Forecast Impact Research Articles

Impact of ATOVS Radiance on the Analysis and Forecasts of a Mesoscale Model over the Indian Region During the 2008 Summer Monsoon

Assimilation experiments are performed with the Weather Research and Forecasting (WRF) models’ three-dimensional variational data assimilation (3D-Var) scheme to evaluate the impact of directly assimilating the Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) radiance, including AMSU-A, AMSU-B and HIRS, on the analysis and forecasts of a mesoscale model over the Indian region. The present study is, to our knowledge, the first where the impact of ATOVS radiance has been evaluated on the analysis and forecasts of a mesoscale model over the Indian region. The control (without ATOVS radiance) as well as experimental (which assimilated ATOVS radiance) run were made for 48 h starting at 0000 UTC during the entire July 2008. The impacts of assimilating the radiances from different instruments (e.g., AMSU-A, AMSU-B and HIRS) were measured in comparison to the control run. The assimilation experiments for July 2008 (30 cases) demonstrated a positive impact of the assimilated ATOVS radiance on both the analysis state as well as subsequent short-range forecasts. Relative to the control run, the moisture analysis was improved with the assimilation of AMSU-B and HIRS radiance, while AMSU-A was mainly responsible for improved temperature analysis. The comparison of the model-predicted temperature, moisture and wind with NCEP analysis indicated that a positive forecast impact is achieved from each of the three instruments. HIRS and AMSU-A radiance yielded only a slight positive forecast impact, while AMSU-B radiance had the largest positive forecast impact for moisture, temperature and wind. The comparison of model-predicted rainfall with observed rainfall indicates that ATOVS radiance, particularly AMSU-B and HIRS, impacted the rainfall positively. This study clearly shows that the improved analysis of mid-tropospheric moisture, due to the assimilation of AMSU-B radiances, is a key factor to improve the short-term forecast skill of a mesoscale model.

Impact of assimilation of ATOVS temperature and humidity and SSM/I total precipitable water on the simulation of a monsoon depression

The present study explored the effect of assimilation of Advanced TIROS Vertical Sounder (ATOVS) temperature and humidity profiles and Spectral sensor microwave imager (SSM/I) total precipitable water (TPW) on the simulation of a monsoon depression which formed over the Arabian Sea during September 2005 using the Weather Research and Forecast model. The three-dimensional variational (3DVAR) data assimilation technique has been employed for the purpose of assimilation of satellite observations. Statistical scores like “equitable threat score,” “bias score,” “forecast impact,” and “improvement parameter” have been used to examine the impact of the above-mentioned satellite observations on the numerical simulation of a monsoon depression. The diagnostics of this study include verification of the vertical structure of depression, in terms of temperature anomaly profiles and relative vorticity profiles with observations/analysis. Additional diagnostics of the study include the analysis of the heat budget and moisture budget. Such budget studies have been performed to provide information on the role of cumulus convection associated with the depression. The results of this study show direct and good evidence of the impact of the assimilation of the satellite observations using 3DVAR on the dynamical and thermodynamical features of a monsoon depression along with the effect of inclusion of satellite observation on the spatial pattern of the simulated precipitation associated with the depression. The “forecast impact” parameter calculated for the wind speed provides good evidence of the positive impact of the assimilation of ATOVS temperature and humidity profiles and SSM/I TPW on the model simulation, with the assimilation of the ATOVS profiles showing better impact in terms of a more positive value of the “forecast impact” parameter. The results of the study also indicate the improvement of the forecast skill in terms of “equitable threat score” and “bias score” due to the assimilation of satellite observation.

Assimilating Satellite Observations of Clouds and Precipitation into NWP Models

No Abstract available.

Gender, Social Networks, and Performance Among Sell-Side Analysts

We compare forecast accuracy and recommendation impact of male and female all-star analysts, and how social networks affect their performances. Male analysts are significantly more accurate than female after controlling for various analysts and firm effects. However this gender difference disappears among analysts connected to company management through social networks. Among connected analysts, male make more impactful buy recommendations than female, and such recommendations are rewarded in promotions to high-rank all-star status. Connected male analysts’ advantage in making impactful buy recommendations disappeared after Reg-FD, which curtailed selective disclosure. These results suggest that male and female analysts reap differential benefits from social networks; in particular, male analysts were apt at tapping social networks for soft, material information.

