Geopotential Height Forecasts Research Articles

Application of a Two-Step Space–Time EOF Statistical Postprocessing Algorithm to Mitigate Subseasonal 200-hPa Geopotential Height Forecast Error

Abstract The time-extended empirical orthogonal function (EEOF) patterns of error developed in a companion paper are applied in this study for model data postprocessing. Projecting forecast anomalies from the model seasonal cycle from GEFSv12 outputs onto these EEOF patterns reconstructs the error principal components (PCs) in a manner that can be applied in real time. When these reconstructed error anomalies are compared with the original GEFSv12 200-hPa geopotential height (Z200) reforecast error anomalies, the projected and validation error signals tend to align temporally. When distributed globally, projection and validation error anomalies have statistically significant positive Pearson correlation coefficients with each other at most grid points outside of the tropics. Categorical Heidke skill score (HSS) analysis reaffirms that by lead day 16 of a forecast, the algorithm skillfully predicts systematic errors for locations outside of the tropics. Hemispheric winter demonstrates that the highest magnitude HSS values are in the high latitudes, illustrating the underlying seasonality of Z200 behavior and predictability. On average, wintertime HSS values hover around 20%, with high-latitude locations exceeding mean values of 40%. Comparatively, summertime values generally exceed 10% improvement outside of the tropics. High-latitude storm-track regions, such as the North Atlantic, North Pacific, and South Pacific, contain the most Z200 variance in the globe and the highest HSS values. This algorithm will be applied to real-time forecasts and additional variables in future work, with the end goal of implementation into the NOAA-CPC subseasonal forecasts.

Why Do DJF 2023/24 Upper‐Level 200‐hPa Geopotential Height Forecasts Look Different From the Expected El Niño Response?

AbstractWe investigate why the North American Multi‐Model Ensemble (NMME) upper‐level height forecast for December–February (DJF) 2023/24 differs from the expected El Niño response. These atypical height anomalies emerged despite the fact a strong El Niño was forecast. The analysis focuses on diagnosing the NMME forecasts of DJF 2023/24 for SSTs and 200‐hPa heights initialized at the beginning of November 2023 relative to other ensemble mean NMME DJF forecasts dating back to 1982. The results demonstrate that forecasts of the 200‐hPa height anomalies had a large contribution from warming trends in global SSTs. It is the combination of trends and the expected El Niño teleconnection that results in the forecast height anomalies. Increasingly, for forecasts of geopotential height anomalies during the recent El Niño winters, the amplitude of trends is nearly equal to the signal from El Niño and has implications for the climatological base period selection for seasonal forecasts.

Impact of HY‐2B SMR radiance assimilation on CMA global medium‐range weather forecasts

AbstractThe Chinese second ocean dynamic environment satellite Haiyang‐2B (HY‐2B) was successfully launched on October 25, 2018, carrying a scanning microwave radiometer (SMR) to provide information about the ocean and atmosphere. For the first time, this study investigated the impact of radiance data from the HY‐2B SMR in the four‐dimensional variational (4DVar) data assimilation system of the Global Forecast System developed by the China Meteorology Administration (CMA‐GFS). Prior to the radiance assimilation, we evaluated the data quality and developed suitable quality control (QC) procedures for the HY‐2B SMR observations. In addition, the cloud liquid water path (CLWP) and ocean wind speed (OWS) were introduced as new bias correction (BC) predictors to remove the systematic bias originating from the forward operator in the CMA‐GFS 4DVar system. The assimilation effects of SMR observations were evaluated through one‐month cycling experiments. Verification with independent observations and reanalysis data has demonstrated that assimilating SMR clear‐sky radiance can greatly reduce the analysis errors for temperature and humidity, while indirectly improving the quality of wind analysis. Furthermore, significant improvements in geopotential height forecasts were found for days 1–5 in the lower troposphere of the tropical region.

