National Centers For Environmental Prediction Analyses Research Articles

Indian summer monsoon features in the NCEP analysis and forecast system

The diagnosis of the summer monsoon features over India are investigated using global analysis and forecast system (GFS) of the National Center for Environmental Prediction (NCEP). In addition, the performance of the NCEP GFS model in capturing the observed rainfall variability of the summer monsoon features over India through diagnostic parameters. We delineate the variability between two normal monsoon seasons with basic meteorological fields and significant parameters of kinetic energy, heat and moisture budgets. The 1200 UTC operational analyses and forecasts (day1 through day5) data for summer monsoon seasons of June, July, August and September (JJAS) of 2010 and 2011 are considered. Although both are normal monsoon seasons with seasonal rainfall (JJAS) departure of about −3.1% and 2.1% respectively, during 2010 and 2011, some differences are observed in the spatial distribution of rainfall. The diagnostic fields reasonably reproduced the variations in analyses and forecast fields of up to 5 days. The model forecasts of day1 delineated that the Indian summer monsoon features are well captured, whereas in day3 and day5, forecast depicts some differences. Overall the NCEP analysis and forecast fields not only produces mean monsoonal flow, but also captures seasonal variability realistically. This is evident from the large-scale balances represented by the analyses and model forecasts.

INSAT-3D low-level atmospheric motion vectors: Capability to capture Indian summer monsoon intra-seasonal variability

In India, Atmospheric Motion Vectors (AMVs) are derived operationally from the advanced Indian meteorological geostationary satellite INSAT-3D since July 2013 over Indian Ocean region and are used in the numerical model for forecast improvement. In this study, first-time the low-level monsoon winds derived from INSAT-3D satellite have been used to see how these winds are successful in capturing the intra-seasonal variability over the Indian Ocean region for the year 2016. A validation of AMVs is done on regular basis. In this study, the validation of low-level AMVs with National Center for Environmental Prediction (NCEP) analysis winds carried out during June to September 2016. The observed mean monthly features of the Indian Summer Monsoon (ISM) in July and August 2016 from low-level AMVs from INSAT-3D match well with those of NCEP analysis winds. INSAT-3D low-level AMVs are quite successful in capturing the northward propagation of low level jet and their locations during active and break monsoon conditions which are known features of the ISM. They are also able to explain the two dominant modes of variability: (i) one with a periodicity between 32 and 64 days, and (ii) another with a periodicity between 8 and 16 days, for the monsoon season of 2016 when Morlet wavelet transform analysis is performed for time series analysis. An EOF analysis is performed for the study of the spatial structure of intra-seasonal variability and temporal variability of INSAT-3D low-level AMVs over the Indian Ocean region for ISM 2016 and quantifies the results of EOF analysis by performing RMSE analysis between EOFs of INSAT-3D and NCEP wind data.

Impact of ECMWF, NCEP, and NCMRWF global model analysis on the WRF model forecast over Indian Region

The global model analysis has significant impact on the mesoscale model forecast as global model provides initial condition (IC) and lateral boundary conditions (LBC) for the mesoscale model. With this objective, four operational global model analyses prepared from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS), NCEP Global Forecasting System (GFS), and National Centre for Medium Range Weather Forecasting (NCMRWF) are used daily to generate IC and LBC of the mesoscale model during 13th December 2012 to 13th January 2013. The Weather Research and Forecasting (WRF) model version 3.4, broadly used for short-range weather forecast, is adopted in this study as mesoscale model. After initial comparison of global model analyses with Atmospheric Infrared Sounder (AIRS) retrieved temperature and moisture profiles, daily WRF model forecasts initialized from global model analyses are compared with in situ observations and AIRS profiles. Results demonstrated that forecasts initialized from the ECMWF analysis are closer to AIRS-retrieved profiles and in situ observations compared to other global model analyses. No major differences are occurred in the WRF model forecasts when initialized from the NCEP GDAS and GFS analyses, whereas these two analyses have different spatial resolutions and observations used for assimilation. Maximum RMSD is seen in the NCMRWF analysis-based experiments when compared with AIRS-retrieved profiles. The rainfall prediction is also improved when WRF model is initialized from the ECMWF analysis compared to the NCEP and NCMRWF analyses.

