Office Global Numerical Weather Prediction Research Articles

A channel selection for the assimilation of CrIS and HIRAS instruments at full spectral resolution

AbstractA new channel selection is proposed for the processing of observations at full spectral resolution (FSR) from the Cross‐track Infrared Sounder (CrIS) and Hyperspectral Infrared Atmospheric Sounder (HIRAS) instruments in the Met Office global numerical weather prediction (NWP) system. The new selection has been derived in order to minimise the error in NWP analysis and has been compared to an existing selection developed at the National Oceanic and Atmospheric Administration (NOAA). Both selections have been tested in the Met Office global NWP system to investigate the use of FSR CrIS compared with the current normal spectral resolution (NSR) set‐up employed in operations. Improvements are obtained for most forecast variables up to 7‐day lead times, with change in root‐mean‐square error (RMSE) ranging from 0.13 to 0.54%, although degradation of tropical temperatures are noted for the NOAA selection. The background fit to observations from independent sounders improves by up to 0.8% with the new selection, outperforming the NOAA selection which results in degradations across most instruments. The assimilation of HIRAS observations does not however benefit the system.

All-sky Data Assimilation of MWTS-2 and MWHS-2 in the Met Office Global NWP System

Microwave radiances from passive polar-orbiting radiometers have been, until recently, assimilated in the Met Office global numerical weather prediction system after the scenes significantly affected by atmospheric scattering are discarded. Recent system upgrades have seen the introduction of a scattering-permitting observation operator and the development of a variable observation error using both liquid and ice water paths as proxies of scattering-induced bias. Applied to the Fengyun 3 Microwave Temperature Sounder 2 (MWTS-2) and the Microwave Humidity Sounder 2 (MWHS-2), this methodology increases the data usage by up to 8% at 183 GHz. It also allows for the investigation into the assimilation of MWHS-2 118 GHz channels, sensitive to temperature and lower tropospheric humidity, but whose large sensitivity to ice cloud have prevented their use thus far. While the impact on the forecast is mostly neutral with small but significant short-range improvements, 0.3% in terms of root mean square error, for southern winds and low-level temperature, balanced by 0.2% degradations of short-range northern and tropical low-level temperature, benefits are observed in the background fit of independent instruments used in the system. The lower tropospheric temperature sounding Infrared Atmospheric Sounding Interferometer (IASI) channels see a reduction of the standard deviation in the background departure of up to 1.2%. The Advanced Microwave Sounding Unit A (AMSU-A) stratospheric sounding channels improve by up to 0.5% and the Microwave Humidity Sounder (MHS) humidity sounding channels improve by up to 0.4%.

A comparison of hybrid variational data assimilation methods for global NWP

Variational data assimilation methods are reviewed and compared in the Met Office global numerical weather prediction system. This supports hybrid background‐error covariances which are a weighted combination of modelled static “climatological” covariances with covariances calculated from a current ensemble of forecasts, in both three‐ and four‐dimensional methods. For the latter, we compare the use of linear and adjoint models (hybrid‐4DVar) with the direct use of ensemble forecast trajectories (hybrid‐4DEnVar). Earlier studies had shown that hybrid‐4DVar outperforms hybrid‐4DEnVar, and 4DVar outperforms 3DVar. Improvements in the processing of ensemble covariances and computer enhancements mean we are now able to explore these comparisons for the full range of hybrid weights. We find that, using our operational 44‐member ensemble, the static covariance is still beneficial in hybrid‐4DVar, so that it significantly outperforms hybrid‐4DEnVar. In schemes not using linear and adjoint models, the static covariance is less beneficial. It is shown that the time‐propagated static covariance is the main cause of the better performance of 4DVar; when using pure ensemble covariances, 4DVar and 4DEnVar show similar skill. These results are consistent with nonlinear dynamics theory about assimilation in the unstable subspace.

A New Method to Correct Radiosonde Temperature Biases Using Radio Occultation Data

AbstractA new method to estimate radiosonde temperature biases using radio occultation measurements as a reference has been developed. The bias is estimated as the difference between mean radio occultation and mean radiosonde departures from collocated profiles extracted from the Met Office global numerical weather prediction (NWP) system. Using NWP background profiles reduces the impact of spatial and temporal collocation errors. The use of NWP output also permits determination of the lowest level at which the atmosphere is sufficiently dry to analyze radio occultation dry temperature retrievals. The authors demonstrate the advantages of using a new tangent linear version of the dry temperature retrieval algorithm to propagate bending angle departures to dry temperature departures. This reduces the influence of a priori assumptions compared to a nonlinear retrieval. Radiosonde temperature biases, which depend on altitude and the solar elevation angle, are presented for five carefully chosen upper-air sites and show strong intersite differences, with biases exceeding 2 K at one of the sites. If implemented in NWP models to correct radiosonde temperature biases prior to assimilation, this method could aid the need for consistent anchor measurements in the assimilation system. The method presented here is therefore relevant to NWP centers, and the results will be of interest to the radiosonde community by providing site-specific temperature bias profiles. The new tangent linear version of the linear Abel transform and the hydrostatic integration are described in the interests of the radio occultation community.

