Assimilation of SEVIRI cloud-top parameters in the Met Office regional forecast model

Transcription

1 Assimilation of SEVIRI cloud-top parameters in the Met Office regional forecast model Ruth B.E. Taylor, Richard J. Renshaw, Roger W. Saunders & Peter N. Francis Met Office, Exeter, U.K. Abstract A system of assimilating SEVIRI cloud products directly into the Met Office NAE forecast model via four-dimensional variational assimilation has been developed. This has been trialled over both Summer and Winter seasons. Forecast impact in Summer is generally slightly positive overall, due to improved precipitation and surface temperature. The impact on forecasts during the Winter period is slightly negative, however, mainly due to poorer verification scores for cloud fraction and visibility. INTRODUCTION For regional numerical weather prediction models, one of the critical variables to forecast correctly is the cloud field, in terms of both its coverage and its vertical distribution. The correct diagnosis of the initialised cloud field is crucial to the performance of these models, since deficiencies in the determination of the cloud coverage, or of its height, can lead to significant errors in the time-evolution of the cloud through the forecast period, in turn causing serious inaccuracies in important forecast quantities such as precipitation, temperature and visibility. The Met Office s regional North Atlantic/European (NAE) model currently makes use of observed cloud-cover and cloud-top height (CTH) fields derived from SEVIRI as part of its cloud analysis, although the methods by which this is accomplished are now rather outdated when compared with the rest of the Met Office s data assimilation system, relying as they do on an intermediate step involving the Office s nowcasting system Nimrod, and the use of an Analysis Correction (AC) scheme to do the assimilation itself. This paper gives a brief overview of this existing cloud assimilation system, and also presents the latest results from attempts to assimilate the SEVIRI cloud products directly into the NAE model via the four-dimensional variational (4D-Var) scheme. INFORMATION ON CLOUD FROM SEVIRI DATA The SEVIRI imager on the Meteosat Second Generation satellites provides 15 minute imagery of the cloud field over the North Atlantic and European areas at a resolution of about 5km. This imagery contains a wealth of valuable information about the cloud, including information on cloud fraction, cloud-top height, cloud-top temperature and cloud phase, which should improve our forecasts significantly were we able to assimilate the information well enough. The Met Office Autosat processor routinely generates many of these cloud products from the SEVIRI imagery (Saunders et al. 2006, Francis et al. 2008). Figure 1 below shows an example of some of these products from the 1430 UTC slot on 8th August 2007, in this case the cloud-top height in Figure 1(a) and the effective cloud amount (ECA) in Figure 1. The ECA is a dimensionless quantity defined as the product of the true cloud-fraction and the cloud emissivity, and varies between 0 and 1.

2 (a) Figure 1: Derived cloud products for the North Atlantic/European area from the slot ending 1430 UTC on 8th August (a) Cloud-top height (metres). Effective cloud amount. CURRENT CLOUD ASSIMILATION IN THE MET OFFICE REGIONAL MODEL SEVIRI cloud parameters have already been provided indirectly to the regional model assimilation for several years via the Met Office s Nimrod nowcasting system (Golding, 1998). Figure 2 shows a schematic diagram of this assimilation route. An MSG-derived cloud mask and cloud-top height product are derived on the Autosat system (see above) and sent to Nimrod, where they are processed, together with surface cloud observations and background model data, to produce a threedimensional cloud fraction analysis. This is then interpolated onto the NAE model grid by the Moisture Observational Preprocessing System, MOPS (Macpherson et al., 1996), before it is assimilated into the model via the Analysis Correction (AC) scheme (Lorenc et al., 1991). For convenience, we shall refer to the cloud data being assimilated via this route as MOPS cloud data for the remainder of this report.

3 (a) (e) NIMROD area (c) (d) Figure 2: Schematic diagram of the current assimilation of SEVIRI cloud data into the NAE model. (a) SEVIRI cloud mask. SEVIRI cloud-top height. (c) Nimrod nowcasting system. (d) MOPS cloud data maximum cloud fraction in column. (e) Met Office North Atlantic/European model domain, with Nimrod domain overlaid. There are many reasons why it is desirable to improve this system: It is rather cumbersome and convoluted, with several intermediate steps between the original data and the assimilation step. It only uses cloud data over UK area, whilst satellite data are available over whole NAE domain. It uses an outdated Analysis Correction scheme, whereas the rest of the data assimilation system uses a 4-dimensional variational (4D-Var) scheme. For these reasons, we are investigating the assimilation of the Autosat cloud products directly into the NAE model via 4D-Var, work that will be described in the following section. DIRECT ASSIMILATION OF SEVIRI CLOUD PRODUCTS VIA 4D-VAR A schematic diagram summarising this more simplified process is given in Figure 3, i.e. we are attempting to assimilate the SEVIRI cloud-top height (CTH) and effective cloud amount (ECA) fields directly into the NAE model, bypassing the need for intermediate steps through the Nimrod and MOPS systems. Note that one drawback of this new approach is that we are no longer making use of the information from surface cloud observations which are available to the Nimrod cloud analysis in the existing route. For convenience, we shall refer to the cloud data being assimilated via this new route as GeoCloud data for the remainder of this report.