Forecast Impact of Targeted Observations: Sensitivity to Observation Error and Proximity to Steep Orography

Abstract For a targeted observations case, the dependence of the size of the forecast impact on the targeted dropsonde observation error in the data assimilation is assessed. The targeted observations were made in the lee of Greenland; the dependence of the impact on the proximity of the observations to the Greenland coast is also investigated. Experiments were conducted using the Met Office Unified Model (MetUM), over a limited-area domain at 24-km grid spacing, with a four-dimensional variational data assimilation (4D-Var) scheme. Reducing the operational dropsonde observation errors by one-half increases the maximum forecast improvement from 5% to 7%–10%, measured in terms of total energy. However, the largest impact is seen by replacing two dropsondes on the Greenland coast with two farther from the steep orography; this increases the maximum forecast improvement from 5% to 18% for an 18-h forecast (using operational observation errors). Forecast degradation caused by two dropsonde observations on the Greenland coast is shown to arise from spreading of data by the background errors up the steep slope of Greenland. Removing boundary layer data from these dropsondes reduces the forecast degradation, but it is only a partial solution to this problem. Although only from one case study, these results suggest that observations positioned within a correlation length scale of steep orography may degrade the forecast through the anomalous upslope spreading of analysis increments along terrain-following model levels.

Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters

AbstractThe development of the adjoint of the forecast model and of the adjoint of the data assimilation system (adjoint‐DAS) makes feasible the evaluation of the local sensitivity of a model forecast aspect with respect to a large number of parameters in the DAS. In this study it is shown that, by exploiting sensitivity properties that are intrinsic to the analyses derived from a minimization principle, the adjoint‐DAS software tools developed at numerical weather prediction centres for observation and background sensitivity may be used to estimate the forecast sensitivity to observation‐ and background‐error covariance parameters and for forecast impact assessment. All‐at‐once sensitivity to error covariance weighting coefficients and first‐order impact estimates are derived as a particular case of the error covariance perturbation analysis. The use of the sensitivity information as a DAS diagnostic tool and for implementing gradient‐based error covariance tuning algorithms is illustrated in idealized data assimilation experiments with the Lorenz 40‐variable model. Preliminary results of forecast sensitivity to observation‐ and background‐error covariance weight parameters are presented using the fifth‐generation NASA Goddard Earth Observing System (GEOS‐5) atmospheric DAS and its adjoint developed at the Global Modeling and Assimilation Office. Copyright © 2010 Royal Meteorological Society

A Two-Season Impact Study of the WindSat Surface Wind Retrievals in the NCEP Global Data Assimilation System

Abstract A two-season observing system experiment (OSE) was used to quantify the impacts of assimilating the WindSat surface winds product developed by the Naval Research Laboratory (NRL). The impacts of assimilating these surface winds were assessed by comparing the forecast results through 168 h for the months of October 2006 and March 2007. The National Centers for Environmental Prediction’s (NCEP) Global Data Assimilation/Global Forecast System (GDAS/GFS) was used, at a resolution of T382-64 layers, as the assimilation system and forecast model for these experiments. A control simulation utilizing all the data types assimilated in the operational GDAS was compared to an experimental simulation that added the WindSat surface winds. Quality control procedures required to assimilate the surface winds are discussed. Anomaly correlations (ACs) of geopotential heights at 1000 and 500 hPa were evaluated for the control and experiment during both seasons. The geographical distribution of the forecast impacts (FIs) on the wind field and temperature fields at 10-m height and 500 hPa is also discussed. The results of this study show that assimilating the surface wind retrievals from the WindSat satellite improve the NCEP GFS wind and temperature forecasts. A positive FI, which suggests that the error growth of the experiment is slower than the control, has been realized in the NCEP GDAS/GFS wind and temperature forecasts through 24 h. The WindSat experiment AC scores are similar to the control simulation AC scores until the day 6 forecasts, when the improvements in the WindSat experiment become greater for both seasons and in most of the cases.

Forecast sensitivity to the observation error covariance in variational data assimilation

The development of the adjoint of the forecast model and of the adjoint of the data assimilation system (adjointDAS) make feasible the evaluation of the derivative-based forecast sensitivity to DAS input parameters in numerical weather prediction (NWP). The adjoint estimation of the forecast sensitivity to the observation error covariance in the DAS is considered as a practical approach to provide all-at-once first order estimates to the forecast impact as a result of variations in the specification of the observation error statistics and guidance for tuning of error covariance parameters. The proposed methodology extends the capabilities of the adjoint modeling tools currently in place at major NWP centers for observation sensitivity and observation impact analysis. Illustrative numerical results are presented with the fifth-generation NASA Goddard Earth Observing System (GEOS-5) atmospheric DAS and its adjoint.