Impacts of increasing the aerosol complexity in the Met Office global numerical weather prediction model

Abstract. The inclusion of the direct and indirect radiative effects of aerosols in high-resolution global numerical weather prediction (NWP) models is being increasingly recognised as important for the improved accuracy of short-range weather forecasts. In this study the impacts of increasing the aerosol complexity in the global NWP configuration of the Met Office Unified Model (MetUM) are investigated. A hierarchy of aerosol representations are evaluated including three-dimensional monthly mean speciated aerosol climatologies, fully prognostic aerosols modelled using the CLASSIC aerosol scheme and finally, initialised aerosols using assimilated aerosol fields from the GEMS project. The prognostic aerosol schemes are better able to predict the temporal and spatial variation of atmospheric aerosol optical depth, which is particularly important in cases of large sporadic aerosol events such as large dust storms or forest fires. Including the direct effect of aerosols improves model biases in outgoing long-wave radiation over West Africa due to a better representation of dust. However, uncertainties in dust optical properties propagate to its direct effect and the subsequent model response. Inclusion of the indirect aerosol effects improves surface radiation biases at the North Slope of Alaska ARM site due to lower cloud amounts in high-latitude clean-air regions. This leads to improved temperature and height forecasts in this region. Impacts on the global mean model precipitation and large-scale circulation fields were found to be generally small in the short-range forecasts. However, the indirect aerosol effect leads to a strengthening of the low-level monsoon flow over the Arabian Sea and Bay of Bengal and an increase in precipitation over Southeast Asia. Regional impacts on the African Easterly Jet (AEJ) are also presented with the large dust loading in the aerosol climatology enhancing of the heat low over West Africa and weakening the AEJ. This study highlights the importance of including a more realistic treatment of aerosol–cloud interactions in global NWP models and the potential for improved global environmental prediction systems through the incorporation of more complex aerosol schemes.

Comparing Tropical Precipitation Simulated by the Met Office NWP and Climate Models with Satellite Observations

AbstractForecasts of precipitation and water vapor made by the Met Office global numerical weather prediction (NWP) model are evaluated using products from satellite observations by the Special Sensor Microwave Imager/Sounder (SSMIS) and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) for June–September 2011, with a focus on tropical areas (30°S–30°N). Consistent with previous studies, the predicted diurnal cycle of precipitation peaks too early (by ~3 h) and the amplitude is too strong over both tropical ocean and land regions. Most of the wet and dry precipitation biases, particularly those over land, can be explained by the diurnal-cycle discrepancies. An overall wet bias over the equatorial Pacific and Indian Oceans and a dry bias over the western Pacific warm pool and India are linked with similar biases in the climate model, which shares common parameterizations with the NWP version. Whereas precipitation biases develop within hours in the NWP model, underestimates in water vapor (which are assimilated by the NWP model) evolve over the first few days of the forecast. The NWP simulations are able to capture observed daily-to-intraseasonal variability in water vapor and precipitation, including fluctuations associated with tropical cyclones.

The Impact of MetOp and Other Satellite Data within the Met Office Global NWP System Using an Adjoint-Based Sensitivity Method

Abstract The role of observations in reducing 24-h forecast errors is evaluated using the adjoint-based forecast sensitivity to observations (FSO) method developed within the Met Office global numerical weather prediction (NWP) system. The impacts of various subsets of observations are compared, with emphasis on space-based observations, particularly those from instruments on board the European Organisation for the Exploitation of Meteorological Satellites Meteorological Operational-A (MetOp-A) platform. Satellite data are found to account for 64% of the short-range global forecast error reduction, with the remaining 36% coming from the assimilation of surface-based observation types. MetOp-A data are measured as having the largest impact of any individual satellite platform (about 25% of the total impact on global forecast error reduction). Their large impact, compared to that of NOAA satellites, is mainly due to MetOp's additional sensors [Infrared Atmospheric Sounding Interferometer (IASI), Global Navigation Satellite System (GNSS) Receiver for Atmospheric Sounding (GRAS), and the Advanced Scatterometer (ASCAT)]. Microwave and hyperspectral infrared sounding techniques are found to give the largest total impacts. However, the GPS radio occultation technique is measured as having the largest mean impact per profile of observations among satellite types. This study demonstrates how the FSO technique can be used to assess the impact of individual satellite data types in NWP. The calculated impacts can be used to guide improvements in the use of currently available data and to contribute to discussions on the evolution of future observing systems.

