National Centers Of Environmental Prediction Research Articles

Time Series Analysis of Atmospheric Precipitation Characteristics in Western Siberia for 1979–2018 across Different Datasets

A comparative statistical analysis of the spatiotemporal variability of atmospheric precipitation characteristics (mean and extreme values) in Western Siberia was performed based on data acquired from meteorological stations, global precipitation datasets such as the project of Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) and from Global Precipitation Climatology Centre (GPCC), and reanalysis archives, including from National Centers of Environmental Prediction (NCEP-DOE) and the European Center for Medium Range Weather Forecasts (ERA5) for the period 1979–2018. The best agreement of the values from the observational data was observed with the values from GPCC. This archive also represented the periodicities in the time series of observational data from meteorological stations, especially in the short-period part of the spectrum. Underestimated values were revealed for the APHRODITE archive, while overestimated ones were found for the NCEP reanalysis data. In comparison with GPCC, the ERA5 dataset reproduced the general variability but with a smaller amplitude (the correlation coefficient was up to 0.9). In general, the median estimates of the precipitation amount derived from the meteorological stations’ data, as well from the reanalysis data, were in better agreement with each other rather than their extreme values. However, their temporal variability can be effectively described by other datasets.

Global Ocean Wind‐Wave Model Hindcasts Forced by Different Reanalyzes: A Comparative Assessment

AbstractFour global wind wave hindcasts based on the third‐generation spectral wave model WAVEWATCH III forced by ERA5, ERA‐Interim, National Centers of Environmental Prediction and Climate Forecast System Reanalysis (CFSR) and MERRA2 reanalyzes for 1980–2019 are presented. Absolute magnitudes of both 10‐m wind speeds and significant wave heights are the largest in the CFSR and CFSR‐based hindcast respectively, particularly in the extratropical regions. The differences in annual extremes between CFSR and ERA5 (and corresponding hindcasts) amount to 2.4 ms−1 for wind speeds and 1.2 m for wave heights. CFSR and CFSR‐based hindcast also stand out with respect to remaining datasets, showing mostly negative trends in both mean and extreme characteristics. ERA5‐, ERA‐Interim‐, and MERRA2‐based hindcasts show annual positive trends in wave heights in 1980–2019, in particular in the Southern Ocean (up to 0.15 m decade‐1 for extreme wave heights in MERRA2‐based hindcast) as well as in the central Pacific, equatorial and tropical Atlantic, and Indian Oceans. All four hindcasts agree on the positive linear trends in the North Atlantic midlatitudes and subtropics, western South Atlantic midlatitudes and the tropical South Pacific, and the negative trends in the eastern North Pacific midlatitudes. Areas with significant linear trends in wave heights generally coincide with those for wind speeds, however, they have larger spatial coverage. The interannual variability in significant wave heights is the most consistent across different hindcasts in the eastern midlatitudes of both hemispheres. The performed hindcasts provide a useful background for studies on both present and future wave climates.

The Lagrangian particle dispersion model FLEXPART version 10.4

Abstract. The Lagrangian particle dispersion model FLEXPART in its original version in the mid-1990s was designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as those released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modeling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g., greenhouse gases, short-lived climate forcers like black carbon and volcanic ash, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to global. In particular, inverse modeling based on source–receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.4, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physicochemical parameterizations, input/output formats, and available preprocessing and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes in runs with very large numbers of particles. The deviation from 100 % efficiency is almost entirely due to the remaining nonparallelized parts of the code, suggesting large potential for further speedup. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g., to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option of running backward in time from atmospheric concentrations at receptor locations for many years, but this has now been extended to also work for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, the temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing for more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing FLEXPART output data, and we briefly report on alternative FLEXPART versions.

