Rawinsonde Observations Research Articles

Water vapor motion signal extraction from FY-2E longwave infrared window images for cloud-free regions: The temporal difference technique

The aim of this study is to calculate the low-level atmospheric motion vectors (AMVs) in clear areas with FY-2E IR2 window (11.59–12.79 μm) channel imagery, where the traditional cloud motion wind technique fails. A new tracer selection procedure, which we call the temporal difference technique, is demonstrated in this paper. This technique makes it possible to infer low-level wind by tracking features in the moisture pattern that appear as brightness temperature (T B) differences between consecutive sequences of 30-min-interval FY-2E IR2 images over cloud-free regions. The T B difference corresponding to a 10% change in water vapor density is computed with the Moderate Resolution Atmospheric Transmission (MODTRAN4) radiative transfer model. The total contribution from each of the 10 layers is analyzed under four typical atmospheric conditions: tropical, midlatitude summer, U.S. standard, and midlatitude winter. The peak level of the water vapor weighting function for the four typical atmospheres is assigned as a specific height to the T B “wind”. This technique is valid over cloud-free ocean areas. The proposed algorithm exhibits encouraging statistical results in terms of vector difference (VD), speed bias (BIAS), mean vector difference (MVD), standard deviation (SD), and root-mean-square error (RMSE), when compared with the wind field of NCEP reanalysis data and rawinsonde observations.

A new method for quality control of Chinese rawinsonde wind observations

In 2006, the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA) developed its real-time quality control (QC) system of rawinsonde observations coming from the Global Telecommunications System (GTS) and established the Global Upper-air Report Dataset, which, with the NMIC B01 format, is generally referred to as the B01 dataset and updated on a daily basis. However, when the B01 dataset is applied in climate analysis, some wind errors as well as some accurate values with incorrect error marks are found. To improve the quality and usefulness of Chinese rawinsonde wind observations, a new QC method (NewQC) is proposed in this paper. Different from the QC approach used for B01 datasets, the NewQC includes two vertical-wind-shear checks to analyze the vertical consistency of winds, in which the constant height level winds are used as reference data for the QC of mandatory pressure level winds. Different threshold values are adopted in the wind shear checks for different stations and different vertical levels. Several typical examples of QC of different error types by the new algorithm are shown and its performance with respect to 1980-2008 observational data is statistically evaluated. Compared with the radiosonde QC algorithms used in both the Meteorological Assimilation Data Ingest System (MADIS, http://madis.noaa.gov/madis_raob_qc.html) of the National Oceanic and Atmospheric Administration (NOAA) and the B01 dataset, the NewQC shows higher accuracy and better reliability, particularly when used to judge successive observation errors.

Low-Level Jets in the North American Regional Reanalysis (NARR): A Comparison with Rawinsonde Observations

AbstractClimatological analyses of low-level jets (LLJs) can be negatively influenced by the coarse spatial and temporal resolution and frequent changes in observing and archiving protocols of rawinsonde observations (raobs). The introduction of reanalysis datasets, such as the North American Regional Reanalysis (NARR), provides new resources for climatological research with finer spatial and temporal resolution and potentially fewer inhomogeneities. To assess the compatibility of LLJ characteristics identified from NARR wind profiles with those obtained from raob profiles, LLJs were extracted using standard jet definitions from NARR and raobs at 12 locations in the central United States for four representative years that reflect different rawinsonde protocols. LLJ characteristics (e.g., between-station differences in relative frequency, diurnal fluctuations, and mean speed and elevation) are generally consistent, although absolute frequencies are smaller for NARR relative to raobs at most stations. LLJs are concurrently identified in the NARR and raob wind profiles on less than 60% of the observation times with LLJ activity. Variations are seen between analysis years and locations. Of particular note is the substantial increase in LLJ frequency seen in raobs since the introduction of the Radiosonde Replacement System, which has led to a greater discrepancy in jet frequency between the NARR and raob datasets. The analyses suggest that NARR is a viable additional resource for climatological analyses of LLJs. Many of the findings are likely applicable for other fine-resolution reanalysis datasets, although differences between reanalyses require that each be carefully evaluated before its use in climatological analyses of wind maxima.

