Exploiting existing ground-based remote sensing networks to improve high-resolution weather forecasts

A new generation of high-resolution (1 km) forecast models promises to revolutionize the prediction of hazardous weather such as windstorms, flash floods, and poor air quality. To realize this promise, a dense observing network, focusing on the lower few kilometers of the atmosphere, is required to verify these new forecast models with the ultimate goal of assimilating the data. At present there are insufficient systematic observations of the vertical profiles of water vapor, temperature, wind, and aerosols; a major constraint is the absence of funding to install new networks. A recent research program financed by the European Union, tasked with addressing this lack of observations, demonstrated that the assimilation of observations from an existing wind profiler network reduces forecast errors, provided that the individual instruments are strategically located and properly maintained. Additionally, it identified three further existing European networks of instruments that are currently underexploited, but with minimal expense they could deliver quality-controlled data to national weather services in near–real time, so the data could be assimilated into forecast models. Specifically, 1) several hundred automatic lidars and ceilometers can provide backscatter profiles associated with aerosol and cloud properties and structures with 30-m vertical resolution every minute; 2) more than 20 Doppler lidars, a fairly new technology, can measure vertical and horizontal winds in the lower atmosphere with a vertical resolution of 30 m every 5 min; and 3) about 30 microwave profilers can estimate profiles of temperature and humidity in the lower few kilometers every 10 min. Examples of potential benefits from these instruments are presented.

Ice supersaturation in the ECMWF integrated forecast system

A parametrization for ice supersaturation is introduced into the ECMWF Integrated Forecast System (IFS), compatible with the cloud scheme that allows partial cloud coverage. It is based on the simple, but often justifiable, diagnostic assumption that the ice nucleation and subsequent depositional growth time-scales are short compared to the model time step, thus supersaturation is only permitted in the clear-sky portion of the grid cell. Results from model integrations using the new scheme are presented, which is demonstrated to increase upper-tropospheric humidity, decrease high-level cloud cover and, to a much lesser extent, cloud ice amounts, all as expected from simple arguments. Evaluation of the relative distribution of supersaturated humidity amounts shows good agreement with the observed climatology derived from in situ aircraft observations. With the new scheme, the global distribution of frequency of occurrence of supersaturated regions compares well with remotely sensed microwave limb sounder (MLS) data, with the most marked errors of underprediction occurring in regions where the model is known to underpredict deep convection. Finally, it is also demonstrated that the new scheme leads to improved predictions of permanent contrail cloud over southern England, which indirectly implies upper-tropospheric humidity fields are better represented for this region.

Characterising the background errors for the boundary-layer capping inversion

The one-dimensional variational assimilation of vertical temperature information in the presence of a boundary-layer capping inversion is studied. For an optimal analysis of the vertical temperature profile, an accurate representation of the background error covariances is essential. The background error covariances are highly flow-dependent due to the variability in the presence, structure and height of the boundary-layer capping inversion. Flow-dependent estimates of the background error covariances are shown by studying the spread in an ensemble of forecasts. A forecast of the temperature profile (used as a background state) may have a significant error in the position of the capping inversion with respect to observations. It is shown that the assimilation of observations may weaken the inversion structure in the analysis if only magnitude errors are accounted for as is the case for traditional data assimilation methods used for operational weather prediction. The positional error is treated explicitly here in a new data assimilation scheme to reduce positional error, in addition to the traditional framework to reduce magnitude error. The distribution of the positional error of the background inversion is estimated for use with the new scheme.

Evaluating the ability of a numerical weather prediction model to forecast tracer concentrations during ETEX 2

In this paper the meteorological processes responsible for transporting tracer during the second ETEX (European Tracer EXperiment) release are determined using the UK Met Office Unified Model (UM). The UM predicted distribution of tracer is also compared with observations from the ETEX campaign. The dominant meteorological process is a warm conveyor belt which transports large amounts of tracer away from the surface up to a height of 4 km over a 36 h period. Convection is also an important process, transporting tracer to heights of up to 8 km. Potential sources of error when using an operational numerical weather prediction model to forecast air quality are also investigated. These potential sources of error include model dynamics, model resolution and model physics. In the UM a semi-Lagrangian monotonic advection scheme is used with cubic polynomial interpolation. This can predict unrealistic negative values of tracer which are subsequently set to zero, and hence results in an overprediction of tracer concentrations. In order to conserve mass in the UM tracer simulations it was necessary to include a flux corrected transport method. Model resolution can also affect the accuracy of predicted tracer distributions. Low resolution simulations (50 km grid length) were unable to resolve a change in wind direction observed during ETEX 2, this led to an error in the transport direction and hence an error in tracer distribution. High resolution simulations (12 km grid length) captured the change in wind direction and hence produced a tracer distribution that compared better with the observations. The representation of convective mixing was found to have a large effect on the vertical transport of tracer. Turning off the convective mixing parameterisation in the UM significantly reduced the vertical transport of tracer. Finally, air quality forecasts were found to be sensitive to the timing of synoptic scale features. Errors in the position of the cold front relative to the tracer release location of only 1 h resulted in changes in the predicted tracer concentrations that were of the same order of magnitude as the absolute tracer concentrations.

