Evaluating a modified point-based method to downscale cell-based climate variable data to high-resolution grids

To address the demand for high spatial resolution gridded climate data, we have advanced the Daymet point-based interpolation algorithm for downscaling global, coarsely gridded data with additional output variables. The updated algorithm, High-Resolution Climate Downscaler (HRCD), performs very good downscaling of daily, global, historical reanalysis data from 1° input resolution to 2.5 arcmin output resolution for day length, downward longwave radiation, pressure, maximum and minimum temperature, and vapor pressure deficit. It gives good results for monthly and yearly cumulative precipitation and fair results for wind speed distributions and modeled downward shortwave radiation. Over complex terrain, 2.5 arcmin resolution is likely too low and aggregating it up to 15 arcmin preserves accuracy. HRCD performs comparably to existing daily and monthly US datasets but with a global extent for nine daily climate variables spanning 1948–2006. Furthermore, HRCD can readily be applied to other gridded climate datasets.     

Spatio-temporal network analysis for studying climate patterns

A fast, robust and scalable methodology to examine, quantify, and visualize climate patterns and their relationships is proposed. It is based on a set of notions, algorithms and metrics used in the study of graphs, referred to as complex network analysis. The goals of this approach are to explain known climate phenomena in terms of an underlying network structure and to uncover regional and global linkages in the climate system, while comparing general circulation models outputs with observations. The proposed method is based on a two-layer network representation. At the first layer, gridded climate data are used to identify “areas”, i.e., geographical regions that are highly homogeneous in terms of the given climate variable. At the second layer, the identified areas are interconnected with links of varying strength, forming a global climate network. This paper describes the climate network inference and related network metrics, and compares network properties for different sea surface temperature reanalyses and precipitation data sets, and for a small sample of CMIP5 outputs.     

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.     

Daily relative humidity projections in an Indian river basin for IPCC SRES scenarios

A two-stage methodology is developed to obtain future projections of daily relative humidity in a river basin for climate change scenarios. In the first stage, Support Vector Machine (SVM) models are developed to downscale nine sets of predictor variables (large-scale atmospheric variables) for Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) (A1B, A2, B1, and COMMIT) to R _H in a river basin at monthly scale. Uncertainty in the future projections of R _H is studied for combinations of SRES scenarios, and predictors selected. Subsequently, in the second stage, the monthly sequences of R _H are disaggregated to daily scale using k-nearest neighbor method. The effectiveness of the developed methodology is demonstrated through application to the catchment of Malaprabha reservoir in India. For downscaling, the probable predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis data set for the period 1978–2000 and (2) simulations of the third-generation Canadian Coupled Global Climate Model for the period 1978–2100. The performance of the downscaling and disaggregation models is evaluated by split sample validation. Results show that among the SVM models, the model developed using predictors pertaining to only land location performed better. The R _H is projected to increase in the future for A1B and A2 scenarios, while no trend is discerned for B1 and COMMIT.     

Reconstructing Northern Hemisphere upper-level fields during World War II

Monthly mean fields of temperature and geopotential height (GPH) from 700 to 100 hPa were statistically reconstructed for the extratropical Northern Hemisphere for the World War II period. The reconstruction was based on several hundred predictor variables, comprising temperature series from meteorological stations and gridded sea level pressure data (1939-1947) as well as a large amount of historical upper-air data (1939-1944). Statistical models were fitted in a calibration period (1948-1994) using the NCEP/NCAR Reanalysis data set as predictand. The procedure consists of a weighting scheme, principal component analyses on both the predictor variables and the predictand fields and multiple regression models relating the two sets of principal component time series to each other. According to validation experiments, the reconstruction skill in the 1939-1944 period is excellent for GPH at all levels and good for temperature up to 500 hPa, but somewhat worse for 300 hPa temperature and clearly worse for 100 hPa temperature. Regionally, high predictive skill is found over the midlatitudes of Europe and North America, but a lower quality over Asia, the subtropics, and the Arctic. Moreover, the quality is considerably better in winter than in summer. In the 1945-1947 period, reconstructions are useful up to 300 hPa for GPH and, in winter, up to 500 hPa for temperature. The reconstructed fields are presented for selected months and analysed from a dynamical perspective. It is demonstrated that the reconstructions provide a useful tool for the analysis of large-scale circulation features as well as stratosphere-troposphere coupling in the late 1930s and early 1940s.Electronic Supplementary Material Suplementary material is available in the online version of this article at     