Fine particulate matter events associated with synoptic weather patterns, long-range transport paths and mixing height in the Taipei Basin, Taiwan

Asian dust storms (ADS) and PM2.5 (particle pollution) events have an evident influence on air quality in Taiwan. However, the synoptic weather patterns and atmospheric conditions on ADS days are not entirely similar to those related to PM2.5 event days. The aim of this study is to clarify the weather characteristics such as synoptic weather patterns, long-range transport paths, and stagnant conditions that precipitate PM2.5 events. Air quality and meteorological data from 2006 to 2013 were obtained from government-owned observation stations, and the mixing height was estimated in relation to the Nozaki planetary boundary layer height. This study used back trajectories as simulated gridded analysis data, which were based on kinematic trajectory analysis using NASA's GMAO (Global Modeling Assimilation Office) and NCEP (National Centers for Environmental Prediction) analyses. For testing the differences between means of two large, independent samples, the confidence interval of a common statistical indicator was employed. The results show that in comparison to low PM2.5 level days, weather features such as stagnant conditions, including low mixing height and low wind speed, low rainfall amount, and high solar hours, are favorable for inducing PM2.5 events. Eighty percent of the synoptic weather patterns on PM2.5 days were associated with either polar continental high pressure, a high-pressure system in mainland China moving from the continent to the sea, or a stationary front stretching from southern China to the East Sea, and moving eastwards. More than 81% of the contributing factors of the causes of PM2.5 events were found to be related to stagnant conditions. The pattern of the contributing factors causing the maximum-recorded concentration of PM2.5, (73.90 μg/m3) was attributed to local emissions, and a long-range transport time that was extended for a longer period over the land than over the sea. The synoptic weather patterns were also found to affect the spatial distribution of PM2.5 concentrations in the basin.

Assimilation of GNSS radio occultation observations in GRAPES

Abstract. This paper reviews the development of the global navigation satellite system (GNSS) radio occultation (RO) observations assimilation in the Global/Regional Assimilation and PrEdiction System (GRAPES) of China Meteorological Administration, including the choice of data to assimilate, the data quality control, the observation operator, the tuning of observation error, and the results of the observation impact experiments. The results indicate that RO data have a significantly positive effect on analysis and forecast at all ranges in GRAPES, not only in the Southern Hemisphere where conventional observations are lacking but also in the Northern Hemisphere where data are rich. It is noted that a relatively simple assimilation and forecast system in which only the conventional and RO observation are assimilated still has analysis and forecast skill even after nine months integration, and the analysis difference between both hemispheres is gradually reduced with height when compared with NCEP (National Centers for Environmental Prediction) analyses. Finally, as a result of the new on-board payload of the Chinese FengYun-3 (FY-3) satellites, the research status of the RO of FY-3 satellites is also presented.

Quality assessment and assimilation of Megha‐Tropiques SAPHIR radiances into WRF assimilation system