Clustering Technique Suitable for Eulerian Framework to Generate Multiple Scenarios from Ensemble Forecasts

Abstract In operational weather forecasting, it is effective to aggregate information on all members of an ensemble forecast through cluster analysis. The temporal coherence of ensemble members in each cluster is an important piece of information about the robustness of the forecast scenario given by clusters. This information is especially important for forecasts for which the target area is a city or prefecture, that is, an Eulerian framework, because the members that compose each cluster can change over time because of the small size of the target area. This study provided the temporal coherence of members in clusters by performing principal component analysis and cluster analysis on 3-hourly 500-hPa geopotential height forecasts and linking the clustering results in the time direction. The new method provided a consistently well-divided forecast scenario throughout the forecast period for Eulerian frame forecasts, as well as information on the temporal coherency of the members in the clusters, which was demonstrated to be effective through the experiment to preselect a cluster with small errors. The application of the new technique to improve precipitation forecasts was also discussed. Significance Statement Numerical weather forecasts always contain errors. Although the uncertainty of such forecasts cannot be obtained from the forecast itself, ensemble forecasts, which are aggregates of many forecasts, can be used to estimate the uncertainty of the forecast. In this study, a new method was developed to transfer the information contained in many ensemble forecasts into four forecasts by cluster analysis and to provide forecast information suitable for a small forecasting area such as a prefecture. The use of this method for improving precipitation forecasts was also examined.

The Use of Ensemble Clustering on a Multimodel Ensemble for Medium-Range Forecasting at the Weather Prediction Center

Abstract This article introduces an ensemble clustering tool developed at the Weather Prediction Center (WPC) to assist forecasters in the preparation of medium-range (3–7 day) forecasts. Effectively incorporating ensemble data into an operational forecasting process, like that used at WPC, can be challenging given time constraints and data infrastructure limitations. Often forecasters do not have time to view the large number of constituent members of an ensemble forecast, so they settle for viewing the ensemble’s mean and spread. This ignores the useful information about forecast uncertainty and the range of possible forecast outcomes that an ensemble forecast can provide. Ensemble clustering could be a solution to this problem as it can reduce a large ensemble forecast down to the most prevalent forecast scenarios. Forecasters can then quickly view these ensemble clusters to better understand and communicate forecast uncertainty and the range of possible forecast outcomes. The ensemble clustering tool developed at WPC is a variation of fuzzy clustering where operationally available ensemble members with similar 500-hPa geopotential height forecasts are grouped into four clusters. A representative case from 15 February 2021 is presented to demonstrate the clustering methodology and the overall utility of this new ensemble clustering tool. Cumulative verification statistics show that one of the four forecast scenarios identified by this ensemble clustering tool routinely outperforms all the available ensemble mean and deterministic forecasts. Significance Statement Ensemble forecasts could be used more effectively in medium-range (3–7 day) forecasting. Currently, the onus is put on forecasters to view and synthesize all of the data contained in an ensemble forecast. This is a task they often do not have time to adequately execute. This work proposes a solution to this problem. An automated tool was developed that would split the available ensemble members into four groups of broadly similar members. These groups were presented to forecasters as four potential forecast outcomes. Forecasters felt this tool helped them to better incorporate ensemble forecasts into their forecast process. Verification shows that presenting ensemble forecasts in this manner is an improvement on currently used ensemble forecast visualization techniques.

Contribution of lagged members to the performance of a global ensemble prediction system