Evaluation of machine learning tools as a statistical downscaling tool: temperatures projections for multi-stations for Thames River Basin, Canada

Many impact studies require climate change information at a finer resolution than that provided by global climate models (GCMs). This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely single conjunctive rule learner, decision table, M5 model tree, and REPTree, and explores the impact of climate change on maximum and minimum temperatures (i.e., predictands) of 14 meteorological stations in the Upper Thames River Basin, Ontario, Canada. The data used for evaluation were large-scale predictor variables, extracted from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset and the simulations from third generation Canadian coupled global climate model. Data for four grid points covering the study region were used for developing the downscaling model. M5 model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. Hence, this technique was applied to project predictands generated from GCM using three scenarios (A1B, A2, and B1) for the periods (2046–2065 and 2081–2100). A simple multiplicative shift was used for correcting predictand values. The potential of the downscaling models in simulating predictands was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily predictands from large-scale weather variables. Trend of projected maximum and minimum temperatures was studied for historical as well as downscaled values using GCM and scenario uncertainty. There is likely an increasing trend for T _max and T _min for A1B, A2, and B1 scenarios while decreasing trend has been observed for B1 scenarios during 2081–2100.     

New precipitation and temperature grids for northern Patagonia: Advances in relation to global climate grids

Climate data of mean monthly temperature and total monthly precipitation compiled from different sources in northern Patagonia were interpolated to 20-km resolution grids over the period 1997–2010. This northern Patagonian climate grid (NPCG) improves upon previous gridded products in terms of its spatial resolution and number of contributing stations, since it incorporates 218 and 114 precipitation and temperature records, respectively. A geostatistical method using surface elevation from a Digital Elevation Model (DEM) as the ancillary variable was used to interpolate station data into even spaced points. The maps provided by NPCG are consistent with the broad spatial and temporal patterns of the northern Patagonian climate, showing a comprehensive representation of the latitudinal and altitudinal gradients in temperature and precipitation, as well as their related patterns of seasonality and continentality. We compared the performance of NPCG and various other datasets available to the climate community for northern Patagonia. The grids used for the comparison included those of the Global Precipitation Climatology Project, ERAInterim, Climate Research Unit (University of East Anglia), and University of Delaware. Based on three statistics that quantitatively assess the spatial coherence of gridded data against available observations (bias, MAE, and RMSE), NPCG outperforms other global grids. NPCG represents a useful tool for understanding climate variability in northern Patagonia and a valuable input for regional models of hydrological and ecological processes. Its resolution is optimal for validating data from the general circulation models and working with raster data derived from remote sensing, such as vegetation indices.     

Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as “field” or “global” significance. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ^° grid resolution. Monthly temperature climatology for the 1990–2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. In winter and in most regions in summer, the downscaled distributions are statistically indistinguishable from the observed ones. A systematic cold summer bias occurs in deep river valleys due to overestimated elevations, in coastal areas due probably to enhanced sea breeze circulation, and over large lakes due to the interpolation of water temperatures. Urban areas in concave topography forms have a warm summer bias due to the strong heat islands, not reflected in the observations. WRF-NOAH generates appropriate fine-scale features in the monthly temperature field over regions of complex topography, but over spatially homogeneous areas even small biases can lead to significant deteriorations relative to the driving reanalysis. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.     

Circulation dynamics and its influence on European and Mediterranean January–April climate over the past half millennium: results and insights from instrumental data, documentary evidence and coupled climate models

We use long instrumental temperature series together with available field reconstructions of sea-level pressure (SLP) and three-dimensional climate model simulations to analyze relations between temperature anomalies and atmospheric circulation patterns over much of Europe and the Mediterranean for the late winter/early spring (January–April, JFMA) season. A Canonical Correlation Analysis (CCA) investigates interannual to interdecadal covariability between a new gridded SLP field reconstruction and seven long instrumental temperature series covering the past 250 years. We then present and discuss prominent atmospheric circulation patterns related to anomalous warm and cold JFMA conditions within different European areas spanning the period 1760–2007. Next, using a data assimilation technique, we link gridded SLP data with a climate model (EC-Bilt-Clio) for a better dynamical understanding of the relationship between large scale circulation and European climate. We thus present an alternative approach to reconstruct climate for the pre-instrumental period based on the assimilated model simulations. Furthermore, we present an independent method to extend the dynamic circulation analysis for anomalously cold European JFMA conditions back to the sixteenth century. To this end, we use documentary records that are spatially representative for the long instrumental records and derive, through modern analogs, large-scale SLP, surface temperature and precipitation fields. The skill of the analog method is tested in the virtual world of two three-dimensional climate simulations (ECHO-G and HadCM3). This endeavor offers new possibilities to both constrain climate model into a reconstruction mode (through the assimilation approach) and to better asses documentary data in a quantitative way.     