AbstractThis study presents an initial assessment of the quality of radiances measured from SAPHIR (Sounder for Probing Vertical Profiles of Humidity) on board Megha‐Tropiques (Indo‐French joint satellite), launched by the Indian Space Research Organisation on 12 October 2011. The radiances measured from SAPHIR are compared with those simulated by the radiative transfer model (RTM) using radiosondes measurements, Atmospheric Infrared Sounder retrievals, and National Centers for Environmental Prediction (NCEP) analyzed fields over the Indian subcontinent, during January to November 2012. The radiances from SAPHIR are also compared with the similar measurements available from Microwave Humidity Sounder (MHS) on board MetOp‐A and NOAA‐18/19 satellites, during January to November 2012. A limited comparison is also carried out between SAPHIR measured and the RTM computed radiances using European Centre for Medium‐Range Weather Forecasts analyzed fields, during May and November 2012. The comparison of SAPHIR measured radiances with RTM simulated and MHS observed radiances reveals that SAPHIR observations are of good quality. After the initial assessment of the quality of the SAPHIR radiances, these radiances have been assimilated within the Weather Research and Forecasting (WRF) three‐dimensional variational data assimilation system. Analysis/forecast cycling experiments with and without SAPHIR radiances are performed over the Indian region during the entire month of May 2012. The assimilation of SAPHIR radiances shows considerable improvements (with moisture analysis error reduction up to 30%) in the tropospheric analyses and forecast of moisture, temperature, and winds when compared to NCEP analyses and radiances measurement obtained from MHS, Advanced Microwave Sounding Unit‐A, and High Resolution Infrared Sounder. Assimilation of SAPHIR radiances also resulted in substantial improvement in the precipitation forecast skill when compared with satellite‐derived rain. Overall, initial results show the usefulness of SAPHIR radiances in the numerical weather prediction data assimilation systems.

Evaluation of upper tropospheric humidity from NCEP analysis and WRF Model Forecast with Kalpana observation during Indian summer monsoon 2010

AbstractThis paper presents an evaluation of upper tropospheric humidity (UTH) fields from National Center for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analysis and Weather Research and Forecasting (WRF) mesoscale model forecast with satellite derived UTH. For this purpose, UTH (weighted average of relative humidity in 200–500 hPa layer) obtained from Indian geostationary satellite Kalpana is compared with the UTH computed from NCEP analysis and WRF model forecast during the summer monsoon, 2010. Kaplana‐derived products are not included in the NCEP analysis: therefore, a comparison of the two independent datasets is promising. This study indicates that UTH derived from different sources (Kalpana UTH, NCEP Analysed and subsequent model forecasted UTH) matches very well over the Indian region. However, there are region‐specific small departures in UTH which increases the model predicted UTH. Copyright © 2012 Royal Meteorological Society

Intercomparison of stratospheric ozone and temperature profiles during the October 2005 Hohenpeißenberg Ozone Profiling Experiment (HOPE)

Abstract. Thirteen clear nights in October 2005 allowed successful intercomparison of the lidar operated since 1987 by the German Weather Service (DWD) at Hohenpeißenberg (47.8° N, 11.0° E) with the Network for the Detection of Atmospheric Composition Change (NDACC) travelling standard lidar operated by NASA's Goddard Space Flight Center. Both lidars provide ozone profiles in the stratosphere, and temperature profiles in the strato- and mesosphere. Additional ozone profiles came from on-site Brewer/Mast ozonesondes, additional temperature profiles from Vaisala RS92 radiosondes launched at Munich (65 km north-east), and from operational analyses by the US National Centers for Environmental Prediction (NCEP). The intercomparison confirmed a low bias for ozone from the DWD lidar in the 33 to 43 km region, by up to 10%. This bias is caused by the DWD ozone algorithm, and is consistent with previous comparisons of the DWD lidar with SAGE, GOMOS and other instruments. During HOPE, precision (repeatability) for ozone data from both lidars was better than 5% between 20 and 40 km altitude, dropping to 10% near 45 km, and to 50% near 50 km. These results are consistent with previous NDACC intercomparisons, and confirm the reliability of the NASA NDACC travelling standard lidar. Temperature from the DWD lidar showed a 1 to 2 K cold bias from 30 to 65 km against the NASA lidar, and a 2 to 4 K cold bias against radiosondes and NCEP. This is also consistent with previous intercomparisons. Temperature precision (repeatability) for the DWD lidar was better than 2 K from 30 to 50 km, decreasing to 10 K near 70 km. For the NASA lidar, precision is expected to be better than 1 K over the 30 to 70 km range. However, due to the much lower temperature precision of the DWD lidar, this could not be checked during HOPE. It was noted that the current DWD algorithm over-estimates temperature uncertainty, which should be reduced by a factor of 2.2 (e.g. from 22 K to 10 K near 70 km). The HOPE intercomparison did uncover a 290 m range error (upward shift) of the DWD lidar data. When this shift is removed, the bias of the ozone algorithm is corrected, and a better background estimation is used, ozone profiles from the DWD lidar agree very well with both the NASA lidar and SAGE. Systematic differences are then smaller than 3% between 20 and 44 km, and smaller than 5% between 17 and 47 km. These differences are close to zero, and are not (statistically) significant. The cold temperature bias against the NASA lidar also disappears when the DWD temperature processing is corrected for the 290 m range error, and more appropriate values for the Earth's gravity acceleration are used. Compared to the radiosondes or NCEP analyses, however, both lidars show 1 to 2 K lower temperatures over the entire 15 to 35 km range. Temperature and ozone variations are tracked well by both lidars, by ozone- and radiosondes, and by NCEP analyses. Correlations exceed 0.8 to 0.9 at most stratospheric levels. They decrease at levels above 40 km, especially for ozone or NCEP temperature. The ozone and temperature bias of the DWD lidar does not appear to have changed over time. Records of ozone and temperature from the DWD lidar should be consistent over the years. Nevertheless, the HOPE intercomparison was instrumental in uncovering and repairing several long-standing errors. HOPE also confirmed the reliability of the NASA lidar as a travelling standard. Now the entire DWD lidar data record needs to be reprocessed with the improved and revised algorithms.