National Centre for Medium Range Weather Forecasting (NCMRWF), India runs operationally a global ensemble prediction system (NEPS-G) which has 23 ensemble members. The control and 11 perturbed ensemble members have the initial conditions of 00 UTC. The start time of the other 11 perturbed members is 12 UTC of the previous day. The objective of the present paper is to study the effect of the lagged ensemble members on the forecast quality of NEPS-G. For this, we have carried out an inter-comparison of the relative skills of (i) 11-member ensemble starting from 00 UTC i.e., E00_11, (ii) 11 lagged ensemble members starting from 12 UTC i.e., E12_11 and the (iii) operational 23-member ensemble i.e. E23 using the verification of temperature, wind and geopotential height forecasts for 7-day forecast lead times for April–May-June 2019 (AMJ) of summer and December 2019–January 2020 (DJ) of winter. The temperature at 850 hPa and geopotential height at 500 hPa are verified over the northern hemisphere. The zonal and meridional winds at 850 hPa and 200 hPa are verified over the region 0oN-40oN, 60°E-110°E. The metrics used for validation are ensemble-spread and root-mean-square error (RMSE) relationship, brier skill score (BSS), reliability diagram, outliers statistics, relative operating characteristics (ROC) score and ranked probability score (RPS). For brevity, only the results for Z500, T850 and U850 have been discussed in detail here.The results show that in both seasons, the RMSE-spread relationship is better for E23 than for E00_11 and E12_11. For T850 and Z500 over NH, both E23 and E00_11 have higher RMSE and higher ensemble spread in DJ season as compared to AMJ season. In case of wind over the Indian region, both RMSE and ensemble spread exhibit lower values during DJ as compared to AMJ for both ensemble configurations. For both seasons, the BSS of E23 is positive and the ROC score is >0.5 beyond 7 days forecast times for all the variables for all three ensembles and the percentage of outliers in E23 is also less. Overall, the contribution of 11 lagged ensemble members improves the skill of NEPS-G in both seasons and skill is better during winter (DJ season). The error in the mean forecast track for tropical cyclone ‘Fani’ for E23 is found to be less than for E00_11.We have also compared the forecast skill of E23 with that of the ensemble formed by 22 members, all running from 00 UTC i.e. E00_22 for the same set of variables as well as precipitation for one month forecast data from each season, namely June 2019 from summer and January 2020 from winter. Results show that the RMSE-spread relationship is very much similar for both configurations and both seasons. For E00_22 and E23, the ensemble spread is closer to the RMSE of ensemble mean for all the variables in January compared to June. BSS was slightly higher for E00_22 but was not significant at all lead time for all the variables except Z500 in January which shows significant improvement in BSS of E00_22 from day 2 to day 5. In precipitation forecast verification, RPS for E00_22 is slightly less (though significant) than E23 at all forecast lead times in June while in January there was no significant difference. So, E23 dataset of NEPS-G which demands less computational resources at the same time offers more skilful forecast than E00_11 over a longer forecast lead time and is nearly as skilful as E00_22.

An Intercomparison of Antarctic NWP during the Austral Summer Special Observing Period for the Year of Polar Prediction

Abstract The special observing periods (SOPs) of the Year of Polar Prediction present an opportunity to assess the skill of numerical weather prediction (NWP) models operating over the Antarctic, many of which assimilated additional observations during an SOP to produce some of the most observationally informed model output to date for the Antarctic region and permitting closer examination of model performance under various configurations and parameterizations. This intercomparison evaluates six NWP models spanning global and limited domains, coupled and uncoupled, operating in the Antarctic during the austral summer SOP between 16 November 2018 and 15 February 2019. Model performance varies regionally between each model and parameter; however, the majority of models were found to be warm biased over the continent with respect to ERA5 at analysis, some with biases growing to 3.5 K over land after 48 h. Temperature biases over sea ice were found to be strongly correlated between analysis and 48 h in uncoupled models, but that this correlation can be reduced through coupling to a sea ice model. Surface pressure and 500-hPa geopotential height forecasts and biases were found to be strongly correlated over open ocean in all models, and wind speed forecasts were found to be generally more skillful at higher resolutions with the exception of fast modeled winds over sloping terrain in PolarWRF. Surface sensible and latent heat flux forecasts and biases produced diverse correlations, varying by model, parameter, and gridcell classification. Of the models evaluated, those which couple atmosphere, sea ice, and ocean typically exhibited stronger skill. Significance Statement We evaluated the performance of six numerical weather prediction models operating over the Antarctic during the Year of Polar Prediction austral summer special observing period (16 November 2018–15 February 2019). Our analysis found that several models were as much as 3.5 K warmer than the reference analysis (ERA5) at 48 h over land and were strongly correlated over sea ice in uncoupled models; however, this correlation is reduced through coupling to a sea ice model. Surface pressure biases are communicated to the midtroposphere over the ocean at larger spatial scales, while higher resolution showed an increase in positive wind biases at longer forecasts. Surface turbulent heat fluxes produced complex correlations with other forecast parameters, which should be quantified in future studies. Coupled models that included an ocean/sea ice component typically performed better; providing evidence that the inclusion of such components leads to improved model performance, even at short time scales such as these.