A New Single-Band Pixel-by-Pixel Atmospheric Correction Method to Improve the Accuracy in Remote Sensing Estimates of LST. Application to Landsat 7-ETM+

Monitoring Land Surface Temperature (LST) from satellite remote sensing requires an accurate correction of the atmospheric effects. Although thermal remote sensing techniques have advanced significantly over the past few decades, to date, single-band pixel-by-pixel atmospheric correction of full thermal images is unsolved. In this work, we introduce a new Single-Band Atmospheric Correction (SBAC) tool that provides pixel-by-pixel atmospheric correction parameters regardless of the pixel size. The SBAC tool uses National Centers of Environmental Prediction (NCEP) profiles as inputs and, as a novelty, it also accounts for pixel elevation through a Digital Elevation Model (DEM). Application of SBAC to 19 Landsat 7-ETM+ scenes shows the potential of the proposed pixel-by-pixel atmospheric correction to capture terrain orography or atmospheric variability within the scene. LST estimation yields negligible bias and an RMSE of ±1.6 K for the full dataset. The Landsat Atmospheric Correction Tool (ACT) is also considered for comparison. SBAC-ACT LST deviations are analyzed in terms of distance to the image center, surface elevation, and spatial distribution of the atmospheric water content. Differences within 3 K are observed. These results give us the first insight of the potential of SBAC for the operational pixel-by-pixel atmospheric correction of full thermal images. The SBAC tool is expected to help users of satellite single-channel thermal sensors to improve their LST estimates due to its simplicity and robustness.

中南半岛前期异常气候条件对中国南方稻区褐飞虱灾变性迁入的影响及其预测模型

PDF HTML阅读 XML下载 导出引用 引用提醒 中南半岛前期异常气候条件对中国南方稻区褐飞虱灾变性迁入的影响及其预测模型 DOI: 10.5846/stxb201706231139 作者: 作者单位: 南京信息工程大学气象灾害预报和评估协同创新中心;江苏省农业气象重点实验室,南京信息工程大学气象灾害预报和评估协同创新中心;江苏省农业气象重点实验室,南京信息工程大学气象灾害预报和评估协同创新中心;江苏省农业气象重点实验室,农业部全国农业技术推广与服务中心,南京信息工程大学气象灾害预报和评估协同创新中心;江苏省农业气象重点实验室,农业部全国农业技术推广服务中心 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金面上项目（41475106）；江苏省高校自然科学研究项目（14KJA170003） Identifying and predicting impacts of abnormal climate conditions of the Indochina Peninsula on catastrophic immigration of Nilaparvata lugens (Stål) in South China Author: Affiliation: Jiangsu Key Laboratory of Agricultural Meteorology,Nanjing University of Information Science Technology,,,,, Fund Project: The National Natural Science Foundation of China (General Program,Key Program,Major Research Plan)(41475106), Project Supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(14KJA170003) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:近30多年来，气候变化对中国褐飞虱的灾变性迁入带来了明显的影响，为了进一步了解虫源地的异常气候变化对我国褐飞虱迁入量的影响，利用1980-2016年中国各植保站提供的褐飞虱虫情资料及同期美国国家环境预测中心（NCEP）提供的全球气象再分析资料，分析了中国褐飞虱境外主要虫源地中南半岛前期异常气候条件对中国南方稻区褐飞虱发生程度的影响，并对褐飞虱发生等级与影响其迁飞的气象因子进行了相关性分析，筛选出关键预报因子，分别应用支持向量机（SVM）、BP神经网络和多元回归分析3种方法对代表站点褐飞虱年发生等级进行了预测，并比较了3种预测模型的优劣。结果表明：（1）中南半岛气候异常区主要分布在北部，异常气候的发生次数在中南半岛呈现出北高南低的特征，并从北向南呈环状递减。（2）中南半岛前期温度偏高（暖冬、暖春）、相对湿度偏大（湿冬、湿春），易引起褐飞虱在中国南方稻区的偏重以上发生；若中南半岛前期气候偏冷（冷冬、冷春）、偏干（干冬、干春），则常导致褐飞虱在中国南方稻区的偏轻以下发生。（3）通过比较3种模型的历史回代率和预测准确率，发现3种模型对褐飞虱的发生程度均有一定的预测能力，其中SVM模型的预测效果最好，BP神经网络次之，多元线性回归模型最差，表明SVM更加适用于生产实际中的褐飞虱发生程度预测。 Abstract:Over the past 30 years, climate change has caused obvious impacts on the catastrophic immigration of the brown plant hopper (BPH), Nilaparvata lugens (Stål). To further understand the impact of abnormal climate change on BPH immigration, this study collected data on the BPH using light traps at 35 plant protection stations in China from 1980 to 2016, and collected reanalyzed meteorological data from the National Center of Environmental Prediction (NCEP) from 1979 to 2016 to identify correlations between the occurrence grades of BPH and the meteorological factors affecting them; the key predicting factors were screened. The support vector machine (SVM) model, back propagation (BP) neural network, and regression analysis were used to establish medium long-term prediction models of the annual occurrence grades of BPH at the representative stations in south China, and their advantages and disadvantages were compared. The results were as follows:(1) Most of the abnormal climate occurrence areas in the Indochina Peninsula were distributed in the north. The occurrence frequency of abnormal climate in the north was higher than the frequency of the south, with the characteristics of the frequencies progressively descending from north to south in an annular pattern. (2) If the ground temperature of the Indochina Peninsula was higher than the average temperature and the relative humidity was greater than the average relative humidity during the 37 years, it brought about partially heavy or heavy occurrences of BPH immigration in south China. However, if the ground temperature of the Indochina Peninsula was lower than the average temperature and the relative humidity was less than the average relative humidity during the 37 years, it brought about partially light or light occurrences of BPH immigration in south China. (3) By comparing the correct rates of back substitution and prediction accuracy of all three models, we found that all three models had certain capabilities of predicting the occurrence grades of BPH in south China. The predicting capability of the SVM model was the best, the BP neural network was the second best, and the multiple linear regression model was the worst, indicating that the SVM model was more suitable for predicting the occurrence of BPH in rice production. 参考文献 相似文献 引证文献