Forecast sensitivity to observations in the Met Office Global numerical weather prediction system

AbstractAn adjoint‐based method for calculating the impacts of observations in the Met Office's global four‐dimensional variational assimilation (4D‐Var) system is documented. Our approach is novel, as we seek from the outset a linearized approximation to partition the finite impact from a batch of observations, but our method and results are very similar to those of other systems. The large beneficial impacts measured for satellite radiances and radiosonde soundings in our system are interesting results. We also identify areas for potential improvement, such as the assimilation of Northern Hemisphere stratospheric satellite radio occultation (GPSRO) observations, polar atmospheric motion vectors and tropical cyclone (TC) bogus observations synthesized automatically from tropical cyclone warning centre advisory messages.We use a toy model to explain why only just over 50% of observations improve the forecast. Growing modes, observational errors and errors in the verifying forecast contribute approximately equally and we suggest that our necessarily imperfect knowledge of background‐error statistics also contributes.

Observations and modelling of the solar and terrestrial radiative effects of Saharan dust: a radiative closure case‐study over oceans during the GERBILS campaign

AbstractMineral dust events exert a significant perturbation to the Earth's radiation balance via scattering and absorption in both solar and thermal infrared wavelengths. This study documents aircraft‐based measurements of the solar and terrestrial radiative effects of a mineral dust outbreak off the west coast of Africa while the FAAM BAe‐146 was in transit to the GERBILS observational measurement campaign. By comparing model and measurements of upwelling irradiance, an instantaneous top‐of‐atmosphere broadband solar direct radiative effect (DRE) of −33 ± 6 W m−2 is determined for an aerosol optical depth at 0.55 μm (τ0.55) of around 0.26 ± 0.04 with the variability in τ0.55 being estimated from the uncertainty in the aerosol models used in the radiative transfer calculations. Measurements of the spectral dependence of the solar radiative effect indicate that this is well modelled both above and below the dust layer whether using spherical, spheroid or irregular particle models. The terrestrial radiative impact at the top of the atmosphere is estimated to be +9 ± 3 W m−2 or around 25–30% of the solar radiative effect in this particular case, although the relative magnitude will of course be dependent on the underlying surface albedo, surface skin temperature, and details of the vertical profile of dust, temperature and humidity. The DRE retrieved from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) is in reasonable agreement with the aircraft measurements. Mineral dust aerosol optical depths derived from the Met Office global numerical weather prediction model show a reasonable spatial distribution but a general underestimation when compared against SEVIRI. Additionally, as expected from a relatively low‐resolution global‐scale model, the high τ0.55 values associated with mesoscale convective events are not well reproduced. Copyright © 2011 Royal Meteorological Society and British Crown Copyright, the Met Office

Using Satellite-Derived Snow Cover Data to Implement a Snow Analysis in the Met Office Global NWP Model

AbstractSnow cover and amount are important components in the interaction between the land surface and atmosphere, but until recently there has been no observational snow information incorporated into the Met Office global numerical weather prediction (NWP) model. This paper presents a Northern Hemisphere (NH) snow analysis, based on satellite-derived observations of snow cover, that has recently been implemented in the operational global Unified Model (UM). The analysis uses daily snow maps from the NOAA National Environmental Satellite Data and Information Service Interactive Multisensor Snow and Ice Mapping System (IMS) to modify the UM background snow amount. Assimilation experiments carried out during the NH snow accumulation and ablation periods show improvements in analyzed snow cover, both qualitatively and quantitatively, when compared with unmodified control runs. Although the effect on forecast accuracy of introducing the snow analysis is largely neutral, there is some evidence of small improvements in screen-level temperature and humidity forecasts. Retention of information introduced by the snow analysis is hard, and so the limited impact on forecast accuracy is not unexpected. Several methods of validation and verification are presented, and their results and implications are discussed. The NH snow analysis delivers a basic snow assimilation system that can be expanded and developed further in future iterations to improve the representation of snow cover and amount in global and regional forecasting models.

Examination of long‐wave radiative bias in general circulation models over North Africa during May–July