Multiple Hydrometeors All-Sky Microwave Radiance Assimilation in FV3GFS

Abstract Motivated by the use of the GFDL microphysics scheme in the Finite-Volume Cubed-Sphere Dynamical Core Global Forecast System (FV3GFS), the all-sky radiance assimilation framework has been expanded to include precipitating hydrometeors. Adding precipitating hydrometeors allows the assimilation of precipitation-affected radiance in addition to cloudy radiance. In this upgraded all-sky framework, the five hydrometeors, including cloud liquid water, cloud ice, rain, snow, and graupel, are the new control variables, replacing the original cloud water control variable. The Community Radiative Transfer Model (CRTM) was interfaced with the newly added precipitating hydrometeors. Subgrid cloud variability was considered by using the average cloud overlap scheme. Multiple scattering radiative transfer was activated in the upgraded framework. Radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Advanced Technology Microwave Sounder (ATMS) over ocean were assimilated in all-sky approach. This new constructed all-sky framework shows neutral to positive impact on overall forecast skill. Improvement was found in 500-hPa geopotential height forecast in both Northern and Southern Hemispheres. Temperature forecast was also improved at 850 hPa in the Southern Hemisphere and the tropics.

A New Measure of Ensemble Central Tendency

Abstract Ensemble prediction is a widely used tool in weather forecasting. In particular, the arithmetic mean (AM) of ensemble members is used to filter out unpredictable features from a forecast. AM is a pointwise statistical concept, providing the best sample-based estimate of the expected value of any single variable. The atmosphere, however, is a multivariate system with spatially coherent features characterized with strong correlations. Disregarding such correlations, the AM of an ensemble of forecasts removes not only unpredictable noise but also flattens features whose presence is still predictable, albeit with somewhat uncertain location. As a consequence, AM destroys the structure, and reduces the amplitude and variability associated with partially predictable features. Here we explore the use of an alternative concept of central tendency for the estimation of the expected feature (instead of single values) in atmospheric systems. Features that are coherent across ensemble members are first collocated to their mean position, before the AM of the aligned members is taken. Unlike earlier definitions based on complex variational minimization (field coalescence of Ravela and generalized ensemble mean of Purser), the proposed feature-oriented mean (FM) uses simple and computationally efficient vector operations. Though FM is still not a dynamically realizable state, a preliminary evaluation of ensemble geopotential height forecasts indicates that it retains more variance than AM, without a noticeable drop in skill. Beyond ensemble forecasting, possible future applications include a wide array of climate studies where the collocation of larger-scale features of interest may yield enhanced compositing results.

Extratropical Prediction Skill of the Subseasonal‐to‐Seasonal (S2S) Prediction Models

AbstractThe deterministic prediction skill of the 10 operational models participating in the subseasonal‐to‐seasonal (S2S) prediction project is assessed for both the extratropical stratosphere and troposphere. Based on the mean squared skill score of 50‐ and 500‐hPa geopotential height forecasts, the overall prediction skill is on average 16 days in the stratosphere and 9 days in the troposphere. The high‐top models with a fully resolved stratosphere typically have a higher prediction skill than the low‐top models. Among them, the European Centre for Medium‐Range Weather Forecasts model shows the best performance in both hemispheres. The decomposition of model errors reveals that eddy errors are more important than zonal‐mean errors in both the stratosphere and troposphere. While the errors in the stratosphere are dominated by planetary‐scale eddies, those in the troposphere are equally influenced by planetary‐ and synoptic‐scale eddies. This result indicates that subseasonal‐to‐seasonal prediction could be improved by better representing planetary‐scale wave activities in the model.

