ABSTRACT. The variational method for data assimilation as implemented in the operational scheme at ECMWF is briefly presented. The performance of the variational scheme (3D-Var) with respect to tropical cyclones and the Asian summer monsoon is investigated and compared to the Optimum Interpolation scheme. It is found that the analysis of near-surface winds has improved significantly particularly in the vicinity of tropical storms and depressions. The better analyses have led to improvements in the short range forecasts (day 1 to day 3) of such systems. The summer monsoon appears slightly stronger in the 3D-Var analyses, giving enhanced forecast precipitation over the Western Ghats and over large parts of northern India. Only in the latter of these two areas does this verify with observations. The forecasts for India of geopotential, wind and temperature have improved significantly at all forecast ranges, as verified against own analyses. These results are based on 28 cases in two separate 2-week periods.

SINGV‐DA is a convective‐scale numerical weather prediction system with regional data assimilation for Singapore and the surrounding region. This article documents SINGV‐DA's current operational configuration and the sensitivity studies that influenced its development. We show that background error covariances derived by bootstrapping (via the lagged National Meteorological Centre method) contain spurious vertical structures at higher model levels that may degrade forecast performance. We found that SINGV‐DA precipitation forecasts are sensitive to horizontal resolution and lateral boundary conditions. Our observing system experiments reveal that satellite radiance assimilation, while clearly beneficial for precipitation forecasts in this region, adversely affected model background temperatures and winds at higher altitudes. Benchmarked against the forecast model in isolation, the regional DA system adds significant value to precipitation forecasts in the nowcasting range, but not at longer lead times. Our findings point to the need for further research and development to improve the system.

Hourly cycling four-dimensional variational data assimilation (4D-Var) was implemented operationally in the Met Office's convective-scale UKV forecast model in July 2017, replacing the previous three-hourly cycling three-dimensional 3D-Var scheme. The new system was based on a previous Nowcasting Demonstration Project (NDP), developed and run in real time over a southern UK domain for the London 2012 Olympic and Paralympic Games and focusing on precipitation forecasts. The new operational system extends this capability to the full UK (and surrounding) area, delivering outputs suitable for blending into nowcasting products and for general forecasting of a full range of meteorological variables. We describe the general formulation of the Met Office 4D-Var system and some particular ingredients. Differences between the new system and its NDP and UK 3D-Var antecedents are discussed for both the assimilation algorithm and the observational inputs. As an illustration of the impact on forecast performance, we compare the skill of 3D-Var and 4D-Var using an hourly cycle for both configurations. For precipitation skill, we also compare three-hourly 3D-Var and hourly 4D-Var with a reference nowcasting system, in order to highlight the improvement from the new method at very short forecast ranges. Future avenues for developing the system are outlined.

The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project’s achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall events.

Atmospheric motion vectors (AMVs) are wind observations derived by tracking cloud or water‐vapour features in consecutive satellite images. These observations are incorporated into numerical weather prediction (NWP) through data assimilation. In the assimilation algorithm, the weighting given to an observation is determined by the uncertainty associated with its measurement and representation. Previous studies assessing AMV uncertainty have used direct comparisons between AMVs with collocated radiosonde data and AMVs derived from Observing System Simulation Experiments (OSSEs). These have shown that AMV error is horizontally correlated with the characteristic length‐scale up to 200 km. In this work, we take an alternative approach and estimate AMV error variance and horizontal error correlation using background and analysis residuals obtained from the Met Office limited‐area, 3 km horizontal grid‐length data assimilation system. The results show that the observation‐error variance profile ranges from 5.2–14.1 s m 2 s −2 , with the highest values occurring at high and medium heights. This is indicative that the maximum error variance occurs where wind speed and shear, in combination, are largest. With the exception of AMVs derived from the High Resolution Visible channel, the results show horizontal observation‐error correlations at all heights in the atmosphere, with correlation length‐scales ranging between 140 and 200 km. These horizontal length‐scales are significantly larger than current AMV observation‐thinning distances used in the Met Office high‐resolution assimilation.