2013년 여름철 집중관측동안 통합모델 관측시스템실험을 이용한 이동형 레윈존데 관측의 자료동화 효과

2013년 여름철 집중관측동안 통합모델 관측시스템실험을 이용한 이동형 레윈존데 관측의 자료동화 효과

The Impact of Increased Frequency of Rawinsonde Observations on Forecast Skill Investigated with an Observing System Simulation Experiment

Abstract Most rawinsondes are launched once or twice daily, at 0000 and/or 1200 UTC; only a small number of the total rawinsonde observations are taken at 0600 and 1800 UTC (“off hour” cycle times). In this study, the variations of forecast and analysis quality between cycle times and the potential improvement of skill due to supplemental rawinsonde measurements at 0600 and 1800 UTC are tested in the framework of an observing system simulation experiment (OSSE). The National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA GMAO) Goddard Earth Observing System Model, version 5 (GEOS-5), is used with the GMAO OSSE setup for an experiment emulating the months of July and August with the 2011 observational network. The OSSE is run with and without supplemental rawinsonde observations at 0600 and 1800 UTC, and the differences in analysis error and forecast skill are quantified. The addition of supplemental rawinsonde observations results in significant improvement of analysis quality in the Northern Hemisphere for both the 0000/1200 and 0600/1800 UTC cycle times, with greater improvement for the off-hour times. Reduction of root-mean-square errors on the order of 1%–3% for wind and temperature is found at the 24- and 48-h forecast times. There is a slight improvement in Northern Hemisphere anomaly correlations at the 120-h forecast time.

Effective assimilation of global precipitation: simulation experiments

Past attempts to assimilate precipitation by nudging or variational methods have succeeded in forcing the model precipitation to be close to the observed values. However, the model forecasts tend to lose their additional skill after a few forecast hours. In this study, a local ensemble transform Kalman filter (LETKF) is used to effectively assimilate precipitation by allowing ensemble members with better precipitation to receive higher weights in the analysis. In addition, two other changes in the precipitation assimilation process are found to alleviate the problems related to the non-Gaussianity of the precipitation variable: (a) transform the precipitation variable into a Gaussian distribution based on its climatological distribution (an approach that could also be used in the assimilation of other non-Gaussian observations) and (b) only assimilate precipitation at the location where at least some ensemble members have precipitation. Unlike many current approaches, both positive and zero rain observations are assimilated effectively.Observing system simulation experiments (OSSEs) are conducted using the Simplified Parametrisations, primitivE-Equation DYnamics (SPEEDY) model, a simplified but realistic general circulation model. When uniformly and globally distributed observations of precipitation are assimilated in addition to rawinsonde observations, both the analyses and the medium-range forecasts of all model variables, including precipitation, are significantly improved as compared to only assimilating rawinsonde observations. The effect of precipitation assimilation on the analyses is retained on the medium-range forecasts and is larger in the Southern Hemisphere (SH) than that in the Northern Hemisphere (NH) because the NH analyses are already made more accurate by the denser rawinsonde stations. These improvements are much reduced when only the moisture field is modified by the precipitation observations. Both the Gaussian transformation and the new observation selection criterion are shown to be beneficial to the precipitation assimilation especially in the case of larger observation errors. Assigning smaller horizontal localisation length scales for precipitation observations further improves the LETKF analysis.

The Impact of Dropwindsonde and Supplemental Rawinsonde Observations on Track Forecasts for Hurricane Irene (2011)

Abstract Because of the threat that Hurricane Irene (2011) posed to the United States, supplemental observations were collected for assimilation into operational numerical models in the hope of improving forecasts of the storm. Synoptic surveillance aircraft equipped with dropwindsondes were deployed twice daily over a 5-day period, and supplemental rawinsondes were launched from all upper-air sites in the continental United States east of the Rocky Mountains at 0600 and 1800 UTC, marking an unprecedented magnitude of coverage of special rawinsondes at the time. The impact of assimilating the supplemental observations on National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model track forecasts of Irene was evaluated over the period that these observations were collected. The GFS track forecasts possessed small errors even in the absence of the supplemental observations, providing little room for improvement on average. The assimilation of the combined dropwindsonde and supplemental rawinsonde data provided small but statistically significant improvements in the 42–60-h range for GFS forecasts initialized at 0600 and 1800 UTC. The primary improvement from the dropwindsonde data was also within this time range, with an average improvement of 20% for 48-h forecasts. The rawinsonde data mostly improved the forecasts beyond 3 days by modest amounts. Both sets of observations provided small, additive improvements to the average cross-track errors. Investigations of individual forecasts identified corrections to the model analyses of the Atlantic subtropical ridge and an upstream midlatitude short-wave trough over the contiguous United States due to the assimilation of the extra data.