Uncertainties in atmospheric surface pressure fields from global analyses

Operational analyses from the U.S. National Centers for Environmental Prediction (NCEP) and the European Centre for Medium‐Range Weather Forecasts (ECMWF) provide, among other quantities, atmospheric surface pressure. This field is especially useful for data processing and interpretation of altimeter and gravity satellite missions like Jason‐1 and GRACE (Gravity Recovery and Climate Experiment). Quantitative use of this pressure field, however, requires knowledge of its uncertainties. To assess these uncertainties, we compare NCEP and ECMWF analyses of surface pressure to an extensive set of surface barometric observations for the period 2001–2005, both land‐ and ocean‐based, as well as to each other. Differences between each analysis and the observations are quite similar, indicating that both analyses generally fit the station observations equally well. Such differences, including both bias and variance about the bias, are largest over parts of the Southern Ocean, and over Asia, central Africa, and Antarctica. These bias and variance differences between the NCEP and ECMWF surface pressure fields change only slightly over the period of study. More importantly, though, such differences are substantially smaller than those found by previous studies for an earlier period (1993–1995), suggesting recent improvements in both analyses. The largest differences are found over high latitudes, particularly over Antarctica, and in the respective winter hemisphere. Much of the differences occur on rapid timescales, though with a significant portion at timescales longer than 1–2 months. The potential impact of such uncertainties on de‐aliasing and interpreting sea level and gravity data from altimeter and GRACE missions is discussed.

A ray‐tracing technique for improving Satellite Laser Ranging atmospheric delay corrections, including the effects of horizontal refractivity gradients

In this paper we study the effects of horizontal refractivity gradients on the propagation of optical signals for a globally distributed set of Satellite Laser Ranging (SLR) stations. Using two‐dimensional ray tracing and globally distributed satellite data from NASA’s Atmospheric Infrared Sounder (AIRS), as well atmospheric fields from the National Center for Environmental Prediction (NCEP), we calculate the effects of horizontal gradients on a global set of SLR stations using 2 years of data during 2004 and 2005. We investigate in detail how seasonal and diurnal changes, latitudinal dependence, topography, and oceans affect the gradients at specific locations, as well as the application of these gradients on a set of SLR data from the LAGEOS 1 and 2 geodetic satellites. For the year 2004, AIRS ray‐tracing results showed maximum north‐south (NS) gradient delays of 50 mm at 10° elevation angle at Herstmonceux, UK and east‐west (EW) gradient delays of 35 mm at Yarragadee, Australia. Mean NS and EW gradients had delays not exceeding 5 mm for both AIRS and NCEP analysis at all stations. The standard deviations of AIRS gradients ranged from 6 to 12 mm, while NCEP gradients ranged from 3 to 9 mm, depending on location and time of year. The effects of applying AIRS and NCEP total ray‐tracing results, including horizontal gradient contributions, to a set of global SLR geodetic data resulted in reduction of the solution residuals by up to 43% in variance percent difference. This is a highly significant contribution for the SLR technique’s effort to reach an accuracy at the 1‐mm level this decade.