It has been common practice in data assimilation to treat observation errors as uncorrelated; however, meteorological centres are beginning to use correlated inter-channel observation errors in their operational assimilation systems. In this work, we are the first to characterise inter-channel and spatial error correlations for Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations that are assimilated into the Met Office high-resolution model. The errors are calculated using a diagnostic that calculates statistical averages of observation-minus-background and observation-minus-analysis residuals. This diagnostic is sensitive to the background and observation error statistics used in the assimilation, although, with careful interpretation of the results, it can still provide useful information. We find that the diagnosed SEVIRI error variances are as low as one-tenth of those currently used in the operational system. The water vapour channels have significantly correlated inter-channel errors, as do the surface channels. The surface channels have larger observation error variances and inter-channel correlations in coastal areas of the domain; this is the result of assimilating mixed pixel (land-sea) observations. The horizontal observation error correlations range between 30 km and 80 km, which is larger than the operational thinning distance of 24 km. We also find that estimates from the diagnostics are unaffected by biased observations, provided that the observation-minus-background and observation-minus-analysis residual means are subtracted.

The Convective Precipitation Experiment (COPE) was a joint U.K.-U.S. field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly as a result of the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the United States. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve numerical weather prediction (NWP) model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the U.K. BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360 degrees volume scans over 10 elevation angles approximately every 5 min and was augmented by two Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper i) provides an overview of the COPE field campaign and the resulting dataset, ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone, and iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.

Atmospheric motion vectors (AMVs) have been produced for decades and remain an important source of wind information. Many studies have suggested that the traditional interpretation of AMVs as representative of the wind at cloud top is suboptimal and that they are more representative of the winds within the cloud. This paper investigates the vertical representativity of cloudy AMVs using both first-guess departure [observation - background (O - B)] statistics and the simulation-study technique. A state-of-the-art convection-permitting mesoscale model ("UKV") is used in conjunction with a radiative transfer model and the Nowcasting Satellite Application Facility (NWCSAF) AMV package to produce synthetic AMVs over a 1-month period. The simulated upper-level AMVs suffered from large height-assignment errors uncharacteristic of those in reality; these issues were partially alleviated by using the model cloud top instead of the assigned height. In agreement with previous studies, both the simulated and real AMVs were found to have the closest fit to a layer mean of the model winds with the majority of the layer below the estimated cloud top. However, improvements in the fit between the AMVs and the model were also found by simply lowering the assigned height. A short NWP trial hinted that height reassignment might lead to short-range forecast improvements. The results of this study indicate that the simulation technique was able to match the usefulness of O - B statistics for AMVs associated with low- and medium-level clouds (albeit at a higher computational cost); however, challenges remain in the simulation of upper-level clouds.

The vertical representivity of Atmospheric Motion Vectors (AMVs) has been the subject of much discussion over many years. Several recent studies have suggested that the traditional interpretation of AMVs as representative of the wind at cloud top is sub-optimal and that they are in fact more representative of the winds within the cloud. In this study, the vertical representivity of cloudy AMVs is investigated using both real and simulated AMVs. A state of the art convection permitting mesoscale model is used in conjuction with the RTTOV radiative transfer scheme and the Nowcasting SAF (NWCSAF) AMV package to produce synthetic AMVs over a one month period, which are then compared against the model truth from which they were derived. In agreement with previous studies, the results indicated that AMVs are more representative of a layer mean wind with the majority of the layer below the cloud top. This suggests that the use of a layer averaging observation operator may benefit the assimilation of AMVs into NWP models. Improvements in the fit between the AMVs and the model can also be found by simply lowering the assigned height by around 40hPa. The use of either a lower assigned height or a layer average significantly reduced the slow bias commonly seen in upper level AMVs. The utility of the simulation study data in comparison to traditional O-B statistics was assessed. The results for low and medium height clouds were encouraging with the simulated AMV error characteristics similar to those in real AMVs. However, the simulated upper level AMVs suffered from significantly larger height assignment errors than those in reality making the results unrepresentative.