Forecast impact of targeted observations: sensitivity to observation error and proximity to steep orography

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.

Ensemble prediction for nowcasting with a convection-permitting model - II: forecast error statistics

A 24-member ensemble of 1-h high-resolution forecasts over the Southern United Kingdom is used to study short-range forecast error statistics. The initial conditions are found from perturbations from an ensemble transform Kalman filter. Forecasts from this system are assumed to lie within the bounds of forecast error of an operational forecast system. Although noisy, this system is capable of producing physically reasonable statistics which are analysed and compared to statistics implied from a variational assimilation system. The variances for temperature errors for instance show structures that reflect convective activity. Some variables, notably potential temperature and specific humidity perturbations, have autocorrelation functions that deviate from 3-D isotropy at the convective-scale (horizontal scales less than 10 km). Other variables, notably the velocity potential for horizontal divergence perturbations, maintain 3-D isotropy at all scales. Geostrophic and hydrostatic balances are studied by examining correlations between terms in the divergence and vertical momentum equations respectively. Both balances are found to decay as the horizontal scale decreases. It is estimated that geostrophic balance becomes less important at scales smaller than 75 km, and hydrostatic balance becomes less important at scales smaller than 35 km, although more work is required to validate these findings. The implications of these results for high-resolution data assimilation are discussed.

On the growth of errors in data assimilation systems

Considerable progress has taken place in numerical weather prediction over the last decade. It has been possible to extend predictive skills in the extra-tropics of the Northern Hemisphere during the winter from less than five days to seven days. Similar improvements, albeit on a lower level, have taken place in the Southern Hemisphere. Another example of improvement in the forecasts is the prediction of intense synoptic phenomena such as cyclogenesis which on the whole is quite successful with the most advanced operational models (Bengtsson (1989), Gadd and Kruze (1988)). A careful examination shows that there are no single causes for the improvements in predictive skill, but instead they are due to several different factors encompassing the forecasting system as a whole (Bengtsson, 1985). In this paper we will focus our attention on the role of data-assimilation and the effect it may have on reducing the initial error and hence improving the forecast. The first part of the paper contains a theoretical discussion on error growth in simple data assimilation systems, following Leith (1983). In the second part we will apply the result on actual forecast data from ECMWF. The potential for further forecast improvements within the framework of the present observing system in the two hemispheres will be discussed.

Atmospheric and oceanic contributions to irreducible forecast uncertainty of Arctic surface climate

Uncertainty of Arctic seasonal to interannual predictions arising from model errors and initial state uncertainty has been widely discussed in the literature, whereas the irreducible forecast uncertainty (IFU) arising from the chaoticity of the climate system has received less attention. However, IFU provides important insights into the mechanisms through which predictability is lost, and hence can inform prioritization of model development and observations deployment. Here, we characterize how internal oceanic and surface atmospheric heat fluxes contribute to IFU of Arctic sea ice and upper ocean heat content in an Earth system model by analyzing a set of idealized ensemble prediction experiments. We find that atmospheric and oceanic heat flux are often equally important for driving unpredictable Arctic-wide changes in sea ice and surface water temperatures, and hence contribute equally to IFU. Atmospheric surface heat flux tends to dominate Arctic-wide changes for lead times of up to a year, whereas oceanic heat flux tends to dominate regionally and on interannual time scales. There is in general a strong negative covariance between surface heat flux and ocean vertical heat flux at depth, and anomalies of lateral ocean heat transport are wind-driven, which suggests that the unpredictable oceanic heat flux variability is mainly forced by the atmosphere. These results are qualitatively robust across different initial states, but substantial variations in the amplitude of IFU exist. We conclude that both atmospheric variability and the initial state of the upper ocean are key ingredients for predictions of Arctic surface climate on seasonal to interannual time scales.