All‐sky satellite data assimilation of microwave temperature sounding channels at the Met Office

One of the main avenues to improve the skill of a numerical weather prediction system is to increase the accuracy of the initial conditions for prediction through the assimilation of a larger number of observations. This article discusses our work on assimilating cloud‐affected radiances from microwave temperature sounding channels of the Advanced Microwave Sounding Unit A (AMSU‐A) satellite sensor, which are currently underused by the Met Office operational data assimilation system. Despite their importance, as measured by impact indicators such as the Forecast Sensitivity to Observations Impact (FSOI), most cloud‐affected microwave data are discarded so as to avoid detrimental effects, as they are much more difficult to predict than clear‐sky radiances. This is due to shortcomings in parametrization schemes (for example, large‐scale cloud and precipitation, convection) in both nonlinear and linearized prognostic models, as well as radiative transfer models. As first demonstrated at the European Centre for Medium‐Range Weather Forecasts (ECMWF), these difficulties can be eased when observation uncertainties for cloud‐affected radiances are inflated prior to assimilation as a function of cloud amount present in a given observed or predicted scene. Our results show that cloud‐dependent observation uncertainty inflation for cloud‐affected radiances provides beneficial effects on forecast skill over two three‐month‐long “all‐sky” (nonprecipitating) data assimilation trials. In particular, root‐mean‐square error (RMSE) reductions in 500‐hPa geopotential height forecasts of about 1% up to day 2 and in 10‐m wind and 2‐m temperature forecasts up to day 6 have been demonstrated. Also, our experiments show evidence of significant improvements in the fit between observations and short‐range forecasts for lower‐peaking humidity‐ and temperature‐sensitive channels.

Effect of Assimilating Himawari-8 Atmospheric Motion Vectors on Forecast Errors over East Asia

AbstractIn this study, the effect of assimilating Himawari-8 (HIMA-8) atmospheric motion vectors (AMVs) on forecast errors in East Asia is evaluated using observation system experiments based on the Weather Research and Forecasting Model and three-dimensional variational data assimilation system. The experimental period is from 1 August to 30 September 2015, during which both HIMA-8 and Multifunctional Transport Satellite-2 (MTSAT-2) AMVs exist. The energy-norm forecast error based on the analysis of each experiment as reference was reduced more by replacing MTSAT-2 AMVs with HIMA-8 AMVs than by adding HIMA-8 AMVs to the MTSAT-2 AMVs. When the HIMA-8 AMVs replaced or were added to MTSAT-2 AMVs, the observation impact was reduced, which implies the analysis–forecast system was improved by assimilating HIMA-8 AMVs. The root-mean-square error (RMSE) of the 500-hPa geopotential height forecasts based on the analysis of each experiment decreases more effectively when the region lacking in upper-air wind observations is reduced by assimilating both MTSAT-2 and HIMA-8 AMVs. When the upper-air radiosonde (SOUND) observations are used as reference, assimilating more HIMA-8 AMVs decreases the forecast error. Based on various measures, the assimilation of HIMA-8 AMVs has a positive effect on the reduction of forecast errors. The effects on the energy-norm forecast error and the RMSE based on SOUND observations are greater when HIMA-8 AMVs replaced MTSAT-2 AMVs. However, the effects on the RMSE of the 500-hPa geopotential height forecasts are greater when both HIMA-8 and MTSAT-2 AMVs were assimilated, which implies potential benefits of assimilating AMVs from several satellites for forecasts over East Asia depending on the choice of measurement.