The Landfall and Inland Penetration of a Flood-Producing Atmospheric River in Arizona. Part I: Observed Synoptic-Scale, Orographic, and Hydrometeorological Characteristics

AbstractAtmospheric rivers (ARs) are a dominant mechanism for generating intense wintertime precipitation along the U.S. West Coast. While studies over the past 10 years have explored the impact of ARs in, and west of, California’s Sierra Nevada and the Pacific Northwest’s Cascade Mountains, their influence on the weather across the intermountain west remains an open question. This study utilizes gridded atmospheric datasets, satellite imagery, rawinsonde soundings, a 449-MHz wind profiler and global positioning system (GPS) receiver, and operational hydrometeorological observing networks to explore the dynamics and inland impacts of a landfalling, flood-producing AR across Arizona in January 2010. Plan-view, cross-section, and back-trajectory analyses quantify the synoptic and mesoscale forcing that led to widespread precipitation across the state. The analyses show that a strong AR formed in the lower midlatitudes over the northeastern Pacific Ocean via frontogenetic processes and sea surface latent-heat fluxes but without tapping into the adjacent tropical water vapor reservoir to the south. The wind profiler, GPS, and rawinsonde observations document strong orographic forcing in a moist neutral environment within the AR that led to extreme, orographically enhanced precipitation. The AR was oriented nearly orthogonal to the Mogollon Rim, a major escarpment crossing much of central Arizona, and was positioned between the high mountain ranges of northern Mexico. High melting levels during the heaviest precipitation contributed to region-wide flooding, while the high-altitude snowpack increased substantially. The characteristics of the AR that impacted Arizona in January 2010, and the resulting heavy orographic precipitation, are comparable to those of landfalling ARs and their impacts along the west coasts of midlatitude continents.

Low-Level Stability during Winter Storms in the Uinta Basin of Utah: Potential Impacts on Ground-Based Cloud Seeding

Low-level thermodynamic stability, an important consideration for winter season ground-based cloud seeding site selection and conduct of operations, varies considerably in and near mountainous terrain. Rawinsonde observations may sometimes be poorly representative of nearby areas, with significant lower-level atmospheric temperature variability common between basins and sometimes within a basin. Operational forecast models may lack the ability to resolve or properly initialize low-level stability or “cold pooling” specific to a given basin or portion of a basin. Analyses of surface observations, as well as plume dispersion modeling with HYSPLIT, can be used both for real-time operations and in post-hoc examinations of low-level stability specific to storm periods with cloud seeding potential. Analyses of this type have been performed in various portions of Utah, and a recent analysis in the Uinta Range and Uinta Basin area of northeastern Utah highlights both the geographic variability and the seasonality of basin cold pooling. The Uinta Basin analyses have shown much greater low-level thermodynamic stability during relevant winter storm periods than other portions of Utah. Strong correlation of stability to site elevation within the basin, as well as distinct seasonality, are observed.