Seasonal prediction of South Asian monsoon in CFSv2

The predictions of the seasonal mean monsoon over South Asia by a coupled model are assessed by analyzing the retrospective forecasts by the Climate Forecast System version 2 of the National Centers of Environmental Prediction for the period 1982–2009. The predictability is assessed by examining the June–September seasonal mean of the outgoing longwave radiation in forecasts from May initial conditions. Although the forecasts exhibit considerable interannual variability, the correlation with the observation is low. While the model is able to predict weak (strong) seasonal monsoon during certain El Nino (La Nina) years, with differences in magnitude, the predictions in the years of Indian Ocean Dipole (IOD) events are not so successful. The relative role of the Pacific and Indian oceans in determining the seasonal mean monsoon is analyzed by extracting the El Nino-Southern Oscillation (ENSO) mode and IOD mode from the forecasts and observation. The model’s ENSO mode captures the observed spatial structure whereas its IOD mode shows incorrect anomalies over India. The model is inadequate in predicting the relative strength and signs of the ENSO and IOD modes. The ocean–atmosphere interaction of the ENSO mode is better represented in the model by capturing the observed relation with the sea surface temperature (SST) and the low-level winds. In the model’s IOD mode, the relation with the SST and the low-level winds is not correct over part of the Indian region while the anomalies over the Pacific Ocean also seem to influence, unlike in the observation.

Predictability of CFSv2 in the tropical Indo-Pacific region, at daily and subseasonal time scales

The predictability of a coupled climate model is evaluated at daily and intraseasonal time scales in the tropical Indo-Pacific region during boreal summer and winter. This study has assessed the daily retrospective forecasts of the Climate Forecast System version 2 from the National Centers of Environmental Prediction for the period 1982–2010. The growth of errors in the forecasts of daily precipitation, monsoon intraseasonal oscillation (MISO) and the Madden–Julian oscillation (MJO) is studied. The seasonal cycle of the daily climatology of precipitation is reasonably well predicted except for the underestimation during the peak of summer. The anomalies follow the typical pattern of error growth in nonlinear systems and show no difference between summer and winter. The initial errors in all the cases are found to be in the nonlinear phase of the error growth. The doubling time of small errors is estimated by applying Lorenz error formula. For summer and winter, the doubling time of the forecast errors is in the range of 4–7 and 5–14 days while the doubling time of the predictability errors is 6–8 and 8–14 days, respectively. The doubling time in MISO during the summer and MJO during the winter is in the range of 12–14 days, indicating higher predictability and providing optimism for long-range prediction. There is no significant difference in the growth of forecasts errors originating from different phases of MISO and MJO, although the prediction of the active phase seems to be slightly better.