The assimilation of satellite derived Atmospheric Motion Vectors (AMVs) into numerical weather prediction (NWP) models have provided benefits to operational forecasts for many years. Comparisons of AMVs against sonde observations are routinely used to monitor the accuracy of the AMVs. Simulation studies, whereby AMVs are derived from model cloud fields, provide a useful framework to characterise and understand the sources of errors in AMV wind estimates. The latest generation of high resolution NWP models with grid lengths of order 1km are able to resolve most convective cloud features explicitly. These models have been shown to realistically simulate the life cycle and motion of many cloud features. In this study, the Met Office ‘UKV’ mesoscale model is used to generate synthetic AMVs. Comparisons are presented between the synthetic AMVs and the ‘true’ model winds. The results confirm that the simulated AMVs produced by the system are realistic and provide a good framework for further studies.

Recent monitoring of cloud motion winds (SATOBs) at ECMWF has shown an improvement in quality. Currently, in operations, cloud motion winds are excluded from certain geographical regions and recent numerical experimental results have indicated that the current "blacklist" for cloud motion can be revised. Results for these experiments will be discussed.

The Kalpana Very High Resolution Radiometer (VHRR) water vapour (WV) channel is very similar to the WV channel of the Meteosat Visible and Infrared Radiation Imager (MVIRI) on Meteosat‐7, and both satellites observe the Indian subcontinent. Thus it is possible to compare the performance of VHRR and MVIRI in numerical weather prediction (NWP) models. In order to do so, the impact of Kalpana‐ and Meteosat‐7‐measured WV radiances was evaluated using analyses and forecasts of moisture, temperature, geopotential and winds, using the European Centre for Medium‐range Weather Forecasts (ECMWF) NWP model. Compared with experiments using Meteosat‐7, the experiments using Kalpana WV radiances show a similar fit to all observations and produce very similar forecasts. Copyright © 2011 Royal Meteorological Society and British Crown Copyright, the Met Office

Within a four-dimensional variational (4dvar) assimilation system it is possible to take advantage of the high temporal resolution of geostationary radiance data by assimilation of observations at times other than 00, 06, 12, and 18 Z, thereby providing information about the time evolution of the model fields. In this paper the results of initial experiments in the assimilation of Meteosat Water Vapour channel (WV) radiance data within a 4dvar assimilation system are presented. Comparisons are drawn with the direct assimilation of the Meteosat Clear Sky Water Vapour Wind (WVW) product. An initial investigation into the impact of both WV radiance data and WVW’s on the assimilation and forecast system is made. Future plans leading to the operational assimilation of the Clear Sky WV radiance product within the ECMWF system are outlined.

Enhanced Meteosat wind data sets are provided every 90 minutes together with the Quality Indicator (QI) derived during the quality control of the Meteorological Product Extraction Facility (MPEF) at EUMETSAT. All three channel Cloud Motion Winds (CMW) and clear sky Water Vapour Motion Winds (WVMW) have been passively monitored by comparison to the ECMWF background field. The evaluation of the relationship between the MPEF QI and the background departures indicate possible benefits to be gained from the use of the QI within the observation screening of the assimilation system. The atmospheric motion winds (AMW) with 90 minute time sampling have been implemented into the ECMWF assimilation system. The MPEF quality indicator is used as a selection criterion within the screening. The applied thresholds are restricted in the Tropics compared to the extratropical regions where the threshold for high level winds has been relaxed below the Automatic Quality Control (AQC) at MPEF. The overall effect is an increase of active Meteosat winds by a factor of two.

The variational assimilation method requires the observing process to be modelled for each observation type. Simulated radiances must be calculated from the model background for each satellite radiance observation. This poster compares a model of the SEVIRI Modulation-Transfer-Function (MTF) and PointSpread-Function (PSF) based on pre-launch laboratory measurements with those calculated from images obtained during normal orbital observations. Although simulation of radiative transfer processes in the Earth's atmosphere has been investigated in detail for Met Office variational data assimilation, the simulation of the satellite instrument properties within assimilation schemes at the Met Office has generally been rather simplistic. We investigate here the observed PSF and MTF of the infrared channels of the SEVIRI instrument through comparison with 1km resolution MODIS images and also with the Met Office 1.5km resolution UKVD NWP model.