Near‐surface marine wind profiles from rawinsonde and NORA10 hindcast

With huge investments going into offshore wind farming and strong focus on offshore safety at all levels, there is an increasing demand for high‐resolution wind products in the near‐surface boundary layer. The Norwegian Reanalysis Archive (NORA10) is a dynamical downscaling of ERA‐40 to a spatial resolution of 10–11 km over the northeastern North Atlantic using the High‐Resolution Limited Area Model (HIRLAM). The boundary layer wind speed between 10 and 150 m above the sea surface from NORA10 is used in a large number of applications. In this study, wind speed maps are produced, and the seasonal and decadal variability in wind speed is discussed. The model underestimates the mean wind speed from in situ winds from offshore platforms and 0.5 Hz rawinsonde observations over the sea by 5–10%. One exception is FINO‐1, where there is excellent agreement. Part of the discrepancies may be due to the speed‐up effects over large platform structures. The high sampling rate of the rawinsondes gives good quality recordings of wind speed and temperature in approximately 10 m height intervals for a 10 year period. Mean model wind profile shapes below 150 m above sea level favorable compare with mean wind speed profiles for stable, unstable and neutral conditions from rawinsonde at Polarfront (ocean weather ship in the geographical position 66°N, 2°E). However, in 18% of the cases the wind speed is decreasing with height, which is not reproduced by the model. We suggest that these inverse wind profiles may be related to cold air advection and convection cells, e.g., downstream of cold air outbreaks.

New Methods toward Minimizing the Slow Speed Bias Associated with Atmospheric Motion Vectors

AbstractComparisons between satellite-derived winds and collocated rawinsonde observations often show a pronounced slow speed bias at mid- and upper levels of the atmosphere. A leading cause of the slow speed bias is the improper assignment of the tracer to a height that is too high in the atmosphere. Height errors alone cannot fully explain the slow bias, however. Another factor influencing the speed bias is the size of the target window used in the tracking step. Tracking with a large target window can cause excessive averaging to occur and a smoothing of the instantaneous wind field. Conversely, if too small a window is specified, there is an increased risk of finding a false match. The authors have developed a new “nested tracking” approach that isolates the dominant local motion within a cloud scene and minimizes the smoothing of the motion estimate. A major advantage of the new approach is the ability to identify which pixels within the cloud scene are contributing to the tracking solution. Knowing which pixels contribute to the dominant motion allows for a more representative height to be derived, thereby directly linking the height assignment to the tracking process, which is an important goal for producers of global atmospheric motion vector (AMV) data. When compared with equivalent rawinsondes, the AMVs derived with the new approach show a considerable improvement in the speed bias and root-mean-square error over a control set of AMVs derived with more-conventional methods.

A hybrid nudging‐ensemble Kalman filter approach to data assimilation in WRF/DART

AbstractA hybrid nudging‐ensemble Kalman filter (HNEnKF), previously tested in the Lorenz three‐variable model and a two‐dimensional shallow‐water model, is now explored in WRF/DART with real observations using a Cross Appalachian Tracer Experiment case from September 1983 (CAPTEX‐83). The HNEnKF, effectively combining observation nudging and the ensemble Kalman filter (EnKF), achieves a more gradual data assimilation and greatly reduces the insertion noise compared to the EnKF.Three‐hourly surface observations and 12‐hourly rawinsonde observations from the World Meteorological Organization (WMO) are assimilated. These assimilated meteorological observations are used to verify the priors of the experiments. It is found that the HNEnKF generally obtains better priors than the EnKF. To independently verify the HNEnKF approach, the hourly WRF experiment results are also used to drive the Second‐Order Closure Integrated Puff (SCIPUFF) model to predict surface tracer concentrations that are verified against the observed surface concentration data. The HNEnKF analyses driving SCIPUFF produce better statistics of the independent tracer data than the EnKF. Thus there appear to be some advantages in the hourly dynamic analyses produced by the continuous HNEnKF compared to the intermittent EnKF. The analyses of surface pressure tendency demonstrate that the HNEnKF is able to provide better temporal smoothness and dynamic consistency in the hourly analyses than the EnKF. Copyright © 2012 Royal Meteorological Society

Global cloud-layer distribution statistics from 1 year CALIPSO lidar observations

In this paper, the cloud statistics and global cloud distributions are derived from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) level 2, 5 km lidar cloud-layer products between 13 June 2006 and 24 June 2007. The global cloud-layer occurrence frequency, the horizontal and the vertical distributions of cloud-occurrence frequency, the global cloud-top height statistics and their seasonal changes are given for 1 year CALIPSO observations. Opaque cloud-layer statistics are analysed for better understanding the statistics of the clouds observed using CALIPSO. Parts of the results are compared with some results from the High Resolution Infrared Radiation Sounder (HIRS) observations, the Geoscience Laser Altimeter System (GLAS) observations and the global rawinsonde observations, the Aqua MODerate Resolution Imaging Spectroradiometer (MODIS) monthly cloud-fraction data. The comparisons show that results are in agreement with other observations. Due to the primary advance from the active optical sensing, CALIPSO lidar can offer measurements of accurate, highly resolved vertical profiles of atmospheric scattering layers. It has advantages in determining the location of optically thin clouds and detecting occurrences of multiple layers. Long-term observations by CALIPSO lidar can provide valuable information for the better understanding of the climate system and global climate change.