Tropical tropopause climatology as observed with radio occultation measurements from CHAMP compared to ECMWF and NCEP analyses

A temperature climatology from radio occultation measurements of the CHAllenging Minisatellite Payload (CHAMP) satellite, comprising five years of measurements from September 2001 to August 2006, was analyzed in the tropical (15°S–15°N) tropopause region. Validation against operational analyses of the European Centre for Medium‐Range Weather Forecasts (ECMWF) showed excellent overall agreement except near the tropical tropopause, where systematic differences generally amount to −1 K to −2 K. Validation against tropopause temperatures from the U.S. National Centers for Environmental Prediction (NCEP) reanalyses showed NCEP deviations of about +2 K to +4 K. The ECMWF deviations can be attributed to the lower vertical resolution and weaker representation of atmospheric wave activity in ECMWF analyses. This evidence is confirmed by improved data from February 2006 onwards, where an enhancement of the ECMWF analyses became effective. Initial inspection of extreme tropical tropopause profiles provided evidence that extremely cold tropopause temperatures can reach −100°C.

Application of Principal Component Analysis to CHAMP Radio Occultation Data for Quality Control and a Diagnostic Study

Abstract A principal component analysis (PCA) method is applied to Challenging Minisatellite Payload (CHAMP) level-2 radio occultation (RO) observations and the corresponding global analyses from the National Centers for Environmental Prediction (NCEP) in March 2004. The PCA is performed on a square symmetric vertical correlation matrix of observed or modeled RO profiles. By decomposing the matrix into pairs of loadings (EOFs) and associated principal components (PCs), outliers are identified and important modes that explain most variances of the vertical variability of the atmosphere as represented by the GPS RO data and the NCEP analyses are extracted and compared. Specifically, a quality control of RO data based on Hotelling’s T 2 index is applied first, which removes 255 RO profiles from 4884 total profiles (about 5%) and smoothes the distributions of PC modes, making the remaining GPS RO dataset much more meaningful. The leading PC mode for global refractivity explains 60% of the total variance and is associated with a symmetric zonal pattern, with positive anomalies in the Tropics and negative anomalies at the two poles. The second PC mode explains an additional 16% of the total variance and shows a dipole pattern with positive anomalies in the North Pole and negative anomalies in the South Pole. Three significant positive anomalies are also found in the second and third PC modes over three predominant convective areas in the western Pacific, South America, and Africa in the Tropics. The first leading PC mode calculated from global NCEP analyses compared favorably with that from CHAMP observations, which proves that NCEP analyses are capable of representing most of the variance of the atmospheric profiles. However, disagreements between CHAMP observations and NCEP analyses are noticed in the second EOF over the Tropics and the Southern Hemisphere (SH). It is also found that the NCEP analyses describe CHAMP-observed larger vertical scale features better than smaller-scale features, captures features of more leading EOF modes in the Northern Hemisphere than in the SH and the Tropics, and does not capture the vertical structures revealed by the EOFs in CHAMP observations near and above the tropopause in the Tropics.

