Assimilation of cloud and precipitation affected microwave radiances at ECMWF

Transcription

1 Assimilation of cloud and precipitation affected microwave radiances at ECMWF Abstract Alan Geer, Peter Bauer and Philippe Lopez European Centre for Medium-range Weather Forecasts Cloud and precipitation affected radiances from SSM/I have been assimilated operationally at ECMWF since June 2005, using a two-step approach. A 1D variational (1D-Var) retrieval produces a total column water vapour pseudo-observation that is then assimilated in the main 4D-Var analysis. The 1D-Var observation operator contains simplified large-scale and convective cloud schemes as well as microwave radiative transfer. This paper briefly summarises the operational system, it describes the impact of the assimilation of SSM/I rain observations, and examines test cases and problem areas. It also describes the modifications that have been made to the system after its first operational implementation. Introduction In terms of both modelling and observations, it is perhaps in the area of clouds and precipitation that Numerical Weather Prediction (NWP) is least well-developed. Such phenomena occur on much smaller scales than the 25 to 60 km horizontal resolution of current forecast models. Satellite sensors in the infrared (IR) and visible are unable to see much below the cloud top. Sensors in the microwave are much less sensitive to cloud. Because they can provide information in the region of active weather systems, which are usually cloudy, microwave observations provide a large benefit to forecasts, particularly in the SH (e.g English et al., 2000). Until recently, however, microwave observations remained largely unused in areas of heavier cloud and rain, because of the difficulty of simulating radiative transfer in such conditions, and the non-linearity of the moist physical processes involved. It is hoped that forecast benefits will come from assimilating more observations in such areas. One of the first operational uses of satellite data in such areas is the assimilation of cloud and rain-affected Special Sensor Microwave / Imager (SSM/I) radiances at the European Centre for Medium-range Weather Forecasts (ECMWF), which started in June 2005 (Bauer et al., 2006a,b). SSM/I observations are sensitive to surface properties, total column water vapour (TCWV), cloud ice and water, and rain and snow (Chevallier and Bauer, 2003). In order to assimilate this information, an observation operator is required that links the model s prognostic variables, principally temperature, pressure, winds and water vapour, to the radiances seen by the satellite. At ECMWF the operator comes in two parts: a cloud operator calculates the cloud and precipitation profile associated with a particular profile of temperature and humidity, and from this, a radiative transfer operator calculates the observed radiances. The cloud operator is formed from a convection scheme (Lopez and Moreau, 2005), which models the effect of clouds formed by sub-grid scale transport, and a large-scale condensation scheme (Tompkins and Janisková, 2004), which models clouds when they are formed by model-

2 resolved transport. Given a profile of temperature, moisture, cloud and precipitation, and surface parameters, it is then possible to calculate the microwave radiances seen by the satellite, taking into account not just absorption and emission, but also the scattering effects of raindrops and ice particles (Bauer, 2006c). For all these operators, an adjoint version must also exist, linking changes in the observations back to changes in modelled temperature and moisture. This paper reviews the performance of the operational assimilation of cloud and rain-affected SSM/I radiances at ECMWF, it presents recent upgrades to the system, and it describes a number of areas where improvements are still needed in order to get the best out of the observations. 1D+4D-Var assimilation of SSM/I cloudy and rainy radiances at ECMWF ECMWF produces routine global analyses and 10-day forecasts from an assimilation system based on an atmospheric model with a semi-lagrangian, spectral formulation. The model has 91 levels in the vertical from the surface to an altitude of 80km, and a T799 horizontal resolution, corresponding to about 25km (see for documentation on earlier model versions; documentation on the latest version is not yet available). A global analysis of wind, temperature, surface pressure, humidity and ozone is produced twice-daily using four-dimensional multivariate variational assimilation (4D-Var) with a 12-hour time window (Rabier et al. 2000). The analysis includes in-situ conventional data, satellite radiances from polar-orbiters, mainly from the AMSU-A and AIRS instruments, geostationary radiances, satellite-derived atmospheric motion vectors and surface wind from scatterometers (McNally et al., 2006, in these proceedings). SSM/I radiances are assimilated, over oceans only, in two streams. First, rain and cloud amounts are estimated directly from the observed brightness temperatures. Observations identified as clear sky are assimilated as radiances directly in 4D-Var. Cloud and rain-affected radiances are assimilated using a twostep 1D+4D-Var procedure (Bauer et al., 2006a,b). The two-step procedure starts with a 1D-Var retrieval at each observation point. From this, a TCWV pseudo-observation is derived and then assimilated in the main 4D-Var analysis. Why do we not assimilate the radiances directly into 4D-Var? At ECMWF between 1992 and 1996, a 1D-Var retrieval was used for assimilation of IR observations (Eyre et al., 1993), but the real forecast benefit from these observations only appeared after the introduction of 3D-Var and 4D-Var data assimilation systems (e.g., Bouttier and Kelly, 2001; Uppala et al., 2005), which in particular enabled the retrieval step to be eliminated and radiances to be assimilated directly. However, early experiments towards assimilation of rain and cloud observations at ECMWF (e.g. Marécal and Mahfouf 2000, 2002; Moreau et al. 2004) have all been based around 1D-Var retrievals. This is because the moist physics and radiative transfer of such observations tends to be highly non-linear. A 1D-Var retrieval is thought better able to handle such non-linearities than the full 4D-Var analysis (Marécal and Mahfouf, 2004). A further advantage is the ability to apply quality controls after 1D-Var, in order to eliminate bad retrievals. Such observations might cause convergence failures if used directly in the 4D-Var assimilation.