Spatial and Temporal Variation of the Extreme Saharan Dust Event over Turkey in March 2016

In this study, the influence of an extraordinary Saharan dust episode over Turkey on 23–24 March 2016 and the atmospheric conditions that triggered this event were evaluated in detail. PM10 (particulate matter less than 10 μm) observations from 97 air quality stations, METAR (Meteorological Terminal Aviation Routine Weather Report) observations at 64 airports, atmospheric soundings, and satellite products were used for the analysis. To determine the surface and upper levels of atmospheric circulation, National Centers of Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis data were applied to the extreme dust episodes. On 23 March 2016, high southwesterly winds due to the interaction between surface low- and high-pressure centers over Italy and Levant basin brought thick dust particles from Libya to Turkey. The daily PM10 data from 43 stations exceeded their long-term spring means over Turkey (especially at the northern and western stations). As a consequence of the longitudinal movement of the surface low from Italy to the Balkan Peninsula, and the quasi-stationary conditions of the surface high-pressure center allowed for the penetration of strong south and southwesterly winds to inner parts of the country on the following day. As a consequence, 100%, 90%, 88%, and 87% of the monitoring stations in Marmara (NW Turkey), central Anatolia, western (Aegean) and northern (Black Sea) regions of Turkey, respectively, exhibited above-normal daily PM10 values. In addition, while strong subsidence at the low levels of the atmosphere plays a significant role in having excessive daily PM10 values in Black Sea, dry atmospheric conditions and thick inversion level near the ground surface of Marmara ensured this region to have peak PM10 values ~00 Local Time (LT).

Tropical intraseasonal oscillations in CFSv2 during Boreal summer and winter

ABSTRACTThe simulation of the tropical intraseasonal oscillations (ISOs) by a coupled model in the Indo‐Pacific region is examined in this study. The retrospective forecasts by the Climate Forecast System version 2 (CFSv2) of National Centers of Environmental Prediction for the period 1982–2010 have been analysed and compared with observation. The leading Indian monsoon intraseasonal oscillation (MISO) during the boreal summer and the Madden‐Julian Oscillation (MJO) during the boreal winter are extracted from daily precipitation anomalies in forecasts and observations by using a data‐adaptive method. The model forecasts from May initial conditions simulates the observed spatial structure, magnitude and the northward and eastward propagation of MISO reasonably well. Similar performance is also evident in the simulation of the eastward propagating MJO in forecasts from November initial conditions. In the simulation of both the MISO and MJO by the model, the zonal wind at lower level shows appropriate observed relation with the precipitation and coupling with convection. The observed phase relations between precipitation and low‐level relative vorticity and between precipitation and upper‐level divergence in the zonal and meridional propagations are captured by the model. However, the variance of the ISOs differs from observation in certain regions, and the phase of the ISOs exhibits wide differences among the ensemble members of the forecast and with the observation. Comparison with the earlier model, CFS version 1, shows that the space‐time structure and evolution of MISO and MJO are better simulated by CFSv2.

Short Review of Climate and Land Use change Impact on Land Degradation in Tashkent Province.

The paper is a short review about scientific articles, local reports and expert’s opinion on land degradation caused by climate change (in particular temperature and precipitation change) and land use change in Central Asia (CA) with a special interest in Tashkent Province and aspects of regional water management in CA in order to clarify future study activities. Furthermore the paper presents the preliminary analysis of climate and land use changes in Tashkent Province and the author’s opinion about the impact of these changes to potential land degradation. Climate change analysis based on the 0.5° resolution National Centers of Environmental Prediction (NCEP) Global Forecast System Reanalysis (CFSR) weather dataset of the 35 years period from 1979 to 2013 demonstrated the trend of increasing temperature and variable trend of precipitation, but small overall precipitation decrease in the croplands, pasturelands and grasslands of study area. Irrigated agricultural cropland increased to 7.800 ha from 1999 to 2010 in order meet population demand and increase national economy. Therefore, it is very importance to carry out comprehensive study of climate and land use change impacts on land degradation at regional scale using high resolution spatio-temporal remotely sensed data for better assessment of land degradation.

The influence of atmospheric circulation types on regional patterns of precipitation in Marmara (NW Turkey)

In this study, regional patterns of precipitation in Marmara are described for the first time by means of Ward’s hierarchical cluster analysis. Daily values of winter precipitation data based on 19 meteorological stations were used for the period from 1960 to 2012. Five clusters of coherent zones were determined, namely Black Sea-Marmara, Black Sea, Marmara, Thrace, and Aegean sub-regions. To investigate the prevailing atmospheric circulation types (CTs) that cause precipitation occurrence and intensity in these five different rainfall sub-basins, objective Lamb weather type (LWT) methodology was applied to National Centers of Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis of daily mean sea level pressure (MSLP) data. Precipitation occurrence suggested that wet CTs (i.e. N, NE, NW, and C) offer a high chance of precipitation in all sub-regions. For the eastern (western) part of the region, the high probability of rainfall occurrence is shown under the influence of E (SE, S, SW) atmospheric CTs. In terms of precipitation intensity, N and C CTs had the highest positive gradients in all the sub-basins of the Marmara. In addition, although Marmara and Black Sea sub-regions have the highest daily rainfall potential during NE types, high daily rainfall totals are recorded in all sub-regions except the Black Sea during NW types.