Radiance assimilation in studying Hurricane Katrina

The Gridpoint Statistical Interpolation (GSI) scheme released in June 2006 from the National Center for Environmental Prediction (NCEP) and the Weather Research and Forecasting (WRF) model are used in this study for improving hurricane analysis. In addition to incorporating observations from conventional instruments and from operational satellites, Special Sensor Microwave Imager Sounder (SSMIS) measurements are assimilated through GSI. A “control‐run” using current NCEP analysis field and a “test‐run” using additional SSMIS data are compared. The error in forecasting the hurricane center after 30‐hour grew up dramatically for the control‐run while the error for the test‐run remains stable for the entire 48‐hour forecasting. The forecasting surface minimum pressure and maximum wind speed from the test‐run generally agree with observations better. Results for the test‐run also showed that the warm core at 200 hPa is strengthened and extended as Hurricane Katrina was developing toward its mature stage, which is consistent with SSMIS observations.

Point probabilistic prediction of precipitation and quantitative precipitation forecast in Western Himalayas

Northwest India is comprised of various Himalayan mountain ranges. These ranges are having different altitude and orientations all along this region. During winter season enormous amount of precipitation is received in this region due to westward moving low pressure synoptic weather systems called Western Disturbances (WD). Variable terrain gives rise to low level circulation during the passage of these systems. Surface weather elements like temperature, pressure and relative humidity are highly dependent on local topography. To draw projected weather, uncertainties involved in the relationship between upper level circulation and surface weather is tried to be formally expressed in statistical terms. Perfect Prognostic Method (PPM) is used to forecast Probability of Precipitation (PoP) occurrence, followed by Quantitative Precipitation Forecast (QPF) model. The objective is to give projected weather in lead time of 24 hour at one of the specific sites, Sonamarg, situated in Great Himalayan range. Analysis data from the National Center for Environmental Prediction (NCEP), US and station data of three stations from India Meteorological Department (IMD), India is used for development of model. Data of December, January, February and March (DJFM) months for 12 year (1984-96) is taken for developmental mode. Whereas IMD data with (i) NCEP analysis, (ii) NCMRWF analysis and (iii) NCMRWF’s T80 day 1 forecast for DJFM months for 1996-97 is considered for the verification purpose. Result shows that PoP model could predict with 90.4% accuracy for developmental set, whereas in verification cases best prediction is made with accuracy of 86.8%. In case of QPF model percentage correct forecast is made with 45.0% in developmental set, whereas maximum 54.2% accuracy is achieved in verification sample.

Lower stratospheric temperature differences between meteorological analyses in two cold Arctic winters and their impact on polar processing studies

A quantitative comparison of six meteorological analyses is presented for the cold 1999/2000 and 1995/1996 Arctic winters. Using different analyzed data sets to obtain temperatures and temperature histories can have significant consequences. The area with temperatures below a polar stratospheric cloud (PSC) formation threshold commonly varies by ∼25% between the analyses, with some differences over 50%. Biases between analyses vary from year to year; in January 2000, Met Office analyses were coldest and National Centers for Environmental Prediction (NCEP) analyses were warmest, while NCEP analyses were usually coldest in 1995/1996 and NCEP/National Center for Atmospheric Research Reanalysis (REAN) were usually warmest. Freie Universität Berlin analyses are often colder than others at T ≲ 205 K. European Centre for Medium‐Range Weather Forecasts (ECMWF) temperatures agreed better with other analyses in 1999/2000, after improvements in the assimilation system, than in 1995/1996. Temperature history case studies show substantial differences using Met Office, NCEP, REAN, ECMWF, and NASA Data Assimilation Office (DAO) analyses. In January 2000 (when a large cold region was centered in the polar vortex), all analyses gave qualitatively similar results. However, in February 2000 (a much warmer period) and in January and February 1996 (comparably cold to January 2000 but with the cold region near the polar vortex edge), distributions of “potential PSC lifetimes” and total time spent below a PSC formation threshold varied significantly between the analyses. Largest peaks in “PSC lifetime” distributions in January 2000 were at 4–6 and 11–14 days, while in 1996 they were at 1–3 days. Different meteorological conditions in comparably cold winters have a large impact on expectations for PSC formation and on the effects of discrepancies between different meteorological analyses. Met Office, NCEP, REAN, ECMWF, and DAO analyses are commonly used in modeling polar processes; the choice of analysis can strongly influence the results of such studies.