3 Fig. 1: Normalised difference in forecast verification score between RAIN and NORAIN experiments, based on the RMS difference between 24 hr forecasts and own analysis of relative humidity (left panel) and temperature (right panel) for the period 15 th June to 15 th August Positive (red) values indicate a degradation in the experiment compared to the control; negative (blue) values show an improvement. Hatched areas indicate where the differences are significant to 90%. The SSM/I 1D-Var retrieval (Bauer et al., 2006a) makes a best estimate of the state of a single column of the atmosphere at the observation point, combining the observed SSM/I radiances in channels 19v, 19h and 22v, with an a-priori temperature and moisture profile taken from the first guess forecast of the 4D-Var assimilation. A variety of surface parameters are supplied to the moist physics and radiative transfer operators, but are held fixed. The control vector, i.e. the state that is varied until the best estimate solution is found, contains only the humidity and temperature, and since September 2006, the surface winds. The vertical profile of cloud and precipitation is derived from the temperature and moisture profile using the moist physics operators. It is important to realise that the SSM/I radiances do not contain enough information to unambiguously reconstruct the full atmospheric state. Unlike a typical satellite retrieval, the 1D-Var retrieval depends strongly upon the model s first guess. In practice, the technique is able to produce a retrieval that matches the SSM/I observations very closely (see Fig. 7, Bauer et al., 2006a). A TCWV amount is calculated from each 1D-Var retrieval, and around 20,000 of these pseudo-observations are assimilated into 4D-Var in each 12-hour window. The 1D-Var retrieval typically makes only very small changes to the first guess temperature profile; these temperature changes are not assimilated in 4D-Var. The TCWV observations cause changes in moisture, cloud, precipitation, and wind fields in the 4D-Var analysis (Bauer et al., 2006b). Here, we have chosen to judge whether that impact is good or not by studying the improvement or degradation in forecasts made with and without the benefit of these observations. Figure 1 gives an example of the impact of these TCWV pseudo-observations on 24 hr forecast scores in cycle 31R1 of the ECMWF system, a version taking into account many of the improvements described in the next section. Forecast errors are calculated as the root-mean squared (RMS) difference between forecasts and analyses valid at the same time. The figure shows the normalised difference in scores between two experiments. The first experiment is similar to the operational 31R1 system, and includes the assimilation of SSM/I cloud and rain affected observations ( RAIN ). The second experiment ( NORAIN ) eliminates these from the system. Negative differences indicate areas where the RAIN