The Advantages of Hybrid 4DEnVar in the Context of the Forecast Sensitivity to Initial Conditions

AbstractHybrid four‐dimensional ensemble variational data assimilation (hybrid 4DEnVar) is a prospective successor to three‐dimensional variational data assimilation (3DVar) in operational weather prediction centers currently developing a new weather prediction model and those that do not operate adjoint models. In experiments using real observations, hybrid 4DEnVar improved Northern Hemisphere (NH; 20°N–90°N) 500 hPa geopotential height forecasts up to 5 days in a NH summer month compared to 3DVar, with statistical significance. This result is verified against ERA‐Interim through a Monte Carlo test. By a regression analysis, the sensitivity of 5 day forecast is associated with the quality of the initial condition. The increased analysis skill for midtropospheric midlatitude temperature and subtropical moisture has the most apparent effect on forecast skill in the NH including a typhoon prediction case. Through attributing the analysis improvements by hybrid 4DEnVar separately to the ensemble background error covariance (BEC), its four‐dimensional (4‐D) extension, and climatological BEC, it is revealed that the ensemble BEC contributes to the subtropical moisture analysis, whereas the 4‐D extension does to the midtropospheric midlatitude temperature. This result implies that hourly wind‐mass correlation in 6 h analysis window is required to extract the potential of hybrid 4DEnVar for the midlatitude temperature analysis to the maximum. However, the temporal ensemble correlation, in hourly time scale, between moisture and another variable is invalid so that it could not work for improving the hybrid 4DEnVar analysis.

Development and testing of the GRAPES regional ensemble-3DVAR hybrid data assimilation system

Based on the GRAPES (Global/Regional Assimilation and Prediction System) regional ensemble prediction system and 3DVAR (three-dimensional variational) data assimilation system, which are implemented operationally at the Numerical Weather Prediction Center of the China Meteorological Administration, an ensemble-based 3DVAR (En-3DVAR) hybrid data assimilation system for GRAPES_Meso (the regional mesoscale numerical prediction system of GRAPES) was developed by using the extended control variable technique to implement a hybrid background error covariance that combines the climatological covariance and ensemble-estimated covariance. Considering the problems of the ensemble-based data assimilation part of the system, including the reduction in the degree of geostrophic balance between variables, and the non-smooth analysis increment and its obviously smaller size compared with the 3DVAR data assimilation, corresponding measures were taken to optimize and ameliorate the system. Accordingly, a single pressure observation ensemble-based data assimilation experiment was conducted to ensure that the ensemble-based data assimilation part of the system is correct and reasonable. A number of localization-scale sensitivity tests of the ensemble-based data assimilation were also conducted to determine the most appropriate localization scale. Then, a number of hybrid data assimilation experiments were carried out. The results showed that it was most appropriate to set the weight factor of the ensemble-estimated covariance in the experiments to be 0.8. Compared with the 3DVAR data assimilation, the geopotential height forecast of the hybrid data assimilation experiments improved very little, but the wind forecast improved slightly at each forecast time, especially over 300 hPa. Overall, the hybrid data assimilation demonstrates some advantages over the 3DVAR data assimilation.

Effects of all‐sky assimilation of GCOM‐W/AMSR2 radiances in the ECMWF numerical weather prediction system

This article assesses the impact of Advanced Microwave Scanning Radiometer 2 (AMSR2) radiances in the all‐sky assimilation of the European Centre for Medium‐Range Weather Forecasts (ECMWF). Individual impacts of three microwave imagers including AMSR2 were examined by using a baseline experiment that had no microwave imager data. The three microwave imagers brought similar improvements in humidity, temperature and wind first‐guess (FG) fields in the troposphere. Improvements were found in fits to both analyses and other observations. Moreover, significant improvements in wind and geopotential height fields in the troposphere were found in day 3–6 forecasts. AMSR2 made larger improvements than other microwave imagers to geopotential height forecasts in the Northern Hemisphere. The use of AMSR2 radiance data in addition to the other existing microwave imagers gave mixed results. Consistent improvements in the FG fit to humidity observations were confirmed. However, the FG fit for several channels of the microwave temperature sounding instruments was degraded. The mean FG departure of AMSR2 showed biases that varied according to the time of day and meteorological conditions. The causes of the biases were identified as insufficient representation of the cloud liquid water path (LWP) in the forecast model under unstable conditions and insufficient amplitude of LWP diurnal variation in stratocumulus areas in the Tropics. The assimilation of too much biased data might start to bring negative effects for the analyses and forecasts, which for some parameters could outweigh the improvements in the assimilation. However, despite this, AMSR2 brought significant improvements of the geopotential height field in the Southern Hemisphere lower troposphere for day 5–7 forecasts.