Relative Short-Range Forecast Impact from Aircraft, Profiler, Radiosonde, VAD, GPS-PW, METAR, and Mesonet Observations via the RUC Hourly Assimilation Cycle

Abstract An assessment is presented on the relative forecast impact on the performance of a numerical weather prediction model from eight different observation data types: aircraft, profiler, radiosonde, velocity azimuth display (VAD), GPS-derived precipitable water, aviation routine weather report (METAR; surface), surface mesonet, and satellite-based atmospheric motion vectors. A series of observation sensitivity experiments was conducted using the Rapid Update Cycle (RUC) model/assimilation system in which various data sources were denied to assess the relative importance of the different data types for short-range (3–12 h) wind, temperature, and relative humidity forecasts at different vertical levels and near the surface. These experiments were conducted for two 10-day periods, one in November–December 2006 and one in August 2007. These experiments show positive short-range forecast impacts from most of the contributors to the heterogeneous observing system over the RUC domain. In particular, aircraft observations had the largest overall impact for forecasts initialized 3–6 h before 0000 or 1200 UTC, considered over the full depth (1000–100 hPa), followed by radiosonde observations, even though the latter are available only every 12 h. Profiler data (including at a hypothetical 8-km depth), GPS-precipitable water estimates, and surface observations also led to significant improvements in short-range forecast skill.

Impact study with observations assimilated over North America and the North Pacific Ocean on the MSC global forecast system. Part ii: Sensitivity experiments

A series of observing system experiments (OSEs) for the two‐month period January–February 2007 was carried out to assess the impact of radiosonde and aircraft data available over North America, as well as the impact of satellite data available over the North Pacific Ocean, on the global data assimilation and forecast systems of the Meteorological Service of Canada. This paper presents the second part of the study and examines the validity of the conclusions drawn with respect to the assimilation scheme used (i.e., the three‐ or four‐dimensional variational (3D‐Var or 4D‐Var) data assimilation scheme) and to the horizontal resolution of the forecast model selected. The effect of the weather regime that prevailed during the evaluation period is also investigated. When radiosonde data are denied over North America, as well as over the globe, the forecast impact over the Canadian Arctic is larger using the 3D‐Var scheme than the 4D‐Var scheme. This indicates that the 4D‐Var scheme is more effective at extracting information from other types of observations from nearby regions. For short‐range forecasts, the results are closer to each other when changing the horizontal resolution of the forecast model than they are when changing the assimilation scheme. In order to assess the effect of the weather regime, the individual results for January are compared with those for February. This is possible because the large‐scale circulations during these two one‐month periods are significantly different. It is found that the large‐scale flow has a significant effect on the propagation of the impacts of data denial, leading to noticeable variations of their magnitudes over areas located downstream.

Impacts of Satellite-Observed Winds and Total Precipitable Water on WRF Short-Range Forecasts over the Indian Region during the 2006 Summer Monsoon

Abstract Assimilation experiments have been performed with the Weather Research and Forecasting (WRF) model’s three-dimensional variational data assimilation (3DVAR) scheme to assess the impacts of NASA’s Quick Scatterometer (QuikSCAT) near-surface winds, and Special Sensor Microwave Imager (SSM/I) wind speed and total precipitable water (TPW) on the analysis and on short-range forecasts over the Indian region. The control (without satellite data) as well as WRF 3DVAR sensitivity runs (which assimilated satellite data) were made for 48 h starting daily at 0000 UTC during July 2006. The impacts of assimilating the different satellite dataset were measured in comparison to the control run, which does not assimilate any satellite data. The spatial distribution of the forecast impacts (FIs) for wind, temperature, and humidity from 1-month assimilation experiments for July 2006 demonstrated that on an average, for 24- and 48-h forecasts, the satellite data provided useful information. Among the experiments, WRF wind speed prediction was improved by QuikSCAT surface wind and SSM/I TPW assimilation, while temperature and humidity prediction was improved due to the assimilation of SSM/I TPW. The rainfall prediction has also been improved significantly due to the assimilation of SSM/I TPW, with the largest improvement seen over the west coast of India. Through an improvement of the surface wind field, the QuikSCAT data also yielded a positive impact on the precipitation, particularly for day 1 forecasts. In contrast, the assimilation of SSM/I wind speed degraded the humidity and rainfall predictions.

