in the quality of both the precipitation and temperature components of the forecasts in the spring wheat region, whereas forecast value increases primarily as a function of the quality of the precipitation forecasts alone in the corn belt region. The results are very sensitive to absolute and relative crop prices.     

A model for predicting actual evapotranspiration under soil water stress in a Mediterranean region

Summary In this paper a model for estimating actual evapotranspiration is developed and tested for field crops (grain sorghum and sunflower) maintained under water stress conditions. The model is based on the Penman-Monteith formulation of ET in which canopy resistance (r _c) is modeled with respect to the crop water status and local climatological conditions. The model was previously tested on reference grass; in this last case no reference was made to soil water conditions andr _c was modeled only as a function of climatological parameters. Herer _c is expressed as a function of available energy, vapour pressure deficit, aerodynamic resistance and crop water status by means of predawn leaf water potential. Results, obtained with various crop water stress intensities, show that, on a daily scale, calculated ET is 98% and 95% of the measured ET for sorghum and sunflower respectively. The correlation between daily calculated and measured ET is very high (r ² = 0.95 for sorghum andr ² = 0.98 for sunflower). On an hourly scale, the model works very well when the crops were not stressed and during the senescence stage. In case of weak and strong stress the model has to be used with some precautions.With 9 Figures     

Terminal forecasting and supersonic transport

Although an official observatory was established in Toronto in 1839, governmental attempts to organize a national meteorological service were not begun until 1871. Storm warnings and general weather forecasts for Eastern Canada were instituted in 1876, and this service was extended across the West and throughout the settled portions of the country by the early years of the 20th century. Historical climatological data were published annually after 1871, but very little statistical data, delineating the climate of the country, were available prior to 1900. A beginning was made at expanding meteorological activities into the North before World War I, but it was not until the 1920's that a significant number of observing stations were located there. By 1930 the need for extensive aviation meteorological services was becoming apparent, but the economic depression prevented an expansion of both aviation and meteorological services.     

Atmospheric effects from post-nuclear fires

During a large nuclear war, the atmosphere would be loaded with huge quantities of pollutants, which are produced by fires in urban and industrial centers, cultivated lands, forests and grasslands. Especially detrimental are the effects of light absorbing airborne particles. An analysis of the amounts of the various types of fuels which could burn in a nuclear war indicates that more than 10¹⁴ g of black smoke could be produced by fires started by the nuclear explosions. Due to this, the penetration of sunlight to the earth's surface would be reduced greatly over extended areas of the northern hemisphere, maybe even globally. This could temporarily cause extreme darkness in large areas in midlatitudes and reduce crop growth and biospheric productivity.This situation would last for several weeks and cause very anomalous meteorological conditions. Much solar radiation would be absorbed in the atmosphere instead of at the earth's surface. The land areas and lower atmosphere would, therefore, cool and the overlying atmosphere warm, creating strong vertical thermal stability in a highly polluted atmosphere. For extended periods and in large parts of the world, weather conditions would be abnormal. The resulting cold, probably freezing, temperatures at the ground would interfere severely with crop production during the growing season and cause extreme conditions for large sections of the biosphere. The combination of lack of sunlight, frost and other adverse meteorological conditions would add enormously to the already huge problems of the survivors.     

1614号超强台风“莫兰蒂”在浙江沿海降水预报偏小的原因

通过对1614号超强台风“莫兰蒂”在浙江沿海降水预报偏小的原因进行回顾,同时基于美国NOAA HYSPLIT4模式,对“莫兰蒂”水汽来源进行轨迹聚类,针对不同路径来源的水汽在“莫兰蒂”浙江沿海降水中的贡献差异进行定量分析,结果表明:数值模式对“莫兰蒂”路径预报偏西、降水预报偏小,直接影响了主观预报服务中降水强度和落区的判断,而数值产品对中纬度环流作用和浙江沿海台风倒槽的预报出现偏差,同时对1616号台风“马勒卡”路径预报偏东,导致主观预报忽略了“莫兰蒂”与其他天气系统的相互作用,影响了对降水动力和水汽因子的判断,也是降水预报偏小的原因之一。“莫兰蒂”水汽输送有3支气流,分别来自东北、东南和偏南洋(海)面,后2支气流为主要水汽来源,浙北沿海以东南向水汽输送最重要,对水汽和降水贡献高达70%以上,表现出1616号台风“马勒卡”对“莫兰蒂”在浙北沿海降水的重要作用,而浙南沿海台风本体的偏南气流与“马勒卡”的东南气流的水汽输送作用相当。可见台风降水预报中,需要密切关注台风环流与其它天气系统以及双台风间的相互作用。      

Synoptic aspects of wet and dry conditions in central Australia: observations and GCM simulations for 1 × CO2 and 2 × CO2 conditions