Rewriting the Climatology of the Tropical North Atlantic and Caribbean Sea Atmosphere

Abstract The Jordan mean tropical sounding has provided a benchmark reference for representing the climatology of the tropical North Atlantic and Caribbean Sea atmosphere for over 50 years. However, recent observations and studies have suggested that during the months of the North Atlantic hurricane season, this region of the world is affected by multiple air masses with very distinct thermodynamic and kinematic characteristics. This study examined ∼6000 rawinsonde observations from the Caribbean Sea region taken during the core months (July–October) of the 1995–2002 hurricane seasons. It was found that single mean soundings created from this new dataset were very similar to C. L. Jordan’s 1958 sounding work. However, recently developed multispectral satellite imagery that can track low- to midlevel dry air masses indicated that the 1995–2002 hurricane season dataset (and likely Jordan’s dataset as well) was dominated by three distinct air masses: moist tropical (MT), Saharan air layer (SAL), and midlatitude dry air intrusions (MLDAIs). Findings suggest that each sounding is associated with unique thermodynamic, kinematic, stability, and mean sea level pressure characteristics and that none of these soundings is particularly well represented by a single mean sounding such as Jordan’s. This work presents three new mean tropical soundings (MT, SAL, and MLDAI) for the tropical North Atlantic Ocean and Caribbean Sea region and includes information on their temporal variability, thermodynamics, winds, wind shear, stability, total precipitable water, and mean sea level pressure attributes. It is concluded that the new MT, SAL, and MLDAI soundings presented here provide a more robust depiction of the tropical North Atlantic and Caribbean Sea atmosphere during the Atlantic hurricane season and should replace the Jordan mean tropical sounding as the new benchmark soundings for this part of the world.

Temperature Inversions over the Inland Indochina Revealed by GAME-T Enhanced Rawinsonde Observations

Detailed structures and variability of lower tropospheric temperature inversions are investigated, using two sets of high-resolution (50 m in height and 3 hours in time) rawinsonde data obtained by the GAME-T enhanced rawinsonde observations conducted over the inland Indochina Peninsula in March 1997 and January 2000. In both observation periods, the strong inversions were observed at 3-4 km height. The causes of variations in their occurrence time and height are discussed by comparing them with meteorological conditions. Five processes are found to be responsible for the inversion variations: 1) cold-air advection accompanied by a cold-surge event, 2) cloud-top radiative cooling, 3) adiabatic heating due to subsidence, 4) shallow convection at or near the observation site, and 5) diurnal heating process due to boundary-layer growth.

A Climatology and Case Study of Continental Cold Season Dense Fog Associated with Low Clouds

Abstract This study focuses on dense fog cases that develop in association with low clouds and sometimes precipitation. A climatology of weather conditions associated with dense fog at Peoria, Illinois, for October–March 1970–94 indicated that fog forming in the presence of low clouds is common, in 57% of all events. For events associated with low pressure systems, low clouds precede dense fog in 84% of cases. Therefore, continental fogs often do not form under the clear-sky conditions that have received the most attention in the literature. Surface cooling is usually observed prior to fog on clear nights. With low cloud bases, warming or no change in temperature is frequent. Thus, fog often forms under conditions that are not well understood, increasing the difficulty of forecasting fog. The possible mechanisms for fog development under low cloud-base conditions were explored for an event when dense fog covered much of Illinois on 7 November 2006. Weather Surveillance Radar-1988 Doppler (WSR-88D) and rawinsonde observations indicated that evaporating precipitation aloft was important in moistening the lower atmosphere. Dense fog occurred about 6 h following light precipitation at the surface. The surface was nearly saturated following precipitation, but relative cooling was needed to overcome weak warm air advection and supersaturate the lower atmosphere. Surface (2 m) temperatures were near or slightly cooler than ground temperatures in most of the region, suggesting surface sensible heat fluxes were not important in this relative cooling. Sounding data indicated drying of the atmosphere above 800 hPa. Infrared satellite imagery indicated deep clouds associated with a low pressure system moved east of Illinois by early morning, leaving only low clouds. It is hypothesized that radiational cooling of the low cloud layer was instrumental in promoting the early morning dense fog.