The impact of targeted observations made during the Greenland Flow Distortion Experiment

AbstractThe impact of targeted sonde observations on the 1‐3 day forecasts for northern Europe is evaluated using the Met Office four‐dimensional variational data assimilation scheme and a 24 km gridlength limited‐area version of the Unified Model (MetUM). The targeted observations were carried out during February and March 2007 as part of the Greenland Flow Distortion Experiment, using a research aircraft based in Iceland. Sensitive area predictions using either total energy singular vectors or an ensemble transform Kalman filter were used to predict where additional observations should be made to reduce errors in the initial conditions of forecasts for northern Europe. Targeted sonde data was assimilated operationally into the MetUM. Hindcasts show that the impact of the sondes was mixed. Only two out of the five cases showed clear forecast improvement; the maximum forecast improvement seen over the verifying region was approximately 5% of the forecast error 24 hours into the forecast. These two cases are presented in more detail: in the first the improvement propagates into the verification region with a developing polar low; and in the second the improvement is associated with an upper‐level trough. The impact of cycling targeted data in the background of the forecast (including the memory of previous targeted observations) is investigated. This is shown to cause a greater forecast impact, but does not necessarily lead to a greater forecast improvement. Finally, the robustness of the results is assessed using a small ensemble of forecasts. Copyright © 2009 Royal Meteorological Society and Crown Copyright

Intercomparison of the performance of MM5/WRF with and without satellite data assimilation in short-range forecast applications over the Indian region

The present study is conducted to verify the short-range forecasts from mesoscale model version5 (MM5)/weather research and forecasting (WRF) model over the Indian region and to examine the impact of assimilation of quick scatterometer (QSCAT) near surface winds, spectral sensor microwave imager (SSM/I) wind speed and total precipitable water (TPW) on the forecasts by these models using their three-dimensional variational (3D-Var) data assimilation scheme for a 1-month period during July 2006. The control (without satellite data assimilation) as well as 3D-Var sensitivity experiments (with assimilating satellite data) using MM5/WRF were made for 48 h starting daily at 0000 UTC July 2006. The control run is analyzed for the intercomparison of MM5/WRF short-range forecasts and is also used as a baseline for assessing the MM5/WRF 3D-Var satellite data sensitivity experiments. As compared to the observation, the MM5 (WRF) control simulations strengthened (weakened) the cross equatorial flow over southern Arabian sea near peninsular India. The forecasts from MM5 and WRF showed a warm and moist bias at lower and upper levels with a cold bias at the middle level, which shows that the convective schemes of these models may be too active during the simulation. The forecast errors in predicted wind, temperature and humidity at different levels are lesser in WRF as compared to MM5, except the temperature prediction at lower level. The rainfall pattern and prediction skill from day 1 and day 2 forecasts by WRF is superior to MM5. The spatial distribution of forecast impact for wind, temperature, and humidity from 1-month assimilation experiments during July 2006 demonstrated that on average, for 24 and 48-h forecasts, the satellite data improved the MM5/WRF initial condition, so that model errors in predicted meteorological fields got reduced. Among the experiments, MM5/WRF wind speed prediction is most benefited from QSCAT surface wind and SSM/I TPW assimilation while temperature and humidity prediction is mostly improved due to latter. The largest improvement in MM5/WRF rainfall prediction is due to the assimilation of SSM/I TPW. The assimilation of SSM/I wind speed alone in MM5/WRF degraded the humidity and rainfall prediction. In summary the assimilation of satellite data showed similar impact on MM5/WRF prediction; largest improvement due to SSM/I TPW and degradation due to SSM/I wind speed.