4 (a) (c) Figure 3: Schematic diagram of the direct assimilation of SEVIRI cloud data into the NAE model. (a) SEVIRI cloud-top height. SEVIRI effective cloud amount. (c) Met Office North Atlantic/European model domain. The cloud top height and effective cloud amount from the Autosat system are converted into column cloud by specifying a cloud amount on each model level (see Figure 4). Below the cloud top it is assumed that nothing is known about cloud amount. At present we use a single moisture control variable within 4D-Var, so a diagnostic relationship based on Smith (1990) is then used to convert cloud amount into relative humidity information. The difference between observed and model cloud fraction is interpreted as a difference in humidity and assimilated as a humidity observation. Data assimilation produces increments to the humidity which are added to the model humidity field, which via the model cloud scheme will then alter the model cloud field itself. Figure 4: Schematic diagram showing how the SEVIRI cloud retrieval is converted into a column observation of cloud fraction.

5 Figure 5 shows the impact of assimilating the GeoCloud data in a single-cycle test for 1200 UTC on 19th December 2006, with (a) showing the background T+6 hour column cloud fraction from the 0600 model run, showing the effect of assimilating the existing MOPS cloud data on the 1200 analysis, and (c) showing the 1200 analysis having assimilated the GeoCloud data instead. Both analysed fields tend to have slightly less cloud than the background on average see for example the cloud field over northern France. The GeoCloud assimilation tends to remove slightly more cloud than MOPS in general e.g. to the east of Denmark and in the north-west of the domain but there are also areas where the GeoCloud field has more cloud than MOPS e.g. the sea areas to the west of France. (a) (c) ( Figure 5: Column cloud fraction for 1200 UTC on 19th December (a) T+6 hour background from 0600 run analysis using MOPS assimilation. (c) 1200 analysis using GeoCloud assimilation. IMPACT TRIAL RESULTS The new scheme has been trialled for both Summer and Winter periods. In both cases, the control used was a baseline version of the operational suite proposed for implementation in the Autumn of 2008, and included surface observations, radiosonde and aircraft data, atmospheric motion vectors, scatterometer winds, ATOVS and SSMI radiances, GPS radio occultation data and ground-based GPS observations, together with the existing MOPS cloud data described above. For the trials, we have removed the MOPS cloud data and assimilated the GeoCloud data via 4D-Var. Although the GeoCloud data are available for the whole NAE domain, we have only used the data for the UK area in this study, enabling a more meaningful comparison with the existing MOPS cloud assimilation to be made. Figure 6 summarises the overall impact of the GeoCloud assimilation relative to the control for the Summer period, 06/08/ /09/2008. Verification has used all available observations over the NAE domain, and the scores quoted are a weighted sum of the difference in skill between the GeoCloud trial and the control, for six different variables (see Figures 6 and 7) with forecast lead times varying between T+6 and T+48 hours at 6-hourly intervals.

6 Figure 6: Summary of the impact of introducing GeoCloud into the NAE assimilation, relative to a control experiment where the existing SEVIRI cloud assimilation was applied (see Figure 2), for a 42-day period during August and September The y-axis represents a weighted sum of skill difference between GeoCloud experiment and control (see text for more details). We see that overall there is a slight positive impact of %, with the majority of this impact coming from improvements to the six-hour precipitation accumulation and the surface (2 metre) temperature. Also note, however, the slight degradation to the surface visibility and total cloud amount skill scores. Figure 7 summarises the impact of the GeoCloud assimilation relative to the control for the Winter period, 16/12/ /01/2008. Overall, there is a slight negative impact for this trial (-0.10 %). We see that the positive impact observed in the Summer trial for the six-hour precipitation accumulation is retained, but also note that there is now a stronger negative impact for the surface visibility and for verification against surface cloud observations, both in terms of the total cloud amount and the cloudbase height. Figure 7: As Figure 6, but for a 31-day period during December 2007 and January 2008.

7 (a) Figure 8: Verification of the forecast fractional cloud cover from surface observations for (a) the Summer trial, and the Winter trial. The RMS error of the forecast cloud cover is plotted against the forecast range for the GeoCloud experiment (blue line) and the control (red line). This degraded cloud verification is shown in more detail in Figure 8, which plots the RMS error in fractional cloud amount against forecast lead time for both Summer and Winter periods. We see that, in both cases, the GeoCloud forecasts verify significantly more poorly than the control for the shorter forecast ranges, this being particularly true for the Winter trial where it is seen that, on average, the T+6 hour forecasts actually verify better against surface observations than do the T+0 analyses. CONCLUSIONS AND FURTHER WORK A system of assimilating SEVIRI cloud products directly into the Met Office NAE model via 4D-Var has been developed, which removes the necessity of having to go through intermediate pre-processing steps involving the Nimrod and MOPS systems. The new assimilation method has been trialled over both Summer and Winter seasons. It shows a slightly positive impact overall for the Summer trial, due mainly to improved precipitation and surface temperature. However, the Winter forecast impact is slightly negative overall, due to poorer skill scores for surface visibility and for cloud cover and cloudbase height. For this reason, it has been decided that the GeoCloud data will not be assimilated into the Met Office NAE model for the time being, and more research will be carried out with a view to establishing a consistently positive impact on forecasts from these data.

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