Postprocessing of simulated precipitation for impact research in West Africa. Part II: A weather generator for daily data

Data from global and regional climate models refer to grid cells and, hence, are basically different from station data. This particularly holds for variables with enhanced spatio-temporal variability like precipitation. On the other hand, many applications like for instance hydrological models require atmospheric data with the statistical characteristics of station data. Here, we present a dynamical-statistical tool to construct virtual station data based on regional climate model output for tropical West Africa. This weather generator (WEGE) incorporates daily gridded rainfall from the model, an orographic term and a stochastic term, accounting for the chaotic spatial distribution of local rain events within a model grid box. In addition, the simulated probability density function of daily precipitation is adjusted to available station data in Benin. It is also assured that the generated data are still consistent with other model parameters like cloudiness and atmospheric circulation. The resulting virtual station data are in excellent agreement with various observed characteristics which are not explicitly addressed by the WEGE algorithm. This holds for the mean daily rainfall intensity and variability, the relative number of rainless days and the scaling of precipitation in time. The data set has already been used successfully for various climate impact studies in Benin.     

Spline models of contemporary, 2030, 2060 and 2090 climates for Mexico and their use in understanding climate-change impacts on the vegetation

Spatial climate models were developed for México and its periphery (southern USA, Cuba, Belize and Guatemala) for monthly normals (1961–1990) of average, maximum and minimum temperature and precipitation using thin plate smoothing splines of ANUSPLIN software on ca. 3,800 observations. The fit of the model was generally good: the signal was considerably less than one-half of the number of observations, and reasonable standard errors for the surfaces would be less than 1°C for temperature and 10–15% for precipitation. Monthly normals were updated for three time periods according to three General Circulation Models and three emission scenarios. On average, mean annual temperature would increase 1.5°C by year 2030, 2.3°C by year 2060 and 3.7°C by year 2090; annual precipitation would decrease ?6.7% by year 2030, ?9.0% by year 2060 and ?18.2% by year 2090. By converting monthly means into a series of variables relevant to biology (e. g., degree-days > 5°C, aridity index), the models are directly suited for inferring plant–climate relationships and, therefore, in assessing impact of and developing programs for accommodating global warming. Programs are outlined for (a) assisting migration of four commercially important species of pine distributed in altitudinal sequence in Michoacán State (b) developing conservation programs in the floristically diverse Tehuacán Valley, and (c) perpetuating Pinus chiapensis, a threatened endemic. Climate surfaces, point or gridded climatic estimates and maps are available at http://forest.moscowfsl.wsu.edu/climate/.     

Multidecadal variability in local growing season during 1901–2009

Global warming exerts a lengthening effect on the growing season, with observational evidences emerging from different regions over the world. However, the difficulty for a global overview of this effect for the last century arises from limited availability of the long-term daily observations. In this study, we find a good linear relationship between the start (end) date of local growing season (LGS) and the monthly mean temperature in April (October) using the global gridded daily temperature dataset for 1960–1999. Using homogenized daily temperature records from nine stations where the time series go back to the beginning of the twentieth century, we find that the rate of change in the start (end) date of the LGS for per degree warming in April (October) mean temperature keeps nearly constant throughout the time. This enables us to study LGS changes during the last century using global gridded monthly mean temperature data. The results show that during the period 1901–2009, averaged over the observation areas, the LGS length has increased by a rate of 0.89 days decade^?1, mainly due to an earlier start (?0.58 days decade^?1). This is smaller than those estimates for the late half of the twentieth century, because of multidecadal climate variability (MDV). A MDV component of the LGS index series is extracted by using Ensemble Empirical Mode Decomposition method. The MDV exhibits significant positive correlation with the Atlantic Multi–decadal Oscillation (AMO) over most of the Northern Hemisphere lands, but negative in parts of North America and Western Asia for start date of LGS. These are explained by analyzing differences in atmospheric circulation expressed by sea level pressure departures between the warm and cool phases of AMO. It is suggested that MDV in association with AMO accelerates the lengthening of LGS in Northern Hemisphere by 53 % for the period 1980–2009.     