A Comparison of the Impact of QuikScat and WindSat Wind Vector Products on Met Office Analyses and Forecasts

Several studies have demonstrated that retrievals of wind vectors from the WindSat polarimetric radiometer are of sufficient quality to be considered for assimilation in operational numerical weather prediction models. In this paper, WindSat data are used in a state-of-the-art global meteorological analysis and forecasting system. Each wind vector contains a directional ambiguity and so is assimilated in a similar way to that of scatterometer data. The forecast impact of using analyses containing information from WindSat data was investigated for a period during August and September of 2005, when a large number of tropical cyclones were present. Forecast errors were reduced in the surface pressure fields, and the average improvement across the forecast range was found to be 1.0%. This is comparable to the improvement of 1.1% found in the same fields when winds were assimilated from the QuikScat scatterometer. The impact on tropical cyclone tracks in the forecasts was also studied. The scatterometer improved (reduced) the track errors markedly by 25% in the analyses. When impacts across the forecast range out to five days were also included, the improvement was found to be 8%. In contrast, the assimilation of WindSat data improved the analysis track errors by 7%, although this figure was found to be 10% across the complete forecast range.

Assimilation of rain-affected radiances with an adjoint model

The ability to assimilate microwave radiance observations of the Earth's atmosphere affected by precipitation is investigated with an emphasis on channels that are sensitive to frozen hydrometeors. Mesoscale numerical weather prediction and radiative transfer models, as well as their corresponding adjoint models are utilized in sensitivity and data assimilation experiments. Special Sensor Microwave / Imager observations of Hurricane Bonnie (1998) are compared with model results that are transferred to radiance space with the radiative transfer model. Sensitivity results indicate that the model error in radiance space in areas of precipitation at the initial time is most dependent on the initial hydrometeor fields. At later forecast times, the model error is more sensitive to initial conventional model variables such as water vapor and temperature. When radiance data is assimilated, the model fields have better agreement with the observations compared to a control experiment for all observed channels at the initial time. However, at the next observation time 12 h later, the quantitative error measurements for the control and post- assimilation forecasts are approximately the same value. Although this study demonstrates the ability to assimilate observations sensitive to atmospheric ice (as well as liquid) concentrations in a variational framework, important aspects such a background error correlation and bias have been ignored for simplification. More observations in a more complex data assimilation system will be needed in order to fully maximize the forecast impact of these observations.

Assimilation of IASI at the Met Office and assessment of its impact through observing system experiments

AbstractObservations from the Infrared Atmospheric Sounding Interferometer (IASI), onboard EUMETSAT's MetOp satellite, have been assimilated at the Met Office in global and regional numerical weather‐prediction systems since 27 November 2007. Pre‐operational trials of IASI assimilation in the global model during the summer of 2007 delivered a positive impact on forecasts approximately twice as large as that shown by the Atmospheric InfraRed Sounder (AIRS) on the EOS‐Aqua satellite. A series of observing system experiments confirmed the relative performance of IASI and AIRS, and showed that impact from IASI is equivalent to a single Advanced Microwave Sounding Unit‐A (AMSU‐A) combined with a single Microwave Humidity Sounder (MHS). The results of an IASI assimilation trial for the winter of 2007 were consistent with those of the summer trial, although the impact was slightly lower overall. The assessment of impact is strongly dependent on the variables and methods chosen for verification: assimilation trials with the regional model showed similar improvements to the large‐scale fields (e.g. mean‐sea‐level pressure and geopotential height) as seen in the global model, but no forecast impact was seen for variables such as visibility and rain‐rate. © Crown Copyright 2009. Reproduced with the permission of the Controller of HMSO. Published by John Wiley & Sons, Ltd.

Preliminary assessment of the scalability of GPS radio occultations impact in numerical weather prediction

This work evaluates the scalability of the impact of the GPS radio occultation (GPSRO) measurements on the reduction of Numerical Weather Prediction forecast errors. We first show that the GPSRO bending angles from the eight satellites FORMOSAT‐3/COSMIC, CHAMP, and GRACE‐A lead to a reduction in geopotential height and temperature forecast Root Mean Square errors in Météo‐France global data assimilation system of a few percents in the Northern troposphere and larger in the Southern troposphere. As we double the number of GPSRO data assimilated, the forecast mean square error reduction grows by a factor between 1.1–1.8 for humidity and 1.3–2.4 for geopotential height, temperature, and wind. We do not observe any evidence of forecast impact saturation, though hints of leveling are apparent.