The measurement uncertainty requirements imposed by numerical weather prediction (NWP) data assimilation applications for temperature sounding radiances are very demanding. For an ensemble of observations collected during an orbit, (postbias correction) measurement uncertainties of similar to 0.2 K (at I or) or better are required in tropospheric sounding channels to improve analyses, and hence forecasts, from current NWP models. A significant fraction of F-16 Special Sensor Microwave Imager/Sounder (SSMIS) observations are affected by calibration errors caused by solar intrusions into the warm calibration load and by thermal emission from the main reflector. The magnitude of these effects is as large as 1.5 K for the lower atmospheric temperature sounding channels. This paper describes the approach to correct for these effects, which involves data averaging, flagging solar intrusions, and modeling reflector emission. The resulting quality of the radiances is improved by a factor of three to four for mid-tropospheric temperature sounding channels. Observation minus background field differences are reduced from 0.5-0.8 K (at one standard deviation) for uncorrected data to 0.2 K for corrected data. Although localized biases remain in the corrected data, assimilation experiments using SSMIS data at four operational NWP centers (Met Office, ECMWF, NCEP, and NRL) show a neutral-to-positive impact on forecast quality in the Southern Hemisphere with, for example, mean sea-level pressure forecast errors at days 1-4 reduced by 0.5%-2.5%. Impacts in the Northern Hemisphere are neutral in most assimilation experiments.

A review of the status of Atmospheric Motion Vectors (AMVs), monitored and assimilated operationally at the European Centre for Medium Range Weather Forecasts (ECMWF), is presented. The period since the last workshop saw a number of satellite replacements. Currently, data from five different geostationary satellites are used in operations (from METEOSAT-9 and -7, GOES-11 and -12, and MTSAT-1R), together with polar AMVs from MODIS on Terra and Aqua. A number of additional AMV datasets are being monitored with a view of eventual operational assimilation, and we report on the results of the monitoring efforts so far. Winds have been derived by CIMSS from the infrared channel of the AVHRR instrument on the NOAA satellites. While the data are relatively sparse, assimilation trials in a system that uses a limited amount of satellite observations document a small positive forecast impact from the AVHRR winds. This also makes the data attractive for reanalyses. In addition, direct-broadcast MODIS winds are now available to improve the timeliness of this important dataset. A greater coverage is obtained for the early-cutoff NWP runs. AMVs from CMA’s FY-2C now include quality indicator information, prompting a re-assessment of the quality of these winds. Lastly, winds from the stereo-viewing MISR instrument on Terra have been compared to the ECMWF first guess, showing broadly similar first guess statistics to other AMVs from geostationary or polar satellites, despite a supposedly much better height assignment.

The Vertical Temperature Profile Radiometer (VTPR) was an operational 8-channel infrared sounding system mounted on the NOAA-2 through NOAA-5 spacecraft. The instrument was a predecessor of the High-Resolution Infrared Radiation Sounder (HIRS) on the continuing NOAA polar orbiting satellite series. The VTPR measurements covered more than six years of data from late 1972 to early 1979. Major work has been done to clear erroneous data records. Theoretical biases between similar channels of VTPR and HIRS are derived using a radiative transfer model to show the potential bias features between the observations of the two instruments. The model simulation shows that for about half of the channels the biases can be in the order of 1 K for certain temperature ranges. Because each spacecraft carried two sets of VTPR instrument, but only one set was turned on at a given time, differences between two sets of channel measurement are expected to exist. It is shown that the differences between two sets of VTPR instrument can range from nearly zero for some channels to about 2 K for other channels. To make the VTPR dataset accessible to the general scientific research community, we have processed the whole VTPR data to common formats and placed the data online along with data quality statistics.