Synoptic climatological patterns that produce anomalous wet conditions in central Australia during the period from September to April have been studied. The analysis was done by using observed daily rainfall data at a number of stations, wind and mean sea level pressure from the European Centre for Medium Range Weather Forecasts (ECMWF), Tropical Ocean and Global Atmosphere (TOGA) data from 1985 to 1991, and the CSIRO 9-level (CSIR09) global climate model (GCM) simulated data for 1 × CO₂ and 2 × CO₂ experiments. On the basis of rainfall values above 99.5 percentile in observed and simulated data, wet days have been selected to study the synoptic-scale weather systems that produce anomalous wet events in central Australia. As the vast majority of days in central Australia are dry, the same number of days with no rainfall for both observed and simulated conditions have been selected randomly. The observed synoptic climatological patterns have been compared with the results of the control simulation of CSIRO9. A comparison between CSIRO9 simulated synoptic patterns and observed synoptic patterns reveals that the model fairly well captures the synoptic climatological characteristics which produce anomalous wet and contrasting dry weather conditions during the period from September to April. Under enhanced greenhouse experiments, the main features of the synoptic patterns are intensified both for wet and dry conditions, which result in an increase in extreme weather conditions, an increase in rainfall intensity, a spatial expansion of the heavy rainfall region during wet days, and an expansion of the dry area during dry days. During anomalous wet conditions, the low pressure area is intensified, monsoonal winds and southeasterlies are strengthened and strong wind shear over tropical Australia is simulated. During this condition, the monsoon shear line moves poleward particularly over the Northern Territory. In contrast, during dry conditions, the anticyclonic circulation over the continent is strengthened.     

A new approach to identifying the weather-crop yield functionals for assessing climate change consequences

A new approach to identifying the weather-crop yield functionals is suggested. It is shown that elimination of crop yield trends using the difference regression (the first and second orders) makes it possible to substantially increase the accuracy and reliability of estimates of climate change (variation) influence on the agriculture productivity. The methodology suggested for assessing a climate change influence is realized for the grain crops in two regions of the Russian Federation with contrast climate conditions. At the same time, it is found that short-term (up to 2–3 years) crop yield trends taken into account and related to changes in the soil effective fertility promote a noticeable increase in the quality of long-term crop yield forecasts.     

Mapping global maximum irrigation extent at 30m resolution using the irrigation performances under drought stress

Accurate global irrigation information is essential for managing water scarcity and improving food security. However, the mapping of high-resolution irrigation at the global scale is challenging due to the wide range of climate conditions, crop types and phenology, ambiguous and heterogeneous spectral features, and farming practices. Here, a robust method is proposed using irrigation performance under drought stress as a proxy for crop productivity stabilization and crop water consumption. For each irrigation mapping zone (IMZ), dry months in the 2017–2019 period and the driest months in the 2010–2019 period were identified over the growing season. The thresholds of the normalized difference vegetation index (NDVI) in the dry months from 2017 to 2019 and the NDVI deviation (NDVI_dev) in the driest month were identified to separate irrigated and rainfed cropland with samples. The final threshold from either the NDVI or the NDVI_dev of the IMZ was determined with a higher overall accuracy in separating irrigated and non-irrigated areas. The results show that the global maximum irrigation extent (GMIE) at a 30-m resolution was 23.38% of global cropland in 2010–2019, with an overall accuracy of 83.6% globally and significant regional differences in irrigation proportions ranging from 1.1% in western Africa to 100% in Old World deserts among the 110 IMZs and from 0.4% in Belarus to 80.2% in Pakistan and 100% in Egypt among 45 countries. The study quantitatively distinguished annually and intermittently irrigated regions, which had values of 42% and 58% of global cropland, respectively, by applying indicators. This method, using the NDVI and NDVI_dev thresholds, is simple, concrete and reproducible and better for zones with homogeneous weather conditions. The study offers independent, consistent and comparable information for defining the baseline, tracking changes in irrigation infrastructure, and leading future changes in how stakeholders plan and design irrigation systems.     

气象探空测风软件系统的标准化研究

高空气象观测是气象业务的基础，是天气预报、气候分析、科学研究和国际交换的气象情报和资料的主要来源。气象探空测风软件是高空气象站探空测风综合探测系统的重要组成部分，它包括了一系列严密的处理方法，同时又融汇了日益发展的计算机处理技术，其规范化、标准化程度直接影响着新一代探空系统效益的发挥。该文介绍了高空气象台站探空测风标准化软件系统的设计思路，重点在软件需求、系统结构、实现技术等方面进行阐述，旨在设计出适合于各种高空探测系统，方便实用的“标准化”探空测风软件，为进一步的软件开发工作奠定坚实的基础。     

我国现有短期气候业务预测方法综述

对我国短期气候业务预测方法的综合分析表明 :我国现有的短期气候业务预测经验和统计方法仍是主要的 ;物理因子和前兆强信号结合天气气候学分析的概念预测模型方法在业务预测中发挥重要作用 ;动力模式预测方法在业务预测中取得一定效果 .随着动力模式的进一步发展 ,短期气候业务预测将逐步走向以动力模式客观预报为指导预报的新阶段