A Two-Season Impact Study of NOAA Polar-Orbiting Satellites in the NCEP Global Data Assimilation System

Abstract Observing system experiments (OSEs) during two seasons are used to quantify the important contributions made to forecast quality from the use of the National Oceanic and Atmospheric Administration’s (NOAA) polar-orbiting satellites. The impact is measured by comparing the analysis and forecast results from an assimilation–forecast system using one NOAA polar-orbiting satellite with results from using two and three polar-orbiting satellites in complementary orbits. The assimilation–forecast system used for these experiments is the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System–Global Forecast System (GDAS–GFS). The case studies chosen consist of periods during January–February and August–September 2003. Differences between the forecasts are accumulated over the two seasons and are analyzed to demonstrate the impact of these satellites. Anomaly correlations (ACs) and geographical forecasts (FIs) are evaluated for all experimental runs during both seasons. The anomaly correlations are generated using the standard NCEP verification software suite and cover the polar regions (60°–90°) and midlatitudes (20°–80°) of each hemisphere. The rms error for 850- and 200-hPa wind vector differences are shown for the tropical region (20°N–20°S). The geographical distribution of forecast impact on geopotential heights, relative humidity, precipitable water, and the u component of wind are also examined. The results demonstrate that the successive addition of each NOAA polar-orbiting satellite increases forecast quality. The use of three NOAA polar-orbiting satellites generally provides the largest improvement to the anomaly correlation scores in the polar and midlatitude regions. Improvements to the anomaly correlation scores are also realized from the use of two NOAA polar-orbiting satellites over only one. The forecast improvements from two satellites are generally smaller than if using three satellites, consistent with the increase in areal coverage obtained with the third satellite.

Assimilation of GPS Radio Occultation Refractivity Data from CHAMP and SAC-C Missions over High Southern Latitudes with MM5 4DVAR

Abstract In this study, the GPS radio occultation (RO) data from the Challenging Minisatellite Payload (CHAMP) and Satellite de Aplicaciones Cientificas-C (SAC-C) missions are assimilated. An updated version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) four-dimensional variational data assimilation system (4DVAR) is used to assess the impact of the GPS RO data on analyses and short-range forecasts over the Antarctic. The study was performed during the period of intense cyclonic activity in the Ross Sea, 9–19 December 2001. On average 66 GPS RO soundings were assimilated daily. For the assimilation over a single 12-h period, the impact of GPS RO data was only marginally positive or near neutral, and it varied markedly from one 12-h period to another. The large case-to-case variation was attributed to the low number of GPS RO soundings and a strong dependency of forecast impact on the location of the soundings relative to the rapidly developing cyclone. Despite the moderate general impact, noticeable reduction of temperature error in the upper troposphere and lower stratosphere was found, which demonstrates the value of GPS RO data in better characterizing the tropopause. Significant error reduction was also noted in geopotential height and wind fields in the stratosphere. Those improvements indicate that early detection of the upper-level precursors for storm development is a potential benefit of GPS RO data. When the assimilation period was extended to 48 h, a considerable positive impact of GPS RO data was found. All parameters that were investigated (i.e., temperature, pressure, and specific humidity) showed the positive impact throughout the entire model atmosphere for forecasts extending up to 5 days. The impact increased in proportion to the length of the assimilation period. Although the differences in the analyses as a result of GPS RO assimilation were relatively small initially, the subtle change and subsequent nonlinear growth led to noticeable forecast improvements at longer ranges. Consequently, the positive impact of GPS RO data was more evident in longer-range (e.g., greater than 2 days) forecasts. A correlation coefficient is introduced to quantify the linear relationship between the analysis errors without GPS RO assimilation and the analysis increments induced by GPS RO assimilation. This measure shows that the growth of GPS RO–induced modifications over time is related to the prominent error reduction observed in GPS RO experiments. The measure may also be useful for understanding how cycling analysis accumulates the positive impact of GPS RO data for an extended period of assimilation.

Forecast impact experiment with a constellation of GPS radio occultation receivers

AbstractA forecast impact experiment with a constellation of global positioning system radio occultation (GPSRO) receivers is presented. The constellation includes the six COSMIC/FORMO‐ SAT‐3 satellites, combined with the GRACE‐A and CHAMP satellites. It is shown that rising and setting occultations measured with the COSMIC satellites have similar bending angle error characteristics. The GPSRO measurements improve the root mean square (rms) geopotential height forecast errors at 500, 200, and 100 hPa in both the Northern and Southern Hemispheres. The forecast fit to radiosonde temperature measurements in the Stratosphere is also improved. Copyright © 2008 Royal Meteorological Society