AbstractSatellite data are used to quantify and examine the bias in the outgoing long‐wave (LW) radiation over North Africa during May–July simulated by a range of climate models and the Met Office global numerical weather prediction (NWP) model. Simulations from an ensemble‐mean of multiple climate models overestimate outgoing clear‐sky long‐wave radiation (LWc) by more than 20 W m−2 relative to observations from Clouds and the Earth's Radiant Energy System (CERES) for May–July 2000 over parts of the west Sahara, and by 9 W m−2 for the North Africa region (20°W–30°E, 10–40°N). Experiments with the atmosphere‐only version of the High‐resolution Hadley Centre Global Environment Model (HiGEM), suggest that including mineral dust radiative effects removes this bias. Furthermore, only by reducing surface temperature and emissivity by unrealistic amounts is it possible to explain the magnitude of the bias.Comparing simulations from the Met Office NWP model with satellite observations from Geostationary Earth Radiation Budget (GERB) instruments suggests that the model overestimates the LW by 20–40 W m−2 during North African summer. The bias declines over the period 2003–2008, although this is likely to relate to improvements in the model and inhomogeneity in the satellite time series. The bias in LWc coincides with high aerosol dust loading estimated from the Ozone Monitoring Instrument (OMI), including during the GERBILS field campaign (18–28 June 2007) where model overestimates in LWc greater than 20 W m−2 and OMI‐estimated aerosol optical depth (AOD) greater than 0.8 are concurrent around 20°N, 0–20°W. A model‐minus‐GERB LW bias of around 30 W m−2 coincides with high AOD during the period 18–21 June 2007, although differences in cloud cover also impact the model–GERB differences. Copyright © Royal Meteorological Society and Crown Copyright, 2010

Factors Affecting Ship and Buoy Data Quality: A Data Assimilation Perspective

Abstract Ship and buoy reports of wind, air pressure, temperature, humidity, and sea temperature for 2007 and 2008 have been compared with values from the operational Met Office global numerical weather prediction (NWP) system. Ship reports have been categorized by vessel type, recruiting country, and manual or automatic reporting. Most estimated ship winds (except Dutch ones) are too strong and are better treated as measured winds. After height adjustment, the average ship wind speeds are reasonably consistent with buoy wind speeds, but the global model winds are 6%–12% weaker. Extra care is needed when comparing/assimilating values close to the coast. Ship air temperatures are too warm during the afternoon due to solar heating of the ship. Adjustment of the pressure to sea level is a problem for some larger ships. Passenger ship reports are relatively poor quality for several variables and their winds are rather strong. Automated ship reports and those from research and coast guard vessels tend to be of good quality but some of the winds are slightly too weak. In most respects buoy data have the best quality. A number of improvements to the Met Office observation processing system are being made, notably tightening of the quality checks and better height adjustment of winds.

Evaluation of Surface Water and Energy Cycles in the Met Office Global NWP Model Using CEOP Data

Components of the surface water and energy balance are evaluated in the Met Office global Numerical Weather Prediction (NWP) 00-36 hour forecasts using (i) GEWEX Continental Scale Experiment (CSE) reference site data provided by the Coordinated Enhanced Observing Period (CEOP) project and (ii) other earth observing datasets from satellites and in-situ measurements. The global hydrological cycle in the model is in reasonable balance in recent model versions. However, comparison against available observations suggest that both precipitation and evaporation are overestimated over both land and ocean, with largest errors over the tropical oceans. Comparison of the model's seasonal and diurnal cycles of surface fluxes, temperature, precipitation, and moisture against the GEWEX/CEOP CSE sites during October 2002 to September 2003 reveals several issues concerning model parametrization performance. For the high latitude (Arctic) sites low cloud is overestimated during boreal winter leading to an enhanced greenhouse effect and too warm near surface temperatures in the model. Snow melt occurs too early in boreal spring in the model forecasts which currently have no snow analysis component. Possible reasons for early snow melt are discussed including the specification of albedo over heterogeneous terrain and excessive sublimation of snow in the model. The mid-latitude CEOP sites all show excessive evaporation and precipitation during boreal spring and early summer and comparison with the International Satellite Cloud Climatology (ISCCP) products show systematic under prediction of cloud cover over mid-latitude land in summer. Some of the largest biases in mid-latitude surface fluxes in summer are during daytime on non-precipitating cloudy days where the model overestimates the downward SW radiation, latent and sensible heat fluxes. Finally, over tropical land we see a tendency to underestimate precipitation which is in direct contrast to tropical oceans where precipitation accumulations are too large. We have also used the CEOP data to investigate structural errors in the vertical profiles of tropical humidity which suggest deficiencies in the convection scheme.

Modelling tides and surface drift in the Arabian Gulf—application to the Gulf oil spill

A tide and surge forecasting model capable of predicting conditions for up to 5 days ahead has been developed to provide environmental data on tides, currents and particle trajectories in the Arabian Gulf. A two-dimensional depth-integrated model on a 5′ × 5′ grid of the entire Gulf, driven by a 10 constituent tidal forcing at the mouth near the Strait of Hormuz and by meteorological forecasts from the United Kingdom Meteorological Office global numerical weather prediction model was used to provide hourly distributions of the depth-averaged tidal and wind-driven currents. Following the discharge of oil from Mina Al Ahmadi into the Gulf in January 1991 an oil spill model was interfaced to the tide and surge model, providing forecasts of the movement and spread of the oil slick. The oil spill model used a three-dimensional particle tracking algorithm to model the dispersion of the oil so that surface and sub-surface concentrations could be determined. The effects of surface evaporation and decay of the oil were included in the model.