统计降尺度法对华北地区未来区域气温变化情景的预估

迄今为止,大部分海气耦合气候模式（AOGCM）的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测。降尺度法已广泛用于弥补AOGCM在这方面的不足。作者采用统计降尺度方法对1月和7月华北地区49个气象观测站的未来月平均温度变化情景进行预估。采用的统计降尺度方法是主分量分析与逐步回归分析相结合的多元线性回归模型。首先,采用1961～2000年的 NCEP再分析资料和49个台站的观测资料建立月平均温度的统计降尺度模型,然后把建立的统计降尺度模型应用于HadCM3 SRES A2 和 B2 两种排放情景, 从而生成各个台站1950～2099年1月份和7月份温度变化情景。结果表明:在当前气候条件下,无论1月还是7月,统计降尺度方法模拟的温度与观测的温度有很好的一致性,而且在大多数台站,统计降尺度模拟气温与观测值相比略微偏低。对于未来气候情景的预估方面,无论1月还是7月,也无论是HadCM3 SRES A2 还是B2排放情景驱动统计模型,结果表明大多数的站点都存在温度的明显上升趋势,同时7月的上升趋势与1月相比偏低。     

Performance of the Rossby Centre regional atmospheric model in Southern Sweden: comparison of simulated and observed precipitation

Summary Two climate model simulations made with the Rossby Centre regional Atmospheric model version 1 (RCA1) are evaluated for the precipitation climate in Scania, southernmost Sweden. These simulations are driven by the HadCM2 and the ECHAM4/OPYC3 global circulation models (GCMs) for 10 years. Output from the global and the regional simulations are compared with an observational data set, constructed from a dense precipitation gauge network in Scania. Area-averaged time series corresponding to the size and location of the RCA1 grid points in Scania have been created (the Scanian Data Set). This data set was compared to a commonly used gridded surface climatology provided by the Climatic Research Unit (CRU). Relatively large differences were found, mainly due to the fact that the CRU-climatology uses fewer stations and lacks a correction for rain-gauge under-catch. This underlines the importance of the data set chosen for model evaluations. The validation is carried out at a large scale including the whole area of Scania and at the finest resolution of RCA1 (the grid point level). When integrated over the whole area of Scania, RCA1 improves the shape of the annual precipitation cycle and the inter-annual variability compared to output from the GCMs. The RCA1 control climate is generally too wet compared to the observations. At the grid point level, RCA1 improves the simulation of the variability compared to the GCMs. There is a strong positive correlation between precipitation and altitude in all seasons in the observations. This relationship is, however, much weaker and even reversed in the RCA1 simulations. Analysis of the dense rain gauge network reveals features of spatial variability at around 20–35km in the area and indicates that a finer resolution is needed if the spatial variability in the area is to be better captured by RCA1.     

Extra-tropical cyclones in the present and future climate: a review

Based on the availability of hemispheric gridded data sets from observations, analysis and global climate models, objective cyclone identification methods were developed and applied to these data sets. Due to the large amount of investigation methods combined with the variety of different datasets, a multitude of results exist, not only for the recent climate period but also for the next century, assuming anthropogenic changed conditions. Different thresholds, different physical quantities, and considerations of different atmospheric vertical levels add to a picture that is difficult to combine into a common view of cyclones, their variability and trends, in the real world and in GCM studies. Thus, this paper will give a comprehensive review of the actual knowledge on climatologies of mid-latitude cyclones for the Northern and Southern Hemisphere for the present climate and for its possible changes under anthropogenic climate conditions.     

Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as ‘field’ or ‘global’ significance. The block length for the local resampling tests is precisely determined to adequately account for the time series structure. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ^° grid resolution. Daily precipitation climatology for the 1990–2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. While the downscaled precipitation distributions are statistically indistinguishable from the observed ones in most regions in summer, the biases of some distribution characteristics are significant over large areas in winter. WRF-NOAH generates appropriate stationary fine-scale climate features in the daily precipitation field over regions of complex topography in both seasons and appropriate transient fine-scale features almost everywhere in summer. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.     