Influence of high-resolution surface databases on the modeling of local atmospheric circulation systems

Abstract. Large-eddy simulations are performed using the Advanced Regional Prediction System (ARPS) code at horizontal grid resolutions as fine as 300 m to assess the influence of detailed and updated surface databases on the modeling of local atmospheric circulation systems of urban areas with complex terrain. Applications to air pollution and wind energy are sought. These databases are comprised of 3 arc-sec topographic data from the Shuttle Radar Topography Mission, 10 arc-sec vegetation-type data from the European Space Agency (ESA) GlobCover project, and 30 arc-sec leaf area index and fraction of absorbed photosynthetically active radiation data from the ESA GlobCarbon project. Simulations are carried out for the metropolitan area of Rio de Janeiro using six one-way nested-grid domains that allow the choice of distinct parametric models and vertical resolutions associated to each grid. ARPS is initialized using the Global Forecasting System with 0.5°-resolution data from the National Center of Environmental Prediction, which is also used every 3 h as lateral boundary condition. Topographic shading is turned on and two soil layers are used to compute the soil temperature and moisture budgets in all runs. Results for two simulated runs covering three periods of time are compared to surface and upper-air observational data to explore the dependence of the simulations on initial and boundary conditions, grid resolution, topographic and land-use databases. Our comparisons show overall good agreement between simulated and observational data, mainly for the potential temperature and the wind speed fields, and clearly indicate that the use of high-resolution databases improves significantly our ability to predict the local atmospheric circulation.

Evaluation of real-time hydrometeorological ensemble prediction on hydrologic scales in Northern California

The paper presents an evaluation of real time ensemble forecasts produced during 2010–2012 by the demonstration project INFORM (Integrated Forecast and Reservoir Management) in Northern California. In addition, the innovative elements of the forecast component of the INFORM project are highlighted. The forecast component is designed to dynamically downscale operational multi-lead ensemble forecasts from the Global Ensemble Forecast System (GEFS) and the Climate Forecast system (CFS) of the National Centers of Environmental Prediction (NCEP), and to use adaptations of the operational hydrologic models of the US National Weather Service California Nevada River Forecast Center to provide ensemble reservoir inflow forecasts in real time. A full-physics 10-km resolution (10 km on the side) mesoscale model was implemented for the ensemble prediction of surface precipitation and temperature over the domain of Northern California with lead times out to 16 days with 6-hourly temporal resolution. An intermediate complexity regional model with a 10 km resolution was implemented to downscale the NCEP CFS ensemble forecasts for lead times out to 41.5 days. Methodologies for precipitation and temperature model forecast adjustment to comply with the corresponding observations were formulated and tested as regards their effectiveness for improving the ensemble predictions of these two variables and also for improving reservoir inflow forecasts. The evaluation is done using the real time databases of INFORM and concerns the snow accumulation and melt seasons. Performance is measured by metrics that range from those that use forecast means to those that use the entire forecast ensemble. The results show very good skill in forecasting precipitation and temperature over the subcatchments of the INFORM domain out to a week in advance for all basins, models and seasons. For temperature, in some cases, non-negligible skill has been obtained out to four weeks for the melt season. Reservoir inflow forecasts exhibit also good skill for the shorter lead-times out to a week or so, and provide a good quantitative basis in support of reservoir management decisions pertaining to objectives with a short term horizon (e.g., flood control and energy production). For the northernmost basin of Trinity reservoir inflow forecasts exhibit good skill for lead times longer than 3 weeks in the snow melt season. Bias correction of the ensemble precipitation and temperature forecasts with fixed bias factors over the range of lead times improves forecast performance for almost all leads for precipitation and temperature and for the shorter lead times for reservoir inflow. The results constitute a first look at the performance of operational coupled hydrometeorological ensemble forecasts in support of reservoir management.