Global observations and forecast skill

The impact of selected observing systems on forecast skill is explored using the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40) system. Analyses have been produced for a surface-based observing system typical of the period prior to 1945/1950, a terrestrial-based observing system typical of the period 1950-1979 and a satellite-based observing system consisting of surface pressure and satellite observations. Global prediction experiments have been undertaken using these analyses as initial states, and which are available every 6 h, for the boreal winters of 1990/1991 and 2000/2001 and the summer of 2000, using a more recent version of the ECMWF model. The results show that for 500-hPa geopotential height, as a representative field, the terrestrial system in the Northern Hemisphere extratropics is only slightly inferior to the control system, which makes use of all observations for the analysis, and is also more accurate than the satellite system. There are indications that the skill of the terrestrial system worsens slightly and the satellite system improves somewhat between 1990/1991 and 2000/2001. The forecast skill in the Southern Hemisphere is dominated by the satellite information and this dominance is larger in the latter period. The overall skill is only slightly worse than that of the Northern Hemisphere. In the tropics (20 degrees S-20 degrees N), using the wind at 850 and 250 hPa as representative fields, the information content in the terrestrial and satellite systems is almost equal and complementary. The surface-based system has very limited skill restricted to the lower troposphere of the Northern Hemisphere. Predictability calculations show a potential for a further increase in predictive skill of 1-2 d in the extratropics of both hemispheres, but a potential for a major improvement of many days in the tropics. As well as the Eulerian perspective of predictability, the storm tracks have been calculated from all experiments and validated for the extratropics to provide a Lagrangian perspective.

Can wavelets improve the representation of forecast error covariances in variational data assimilation?

Two wavelet-based control variable transform schemes are described and are used to model some important features of forecast error statistics for use in variational data assimilation. The first is a conventional wavelet scheme and the other is an approximation of it. Their ability to capture the position and scale-dependent aspects of covariance structures is tested in a two-dimensional latitude-height context. This is done by comparing the covariance structures implied by the wavelet schemes with those found from the explicit forecast error covariance matrix, and with a non-wavelet- based covariance scheme used currently in an operational assimilation scheme. Qualitatively, the wavelet-based schemes show potential at modeling forecast error statistics well without giving preference to either position or scale-dependent aspects. The degree of spectral representation can be controlled by changing the number of spectral bands in the schemes, and the least number of bands that achieves adequate results is found for the model domain used. Evidence is found of a trade-off between the localization of features in positional and spectral spaces when the number of bands is changed. By examining implied covariance diagnostics, the wavelet-based schemes are found, on the whole, to give results that are closer to diagnostics found from the explicit matrix than from the nonwavelet scheme. Even though the nature of the covariances has the right qualities in spectral space, variances are found to be too low at some wavenumbers and vertical correlation length scales are found to be too long at most scales. The wavelet schemes are found to be good at resolving variations in position and scale-dependent horizontal length scales, although the length scales reproduced are usually too short. The second of the wavelet-based schemes is often found to be better than the first in some important respects, but, unlike the first, it has no exact inverse transform.

Evaluation of the Met Office global forecast model using Geostationary Earth Radiation Budget (GERB) data

Simulations of the top-of-atmosphere radiative-energy budget from the Met Office global numerical weather-prediction model are evaluated using new data from the Geostationary Earth Radiation Budget (GERB) instrument on board the Meteosat-8 satellite. Systematic discrepancies between the model simulations and GERB measurements greater than 20 Wm-2 in outgoing long-wave radiation (OLR) and greater than 60 Wm-2 in reflected short-wave radiation (RSR) are identified over the period April-September 2006 using 12 UTC data. Convective cloud over equatorial Africa is spatially less organized and less reflective than in the GERB data. This bias depends strongly on convective-cloud cover, which is highly sensitive to changes in the model convective parametrization. Underestimates in model OLR over the Gulf of Guinea coincide with unrealistic southerly cloud outflow from convective centres to the north. Large overestimates in model RSR over the subtropical ocean, greater than 50 Wm-2 at 12 UTC, are explained by unrealistic radiative properties of low-level cloud relating to overestimation of cloud liquid water compared with independent satellite measurements. The results of this analysis contribute to the development and improvement of parametrizations in the global forecast model.