Trends in warm days and cold nights over the Iberian Peninsula: relationships to large-scale variables

The aims of this study are to identify the trend of warm days and cold nights over the Iberian Peninsula and to connect the variations with large-scale variables. The reasons for performing this analysis are the effects that extremes events have on different ecosystems. Here, we present the results on spatial and temporal variability of warm days (TX90), or those exceeding the 90th percentile of maximum temperature, and cold nights (TN10), or those falling below the 10th percentile of minimum temperature. The extreme indices were derived from daily observations at stations and gridded data over land area for the period 1950 to 2006. Significant trends of more warm days and fewer cold nights were found. The trend to fewer cold nights is within the interval of global results given by the IPCC AR4 report; however, the trend to warm days is greater than the corresponding global trend. The influence of large-scale variables on these extreme indices was examined by means of the Empirical Orthogonal Function, correlation, composite maps and multiple regression analyses. Changes in TX90 are connected with the Scandinavian teleconnection index and a preferred mode of geopotential height at 500 hPa over the North Atlantic. Changes in TN10 are connected with the East Atlantic teleconnection index and the leading mode of Sea Surface Temperature (SST) variability over the North Atlantic area. Based on the links between the extreme indices and the large-scale variables we derived statistical models to describe the response of TX90 and TN10 to atmospheric circulation and SST variations. The models characterized the observed variations of TX90 and TN10 reasonably well. The results of this study encourage us to analyze, in further work, how temperature extremes might change over the Iberian Peninsula under warmer climate conditions.     

The influence of synoptic airflow on UK daily precipitation extremes. Part II: regional climate model and E-OBS data validation

We investigate how well the variability of extreme daily precipitation events across the United Kingdom is represented in a set of regional climate models and the E-OBS gridded data set. Instead of simply evaluating the climatologies of extreme precipitation measures, we develop an approach to validate the representation of physical mechanisms controlling extreme precipitation variability. In part I of this study we applied a statistical model to investigate the influence of the synoptic scale atmospheric circulation on extreme precipitation using observational rain gauge data. More specifically, airflow strength, direction and vorticity are used as predictors for the parameters of the generalised extreme value (GEV) distribution of local precipitation extremes. Here we employ this statistical model for our validation study. In a first step, the statistical model is calibrated against a gridded precipitation data set provided by the UK Met Office. In a second step, the same statistical model is calibrated against 14 ERA40 driven 25?km resolution RCMs from the ENSEMBLES project and the E-OBS gridded data set. Validation indices describing relevant physical mechanisms are derived from the statistical models for observations and RCMs and are compared using pattern standard deviation, pattern correlation and centered pattern root mean squared error as validation measures. The results for the different RCMs and E-OBS are visualised using Taylor diagrams. We show that the RCMs adequately simulate moderately extreme precipitation and the influence of airflow strength and vorticity on precipitation extremes, but show deficits in representing the influence of airflow direction. Also very rare extremes are misrepresented, but this result is afflicted with a high uncertainty. E-OBS shows considerable biases, in particular in regions of sparse data. The proposed approach might be used to validate other physical relationships in regional as well as global climate models.     

A daily temperature dataset over China and its application in validating a RCM simulation

This paper describes the construction of a 0.5°×0.5°daily temperature dataset for the period of 1961- 2005 over mainland China for the purpose of climate model validation. The dataset is based on the in- terpolation from 751 observing stations in China and comprises 3 variables: daily mean,minimum,and maximum temperature.The"anomaly approach"is applied in the interpolation.The gridded climatology of 1971-2000 is first calculated and then a gridded daily anomaly for 1961-2005 is added to the climatologY to o...     

1979年夏季季风试验时期一个孟加拉湾低压中凝结加热的分布

过去不少人对台风或飓风中的凝结加热场进行了计算,得到了许多有意思的结果。但是至今还没有人研究过孟加拉湾热带低压和风暴中凝结加热场的作用。这主要是受到资料的限制。1979年夏季季风试验时期,对7月3—8日发生在孟加拉湾地区的一个季风低压进行了观测,得到了较稠密的高空资料。图1是7月7日由各种探测工具得到的资料分布图。可以看到在孟加拉湾有很稠密的高空观测。本文利用上述资料对这个孟加拉湾低压的凝结加热场进行了计算,以此了解凝结加热场对孟加拉湾低压发展的影响。