Investigations on long‐term temperature changes in the upper stratosphere using lidar data and NCEP analyses

OHP lidar data and National Centers for Environmental Prediction (NCEP) stratospheric temperature analyses provide long and continuous databases for the middle and upper stratosphere that are highly valuable for long‐term studies. However, each data set has limitations. Comparisons between lidar data from 1979 to 1993 and NCEP data interpolated from the global analyses to the lidar location reveal significant mean temperature differences. Insight into the origin of the differences offers an opportunity to improve the overall quality of temperature monitoring in the stratosphere. Some of the differences can be explained by instrumental effects in the lidar system. In the stratosphere most of the limitations in lidar temperatures appear below 35–40 km, due to events of lidar misalignment (as large as 10 K) or to the effects on lidar data of volcanic aerosols (as large as 15 K). Changing biases between lidar and NCEP temperatures above 5 hPa coincide with replacement of satellites used in the NCEP analyses. However, some bias differences in upper stratospheric temperatures remain even after NCEP adjustments are made, based on rocketsonde comparisons. While these biases have been already suspected, they had never been explained. Here we suggest that the remaining bias (2–4 K) is caused by tidal influences, heretofore not accounted for by the NCEP adjustment procedure. Lidar profiles have been filtered in their lower part for misalignment and aerosol contamination. Long‐term changes have been compared, and a factor of 2 in trend differences have been reported. No significant trends (at 95% confidence) have been detected except with lidar around the stratopause and with NCEP analyses at 5 and 10 hPa. According to instrumental limitations of both data sets the temperature trend may vary from 1 to 3 K with altitude (10–0.4 hPa). Because only satellite data can provide global trend estimates and because lidar data have been chosen for ground‐based stratospheric monitoring programs, we suggest some plans to overcome these difficulties for past and future measurements. This should allow a more confident use for future trend estimates from both data sets.

Cyclone surface pressure fields and frontogenesis from NASA scatterometer (NSCAT) winds

Two extratropical marine cyclones and their associated frontal features are examined by computing surface pressure fields from NASA scatterometer (NSCAT) winds. A variational method solves for a new surface pressure field by blending high‐resolution (25 km) relative vorticity computed along the satellite track with an initial geostrophic vorticity field. Employing this method with each successive pass of the satellite over the study area allows this surface pressure field to evolve as dictated by the relative vorticity patterns computed from NSCAT winds. The result is a high‐resolution surface pressure field that captures features such as fronts and low‐pressure centers in more detail than National Centers for Environmental Prediction (NCEP) reanalyses. While using the actual relative vorticity to adjust the geostrophic vorticity ignores the ageostrophy of surface winds, which can be significant in the vicinity of fronts and jet streaks, it is a necessary approximation given that the technique uses only surface data. The NSCAT surface pressure fields prove to be nearly as accurate as NCEP reanalyses when compared to ship and buoy observations, which is an encouraging result given that NCEP reanalyses incorporate a myriad of data sources and the NSCAT fields rely primarily on one source. In addition, the high‐resolution relative vorticity fields computed from NSCAT winds reveal the location of surface fronts in great detail. These fronts are verified using NCEP analyses, in situ data, and satellite imagery.