Comparisons of Satellite-Derived Atmospheric Motion Vectors, Rawinsondes, and NOAA Wind Profiler Observations

Abstract Geostationary satellite-derived atmospheric motion vectors (AMVs) have been used over several decades in a wide variety of meteorological applications. The ever-increasing horizontal and vertical resolution of numerical weather prediction models puts a greater demand on satellite-derived wind products to monitor flow accurately at smaller scales and higher temporal resolution. The focus of this paper is to evaluate the accuracy and potential applications of a newly developed experimental mesoscale AMV product derived from Geostationary Operational Environmental Satellite (GOES) imagery. The mesoscale AMV product is derived through a variant on processing methods used within the University of Wisconsin—Madison Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) AMV algorithm and features a significant increase in vector density throughout the troposphere and lower stratosphere over current NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) processing methods for GOES-12 Imager data. The primary objectives of this paper are to 1) highlight applications of experimental GOES mesoscale AMVs toward weather diagnosis and forecasting, 2) compare the coverage and accuracy of mesoscale AMVs with the NOAA/NESDIS operational AMV product, and 3) demonstrate the utility of 6-min NOAA Wind Profiler Network observations for satellite-derived AMV validation. Although the more conservative NOAA/NESDIS AMV product exhibits closer statistical agreement to rawinsonde and wind profiler observations than do the experimental mesoscale AMVs, a comparison of these two products for selected events shows that the mesoscale product better depicts the circulation center of a midlatitude cyclone, boundary layer confluence patterns, and a narrow low-level jet that is well correlated with subsequent severe thunderstorm development. Thus, while the individual experimental mesoscale AMVs may sacrifice some absolute accuracy, they show promise in providing greater temporal and spatial flow detail that can benefit diagnosis of upper-air flow patterns in near–real time. The results also show good agreement between 6-min wind profiler and rawinsonde observations within the 700–200-hPa layer, with larger differences in the stratosphere, near the mean top of the planetary boundary layer, and just above the earth’s surface. Despite these larger differences within select layers, the stability of the difference profile with height builds confidence in the use of 6-min, ∼404-MHz NOAA Wind Profiler Network observations to evaluate and better understand satellite AMV error characteristics.

Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part IV: Comparison with 3DVAR in a Month-Long Experiment

In previous works in this series study, an ensemble Kalman filter (EnKF) was demonstrated to be promising for mesoscale and regional-scale data assimilation in increasingly realistic environments. Parts I and II examined the performance of the EnKF by assimilating simulated observations under both perfect- and imperfect-model assumptions. Part III explored the application of the EnKF to a real-data case study in comparison to a three-dimensional variational data assimilation (3DVAR) method in the Weather Research and Forecasting (WRF) model. The current study extends the single-case real-data experiments over a period of 1 month to examine the long-term performance and comparison of both methods at the regional scales. It is found that the EnKF systematically outperforms 3DVAR for the 1-month period of interest in which both methods assimilate the same standard rawinsonde observations every 12 h over the central United States. Consistent with results from the real-data case study of Part III, the EnKF can benefit from using a multischeme ensemble that partially accounts for model errors in physical parameterizations. The benefit of using a multischeme ensemble (over a single-scheme ensemble) is more pronounced in the thermodynamic variables (including temperature and moisture) than in the wind fields. On average, the EnKF analyses lead to more accurate forecasts than the 3DVAR analyses when they are used to initialize 60 consecutive, deterministic 60-h forecast experiments for the month. Results also show that deterministic forecasts of up to 60 h initiated from the EnKF analyses consistently outperform the WRF forecasts initiated from the National Centers for Environmental Prediction final analysis field of the Global Forecast System.