Impact of Different Satellite Wind Lidar Telescope Configurations on NCEP GFS Forecast Skill in Observing System Simulation Experiments

AbstractThe Global Wind Observing Sounder (GWOS) concept, which has been developed as a hypothetical space-based hybrid wind lidar system by NASA in response to the 2007 National Research Council (NRC) decadal survey, is expected to provide global wind profile observations with high vertical resolution, precision, and accuracy when realized. The assimilation of Doppler wind lidar (DWL) observations anticipated from the GWOS is being conducted as a series of observing system simulation experiments (OSSEs) at the Joint Center for Satellite Data Assimilation (JCSDA). A companion paper (Riishøjgaard et al.) describes the simulation of this lidar wind data and evaluates the impact on global numerical weather prediction (NWP) of the baseline GWOS using a four-telescope configuration to provide independent line-of-sight wind speeds, while this paper sets out to assess the NWP impact of GWOS equipped with alternative paired configurations of telescopes. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and the Global Forecast System (GFS) were used, at a resolution of T382 with 64 layers, as the assimilation system and the forecast model, respectively, in these lidar OSSEs. A set of 45-day assimilation and forecast experiments from 2 July to 15 August 2005 was set up and executed.In this OSSE study, a control simulation utilizing all of the data types assimilated in the operational GSI/GFS system was compared to three OSSE simulations that added lidar wind data from the different configurations of telescopes (one-, two-, and four-look configurations). First, the root-mean-square error (RMSE) of wind analysis is compared against the nature run. A significant reduction of the stratospheric RMSE of wind analyses is found for all latitudes when lidar wind profiles are used in the assimilation system. The forecast impacts of lidar data on the wind and mass forecasts are also presented. In addition, the anomaly correlations (AC) of geopotential height forecasts at 500 hPa were evaluated to compare the control and different GWOS telescope configuration experiments. The results show that the assimilation of lidar data from the GWOS (one, two, or four looks) can improve the NCEP GFS wind and mass field forecasts. The addition of the simulated lidar wind observations leads to a statistically significant increase in AC scores.

Impact of Infrared, Microwave, and Radio Occultation Satellite Observations on Operational Numerical Weather Prediction

Abstract A comparison of the impact of infrared (IR), microwave (MW), and radio occultation (RO) observations on NCEP’s operational global forecast model over the month of March 2013 is presented. Analyses and forecasts with only IR, MW, and RO observations are compared with analyses and forecasts with no satellite data and with each other. Overall, the patterns of the impact of the different satellite systems are similar, with the MW observations producing the largest impact on the analyses and RO producing the smallest. Without RO observations, satellite radiances are over– or under–bias corrected and RO acts as an anchor observation, reducing the forecast biases globally. Positive correlation coefficients of temperature impacts are generally found between the different satellite observation analyses, indicating that the three satellite systems are affecting the global temperatures in a similar way. However, the correlation in the lower troposphere among all three systems is surprisingly small. Correlations for the moisture field tend to be small in the lower troposphere between the different satellite analyses. The impact of the satellite observations on the 500-hPa geopotential height forecasts is much different in the Northern and Southern Hemispheres. In the Northern Hemisphere, all the satellite observations together make a small positive impact compared to the base (no satellite) forecasts. The IR and MW, but not the RO, make a small positive impact when assimilated alone. The situation is considerably different in the Southern Hemisphere, where all the satellite observations together make a much larger positive impact, and all three observation types (IR, MW, and RO) make similar and significant impacts.