Climate Simulations of African Easterly Waves

The three-dimensional structure and propagation characteristics of African easterly waves for the 1986‐94 period are studied with June‐August European Centre for Medium-Range Weather Forecasts (ECMWF) operational analyses, National Centers for Environmental Prediction (NCEP) reanalyses, and Canadian Centre for Climate Modelling and Analysis GCM output. Specific consideration is given to the differences in the timemean circulation, synoptic-scale variance, covariance, and principal oscillation patterns. The African easterly waves derived from the ECMWF and NCEP analyses are very similar, with both providing a reasonably realistic depiction of African easterly waves—given the agreement with one another, earlier station data studies, and theory. Where significant differences exist between the results from the two data assimilation systems they expectably do so over the tropical Atlantic, and in fields that are not directly observed (such as vertical velocity). The situation with the GCM is not as favorable but there are some encouraging areas of agreement—despite the GCM’s relatively coarse resolution and absence of observed data to constrain it. Selected points of agreement and disagreement between the GCM and the analyses include the following. (i) The GCM African easterly wave energetics are comparable with the analyses in terms of the sign and magnitude of the energy transfers from the time-mean state to the waves. (ii) The northern track of easterly waves seen in the analyses terminates prematurely in the GCM at the African coast. (iii) The southerly track of moist easterly waves seen in the analyses near the seasonal rainband is absent in the GCM. Possible reasons for the deficiencies are discussed. The sensitivity of the GCM-simulated African easterly waves to CO2 doubling is investigated. Together with significant mean warming and moistening over the northern Sahara, the level of simulated African easterly wave activity increases with CO 2 doubling.

Ocean general circulation model sensitivity to forcing from scatterometer winds

Enhanced global surface wind fields are constructed from a blend of NASA scatterometer (NSCAT) and ERS 2 scatterometer data and National Centers for Environmental Prediction (NCEP) analyses, at 6‐hour intervals, for a repeatable annual cycle from August 1996 through July 1997. Wind field properties (wind speed, zonal and meridional wind stresses, wind stress curl, and kinetic energy input) for the enhanced winds are compared with the NCEP analyses for the same time period. Large‐scale, zonal patterns dominate annual average difference maps for wind speed and both components of wind stress. Wind stress curl differences are largest in the subpolar North Atlantic and in the midlatitudes of the southern hemisphere. The importance of wind field differences are measured by their impacts on the response of an ocean general circulation model (OGCM). Twin experiments are performed using the National Center for Atmospheric Research Climate System Model ocean component in stand‐alone mode. The annual mean OGCM responses to the enhanced winds and to surface winds from the NCEP analyses are compared for barotropic stream function, surface velocities, upper ocean upwelling, sea surface temperature (SST), surface heat flux, meridional overturning stream function, and total northward heat transport. Differences in the annual mean responses are attributable to differences in the mean forcing and not to the mesoscale signal that is present in the enhanced winds but not resolved by the OGCM. Differences in SST and surface heat flux are partitioned between local thermodynamic balances and balances involving ocean dynamics as well. Northward heat transport differences of order −0.2 PW in the southern hemisphere midlatitudes are consistent with weaker eastward and southward stresses in the westerlies and a 27% reduction in kinetic energy input from enhanced winds. This difference in northward heat transport is compensated in the southern hemisphere tropical Pacific where surface stresses and the OGCM surface response to enhanced winds are more westward and more divergent, resulting in 27% greater kinetic energy input. An extensive appendix details the realistic high‐wavenumber character of the enhanced winds.

1973–1996 Trends in depth‐averaged tropospheric temperature

The analysis of thickness derived from the National Center for Environmental Prediction (NCEP) reanalysis indicates that there has been no statistically significant warming trend in layer‐averaged global tropospheric temperatures during the period 1979–1996. While this result is at variance with data based on surface information, NCEP trends and interannual variations are closely related to tropospheric mean‐layer temperatures obtained from the satellite‐based microwave sounding unit (MSU) lower‐tropospheric data set. When a longer period, 1973–1996, is used for the NCEP analysis, a warming trend within a 95% confidence interval is derived. However, it is nearly half the magnitude of the surface‐derived trends. Strong correlations of the yearly global surface temperature anomalies and those calculated for several layers in the NCEP data indicate coincident interannual temperature variations throughout the depth of the troposphere. The disagreement of both our analysis and satellite‐derived estimates with surface data could be due to a land surface measurement bias, a result of land use changes such as deforestation and agricultural expansion whose effects are not yet accounted for in the global temperature records, and/or a decoupling between surface and lower‐tropospheric temperature over the last several years.