Impacts of Assimilation of Satellite and Rawinsonde Observations on Southern Hemisphere Baroclinic Wave Activity in the NCEP–NCAR Reanalysis

Abstract In this study, the impacts of the assimilation of satellite and rawinsonde observations on Southern Hemisphere (SH) baroclinic wave activity in the NCEP–NCAR reanalysis are examined by comparing analyses made with and without the assimilation of satellite data (SAT and NOSAT, respectively) for the year 1979, as well as by comparing analyses to the corresponding first guesses from 1958 to 1999. Comparing the eddy kinetic energy (EKE) computed based on the SAT and NOSAT analyses, it is found that the assimilation of satellite data generally decreases the EKE in the SH, which is the opposite of the findings for the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) in previous studies. The decrease of EKE by satellite data in the NCEP–NCAR reanalysis can be traced back to a low bias in retrieved satellite temperature (SATEMP) variance. The eddy available potential energy (EPE) is decreased even more than the EKE with the assimilation of SATEMP, making the waves more barotropic in the SAT analysis. The EKE analysis increment, that is, the difference between the EKE based on analysis and first guess, is a good quantity to indicate the impacts of all observations assimilated. In the NOSAT analysis, positive EKE analysis increments are found around the SH rawinsonde stations, indicating that the assimilation of rawinsonde data increases EKE significantly from the first guess. This also suggests that the NCEP–NCAR first guess is probably biased low. Positive analysis increments around the rawinsonde stations become even larger in the SAT analysis compared with the NOSAT, suggesting that with the assimilation of low-biased SATEMP data, the EKE in the analysis (the initial condition for next time) and hence the first guess is reduced, therefore the rawinsonde observations have to further increase the EKE from the first guess. The patterns of EKE increment from the presatellite (1958–77) and satellite (1979–99) eras show high degrees of similarities to the NOSAT and SAT reanalysis patterns, respectively, lending further support to these findings. The impact of the assimilation of satellite data on the trend of SH baroclinic wave activity is discussed. Positive trends in the SH EKE are found in both the NCEP–NCAR and ERA-40 reanalyses during 1958–99. After taking the impacts of satellite data into account, the EKE trend in the NCEP–NCAR reanalysis gets stronger, while that in the ERA-40 is largely weakened, which adds complications to assessing the real trend in SH baroclinic wave activity. Comparisons among the variances based on the two reanalyses, NCEP–NCAR first guess, SATEMP, and rawinsonde observations are presented to substantiate some of the findings discussed above, such as the low bias in energy in NCEP–NCAR first guess and SATEMP variance.

A Long-Term Climatology of Southerly and Northerly Low-Level Jets for the Central United States

A forty-year (1961–2000) climatology of southerly and northerly low-level jets (LLJs) was prepared from twice-daily rawinsonde observations for thirty-six stations in the central United States. Although there is extensive literature on southerly jets, their spatial variability has received little attention. Furthermore, there is a paucity of studies examining any aspects of northerly LLJs. Thus, the motivation for developing the climatology was to (1) examine in greater detail the spatial variation of LLJ characteristics across the central United States, (2) better document the nature of northerly LLJs, (3) provide a baseline for evaluating potential future changes in LLJ frequency and characteristics, and (4) assess through comparisons with previous studies what aspects of LLJs in the central United States remain poorly understood. The climatology is a substantial temporal and spatial expansion of previous studies and provides a more thorough description of the spatial variability of LLJs in terms of frequency, direction, speed, and elevation at annual, seasonal, and daily time scales. The complex spatial variations of LLJ characteristics in the central United States are summarized by seven objectively defined regions within the central United States with similar jet characteristics. These regions (Central Plains, Upper Midwest, East Central, Southeast, South Texas/Western Gulf, Foothills, and High Plains) vary in shape and extent by season and are indicative of the varying influence of different forcing mechanisms for LLJ formation. Comparison of the findings of this study with those of previous climatological analyses suggests that many of the characteristics of LLJs in the central United States are still incompletely documented and understood.