Satellite Radiance Assimilation in the JMA Operational Mesoscale 4DVAR System

Abstract The direct radiance assimilation scheme used in the Japan Meteorological Agency (JMA) global analysis system is applied to the JMA mesoscale four-dimensional variational data assimilation (4DVAR) system with two modifications. First, the data-thinning distance is shortened, and, second, the atmospheric profiles are extrapolated from the mesoscale model top to the radiative transfer model top using the U.S. Standard Atmosphere lapse rate. Although the variational bias correction method is widely used in many numerical weather prediction centers for global radiance assimilations, a radiance bias correction method for regional models has not been established because of difficulties in estimating the biases within limited regions and times. This paper examined the use of the bias correction coefficients estimated in the global system for the mesoscale system when the radiance data were introduced instead of the retrievals. It was found that the profile extrapolation was necessary to reduce the biases. Moreover, the use of common bias coefficients enables the use of the radiance data in the same way as the global system. The radiance data assimilation experiments in the mesoscale system demonstrated considerable improvements to the tropospheric geopotential height forecasts and precipitation forecasts. The improvements resulted from the introduction of radiance data from multiple satellites into data-sparse regions and times. However, the major effect of the radiance assimilation on the precipitation forecasts was limited to weak precipitation areas over oceans; the effects on deep convective areas and over land were relatively small.

A stochastic method for improving seasonal predictions

Ensemble seasonal forecasts during boreal winter suffer from insufficient spread and systematic errors. In this study we suggest a new stochastic dynamics method to address both issues at a time. Our technique relies on random additive corrections of initial tendency error estimates of the atmospheric component of the CNRM‐CM5.1 global climate model, using ERA‐Interim as a reference over a 1979–2010 hindcast period. The random method improves deterministic scores for 500‐hPa geopotential height forecasts over the Northern Hemisphere extratropics (NH Z500), and increases the ensemble spread. An optimal method consisting in drawing the error corrections within the current month of the hindcast period illustrates the high potential of future improvements, with NH Z500 anomaly correlation reaching 0.65 and North Atlantic Oscillation index correlation 0.71 with ERA‐Interim. These substantial improvements using current year corrections pave the way for future forecasting methods using classification criteria on the correction population.

Preliminary comparison of the CMA, ECMWF, NCEP, and JMA ensemble prediction systems

Based on The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) dataset, using various verification methods, the performances of four typical ensemble prediction systems (EPSs) from the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), the US National Centers for Environmental Prediction (NCEP), and the Japan Meteorological Agency (JMA) are compared preliminarily. The verification focuses on the 500-hPa geopotential height forecast fields in the mid- and high-latitude Eurasian region during July 2007 and January 2008. The results show that for the forecast of 500-hPa geopotential height, in both summer and winter, the ECMWF EPS exhibits the highest forecast skill, followed by that of NCEP, then by JMA, and the CMA EPS gets in the last. The better system behaviors benefit from the better combination of the following: data assimilation system, numerical models, initial perturbations, and stochastic model perturbations. For the medium-range forecast, the ensemble forecasting can effectively filter out the forecast errors associated with the initial uncertainty, and the reliability and resolution (the two basic attributions of the forecast system) of these EPSs are better in winter than in summer. Specifically, the CMA EPS has certain advantage on the reliability of ensemble probabilistic forecasts. The forecasts are easy to be underestimated by the JMA EPS. The deficiency of ensemble spread, which is the universal problem of EPS, also turns up in this study. Although the systems of ECMWF, NCEP, and JMA have more ensemble members, this problem cannot be ignored. This preliminary comparison helps to further recognize the prediction capability of the four EPSs over the Eurasian region, provides important references for wide applications of the TIGGE dataset, and supplies useful information for improving the CMA EPS.