4 Fig. 2: Visible (channel 1) image from Meteosat-8 showing the South Atlantic at 12UTC, 14th August Image copyright EUMETSAT 2005 and courtesy NERC Satellite Receiving Station, Dundee University, Scotland ( Red X marks the approximate location of the case study. experiment has lower RMS errors than the NORAIN experiment, and hence RAIN is improving the forecasts. There is a significant improvement in tropical RH in RAIN, centred on 700hPa. There are significant changes in temperature scores at most levels of the tropical atmosphere, most of which are improvements, but there is degradation at 300hPa. We have not shown forecast scores at other forecast times, but RAIN generally improves forecast scores throughout the forecast range, out to 10 days, though the changes are not statistically significant. The experiments shown in Fig. 1 come from a larger body of work done in collaboration with EUMETSAT, and further details (though not a description of the particular experiments used in this paper) can be found in Kelly (2006, in these proceedings). In general, these experiments have confirmed that the assimilation of cloud and rain-affected SSM/I observations does make a positive impact on the ECMWF system. The remainder of this paper explores some of the developments that have been made since the first implementation of SSM/I rain assimilation (Bauer et al. 2006a,b), in Cycle 29r2 of the ECMWF system, in June A main motivation for the subsequent developments was the observation that forecast scores were being degraded in the SH winter (see Fig. 8, Bauer et al., 2006b). A variety of improvements have been made to the SSM/I assimilation, including the elimination of retrievals which contained too much falling snow. We start with a test case illustrating such retrieval. Case study This section examines a single 1D-Var retrieval in the South Atlantic in the southern hemisphere winter, both to illustrate the workings of 1D-Var and also to show the difficulties of making retrievals when there is an excess of falling snow. Such cases have now been eliminated from the operational system in order to improve forecasts. The case study is from 14th August 2005, southwest of South Africa, within an intrusion of very cold polar air moving northwards. The Meteosat visible channel image (Fig. 2) shows the location of the retrieval within a line of convective cloud along the cold front associated with this northwardmoving air. Behind the front, the cold air-mass shows widespread scattered convective cloud.

5 Table 1: SSM/I brightness temperature departures (observation minus first guess) and TCWV at first guess and after 1D-Var retrieval. Columns in bold indicate the assimilated channels; others are passively monitored. TCWV /kgm-2 Tb departure /K FG analysis SSM/I channel 19v 19h 22v 37v 37h 85v 85h Fig. 3: Tephigram showing the 1DVar temperature ( C, right hand lines) and dewpoint temperature ( C, left hand lines) versus potential temperature at first guess (black line) and analysis (red line) from example at 16 E, 40 S on 14th August The analysed temperature is essentially no different to the first guess. Table 1 indicates the first guess and analysed brightness temperature departures from the 1D- Var retrieval, along with the first guess and retrieved total column moisture. Only SSM/I channels 19v, 19h and 22v are actively assimilated. The higher frequency channels are only passively monitored. In general, SSM/I brightness temperatures increase with increasing liquid cloud and rain amounts (see e.g. Chevallier and Bauer 2003). Here, first guess departures are positive, indicating that the model s first guess has insufficient rain and/or cloud. Figure 3 shows the first guess temperature and moisture profiles, taken from the first model trajectory of the 4D-Var assimilation, along with the profiles retrieved using 1D-Var. The tephigram indicates a potential for convection between roughly 950hPa and 550hPa in the first guess. The 1DVar analysis substantially increases water vapour amounts at levels below 550hPa, making the column even more convectively unstable. The temperature profile remains essentially unchanged between first guess and analysis.

6 Fig. 4: (a) Rain (solid line) and snow (dashed) fluxes; (b) liquid (solid line) and ice (dashed) cloud water content and (c) cloud fraction from the 1DVar retrieval at 16 E, 40 S on 14th August Black lines show the 1DVar first guess and red lines the 1DVar analysis. Figure 4 shows precipitation and cloud parameters from the 1DVar retrieval. These quantities are reconstructed from the temperature and moisture profile by the moist physics part of the 1D-Var observation operator. Precipitation is mainly in the form of snow, melting to rain only near the sea surface. This precipitation has come mainly from the convection scheme. Significant cloud fractions are found only at 900hPa, where the large scale cloud scheme is active, and at 500hPa to 600hPa, where convective detrainment is simulated. The detrainment altitude and the cloud fraction at this level are substantially larger in the analysis than the first guess, indicating much stronger convection, as might be expected from the tephigram. With the substantial increase in rain and cloud, the 1DVar analysis matches observations well at 19v/h, 22v and 37v/h (Table 1). However, there are still large departures in the 85v and 85h channels. In contrast to the other SSMI channels, 85v and 85h are sensitive to snow. In contrast to the influence of rain and cloud, snow tends to reduce 85v/h microwave brightness temperatures (see Chevallier and Bauer, 2003). Hence these departures suggest that the 1DVar analysis has too much snow. Why are channels 85v and 85h not actively assimilated? Microwave radiative transfer is thought to be less reliably simulated for snow than for rain, and the combined observation operator (physics and radiative transfer) is more non-linear at these wavelengths. Nevertheless, these large departures still provide an indication that there is a problem with the retrieved snow amounts. Cases such as this were typical of northward intrusions of cold polar air during August 2005, and such intrusions were found to be associated with systematic large positive increments in TCWV in 1D-Var. This, and the fact that the 1D+4D-Var assimilation of SSM/I rainy radiances appeared to be degrading forecast scores slightly in the SH winter, suggests a systematic problem. It is not clear whether the problem lies in the first guess, the moist