Calculation of the far range atmospheric transport of radionuclides after the Fukushima accident with the atmospheric dispersion model MATCH of the JRODOS system

The paper presents estimates of the far-range atmospheric dispersion of radionuclides after the accident at Fukushima Daiichi Nuclear Power Plant (NPP), obtained using the long-range atmospheric dispersion model MATCH. Software tools were developed to run MATCH in the EU nuclear emergency response system JRODOS using freely available numerical weather prediction (NWP) data of the Global Forecasting System (GFS) operated by the United States National Center of Environmental Prediction (NCEP). Comparisons are made of results with JRODOS/MATCH and a standalone MATCH operated by Swedish Meteorological and Hydrological Institute (SMHI) driven by the European Center for Medium-Range Weather Forecasts (ECMWF) NWP data.

Verification of Forecast Rainfall Anomalies

Statistical downscaling is used to relate the large scale climate information with the local variables that is to find the relationship between the National Center of Environmental Prediction (NCEP) data with the ground data. This study examines the verification of forecast rainfall anomalies during November-December-January-February (NDJF). The ground data used is the 30 years NDJF rainfall for 40 stations while the NCEP data is the 20 grids point Sea Level Pressure (SLP). In this paper, Canonical correlation analysis (CCA) is used to find the maximum correlated pattern between two variables. CCA model is verified using the mean square error skill score and anomaly correlation coefficient and used to simulate the current rainfall using the General Circulation Model (GCM) data as predictors. This is so called the validation method. Due to appearance of some biases, the anomaly correlation coefficient is considerably higher than the skill score. These biases may relate to the penalty associated with retaining the Sea Level Pressure (SLP) in the meteorological features when such features are not predictable. Keywords: Canonical Correlation Analysis (CCA); Mean Square Error Skill Score. 2010 Mathematics Subject Classification: 62H06, 62H07

Atmospheric teleconnection patterns associated with severe and mild ice cover on the Great Lakes, 1963–2011

Atmospheric teleconnection circulation patterns associated with severe and mild ice cover over the Great Lakes are investigated using the composite analysis of lake ice data and National Center of Environmental Prediction (NCEP) reanalysis data for the period 1963–2011. The teleconnection pattern associated with the severe ice cover is the combination of a negative North Atlantic Oscillation (NAO) or Arctic Oscillation (AO) and negative phase of Pacific/North America (PNA) pattern, while the pattern associated with the mild ice cover is the combination of a positive PNA (or an El Niño) and a positive phase of the NAO/AO. These two extreme ice conditions are associated with the North American ridge–trough variations. The intensified ridge–trough system produces a strong northwest-to-southeast tilted ridge and trough and increases the anomalous northwesterly wind, advecting cold, dry Arctic air to the Great Lakes. The weakened ridge–trough system produces a flattened ridge and trough, and promotes a climatological westerly wind, advecting warm, dry air from western North America to the Great Lakes. Although ice cover for all the individual lakes responds roughly linearly and symmetrically to both phases of the NAO/AO, and roughly nonlinearly and asymmetrically to El Niño and La Niña events, the overall ice cover response to individual NAO/AO or Niño3.4 index is not statistically significant. The combined NAO/AO and Niño3.4 indices can be used to reliably project severe ice cover during the simultaneous –NAO/AO and La Niña events, and mild ice cover during the simultaneous +NAO/AO and El Niño events.

Towards space based verification of CO<sub>2</sub> emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation

Abstract. Carbon dioxide (CO2) is the most important man-made greenhouse gas (GHG) that cause global warming. With electricity generation through fossil-fuel power plants now being the economic sector with the largest source of CO2, power plant emissions monitoring has become more important than ever in the fight against global warming. In a previous study done by Bovensmann et al. (2010), random and systematic errors of power plant CO2 emissions have been quantified using a single overpass from a proposed CarbonSat instrument. In this study, we quantify errors of power plant annual emission estimates from a hypothetical CarbonSat and constellations of several CarbonSats while taking into account that power plant CO2 emissions are time-dependent. Our focus is on estimating systematic errors arising from the sparse temporal sampling as well as random errors that are primarily dependent on wind speeds. We used hourly emissions data from the US Environmental Protection Agency (EPA) combined with assimilated and re-analyzed meteorological fields from the National Centers of Environmental Prediction (NCEP). CarbonSat orbits were simulated as a sun-synchronous low-earth orbiting satellite (LEO) with an 828-km orbit height, local time ascending node (LTAN) of 13:30 (01:30 p.m. LT) and achieves global coverage after 5 days. We show, that despite the variability of the power plant emissions and the limited satellite overpasses, one CarbonSat has the potential to verify reported US annual CO2 emissions from large power plants (≥5 Mt CO2 yr−1) with a systematic error of less than ~4.9% and a random error of less than ~6.7% for 50% of all the power plants. For 90% of all the power plants, the systematic error was less than ~12.4% and the random error was less than ~13%. We additionally investigated two different satellite configurations using a combination of 5 CarbonSats. One achieves global coverage everyday but only samples the targets at fixed local times. The other configuration samples the targets five times at two-hour intervals approximately every 6th day but only achieves global coverage after 5 days. From the statistical analyses, we found, as expected, that the random errors improve by approximately a factor of two if 5 satellites are used. On the other hand, more satellites do not result in a large reduction of the systematic error. The systematic error is somewhat smaller for the CarbonSat constellation configuration achieving global coverage everyday. Therefore, we recommend the CarbonSat constellation configuration that achieves daily global coverage.

Trends in column integrated water vapour over Europe from 1973 to 2003

AbstractThe spatial and temporal variations of atmospheric precipitable water (PW) content anomalies were analysed over Europe from 1973 to 2003 using daily data (0000 and 1200 UTC) from National Center of Environmental Prediction and National Center of Atmospheric Research Reanalysis project (NCEP‐1) and in situ radiosonde data. Mann–Kendall trend tests were applied to long‐term PW time series. Technology changes influence PW radiosonde trends, although these are in agreement with NCEP‐1 trends. Over the south of the Iberian Peninsula, trends are negative and statistically significant (<− 0.04 mm year−1; p < 0.05) and positive over the Central European Mountains (The Alpes) and the North Atlantic Ocean (>0.04 mm year−1; p < 0.05). Seasonal trends revealed negative and significant trends over the Iberian Peninsula for all seasons (<− 0.03 mm year−1; p < 0.05). Copyright © 2010 Royal Meteorological Society

Assessing the performance of the CFSR by an ensemble of analyses

The Climate Forecast System Reanalysis (CFSR, Saha et al. in Bull Am Meteor Soc 91:1015–1057, 2010) is the latest global reanalysis from the National Centers of Environmental Prediction (NCEP). In this study, we compare the CFSR tropospheric analyses to two ensembles of analyses. The first ensemble consists of 12 h analyses from various operational analyses for the year 2007. This ensemble shows how well the CFSR analyses can capture the daily variability. The second ensemble consists of monthly means from the available reanalyses from the years 1979 to 2009 which is used to examine the trends. With the 2007 ensemble, we find that the CFSR captures the daily variability in 2007 better than the older reanalyses and is comparable to the operational analyses. With the ensemble of monthly means, the CFSR is often the outlier. The CFSR shows a strong warming trend in the tropics which is not seen in the observations or other reanalyses.

Synoptic-Eddy Feedbacks and Circulation Regime Analysis

Abstract A method to incorporate synoptic eddies into the diagnosis of circulation regimes using cluster analysis is illustrated using boreal winter reanalyses of the National Centers of Environmental Prediction (hereafter observations) over the Pacific–North American region. The motivation is to include the configuration of the high-frequency (periods less than 10 days) transients as well as the low-frequency (periods greater than 10 days) flow explicitly into the definition of the regimes. Principle component analysis is applied to the low-frequency 200-hPa height field, and also to the low-frequency “envelope” modulations of the rms of high-frequency meridional velocity at 200 hPa. A maximum covariance analysis of the height and envelope fields, carried out using the appropriate principal components, defines three modes as explaining most of the covariance. This defines the minimum dimensionality of the space in which to apply k-means cluster analysis to the covariance coefficients. Clusters found using this method agree with results of the previous work. Significance is assessed by comparing cluster analyses with results from synthetic datasets that have the same spectral amplitudes (but random phases) of seasonal means and, separately, intraseasonal fluctuations as do the original observed time series. This procedure ensures that the synthetic series have similar autocovariance structures to the observations. Building on earlier work, the clusters obtained are newly tested to be highly significant without the need for quasi-stationary prefiltering.