7 Table 2: Cumulative improvements to SSM/I rain assimilation Experiment Description Control Cycle 30R2 with SSM/I rain assimilation turned off A Cycle 30R2 B Cycle 30R2 + surface wind as a sink variable in the retrieval. C Expt. B + improved bias correction D Expt C + screening for excess falling snow in the model first guess E Expt D + changes to moist physics operators F Cycle 31R1 (includes all of above changes, plus changes to other parts of the system) G Cycle 31R1 + screening of poorly converged retrievals H Expt G + now includes retrievals previously rejected because of negative humidity physics or the radiative transfer, and an independent observational validation would be difficult in such a remote oceanic area. However, the presence of excessive falling snow is now used to screen out such profiles. Improvements to the operational system This section summarises developments to the assimilation of rainy SSM/I radiances that have happened since it went operational in June A driver for these improvements was the fact that impact studies showed that SSM/I rain assimilation was actually degrading temperature and geopotential forecast scores in the SH. Work over the past year has attempted to remove these problems and to fine-tune the system. Experiments have been made in which each new modification has been added progressively to the full assimilation system, and these are listed in Table 2. Experiments were run for the month of August 2005, and were based on a slightly lower than operational resolution (T511 rather than T799) to save computer resources. These tests started within the experimental cycle 30R2 of the ECMWF system. This cycle had a bug that caused excessive upper tropospheric moisture, but we do not believe this significantly affected the results of our tests. Cycle 30R2 was not made operational, and instead the bug was fixed in a new cycle, 31R1, which included all improvements to the rainy SSM/I assimilation available at the time, along with many other changes to the ECMWF system. Cycle 31R1 went operational on 12 th September 2006 and further modifications have been tested within this new cycle. Test have been made incrementally, adding each new development in turn. Figure 5 gives an illustrative example of the forecast verification statistics used to test these modifications, based on the RMS errors in geopotential height at 500hPa. Statistics such as these have been examined at levels throughout the troposphere, and for temperature, relative humidtity, and vector wind, and they support the conclusions made here. Figure 5 shows the normalised difference in RMS forecast errors between experiment and control, with a positive value indicating that RMS errors have become larger in the experiment compared to control. The control experiment is in all cases based on cycle 30R2 but with SSM/I rain assimilation turned off. The difference in forecast scores between Experiment A and the control tests the impact of SSM/I rain assimilation within the baseline 30R2 system. The black line on figure 5 shows that at 30R2, SSM/I rain assimilation actually degrades SH forecast scores, particularly

8 Fig. 5: Normalised difference in forecast verification score between RAIN experiments and NORAIN control, based on the RMS difference between forecast and own analysis of geopotential height at 500hPa, for the month of August Positive values indicate a degradation in the experiment compared to the control; negative values show an improvement. Error bars indicate the 90% confidence range for a significant difference between experiment and control and are plotted for expt. G only. See Fig. 6 for a key. Fig. 6: As for Fig. 5 but showing the normalised difference in T+72hr forecast score, in the SH region only. in the 2-4 day range. Rain assimilation improves geopotential height scores at short ranges in the tropics in the baseline 30R2 system, and has no significant impact in the NH. Figure 6 focuses on the 3 day (72 hr) forecast errors in the SH, in order to see the results of each experiment more clearly, and also functions as a key for Fig. 5. Our first hypothesis was that problems in the SH could be caused if there were errors in first guess surface wind speed and these were being aliased into the temperature and humidity retrieval. Surface winds have an influence on sea surface microwave emissivity (e.g., Phalippou, 1996), but in the original version of SSM/I rain assimilation, sea surface wind speed was not allowed to vary between first guess and analysis. Hence, in experiment B, the surface wind speed (precisely, the 10m wind) was added to the 1D-Var control vector and allowed to vary during the retrieval. However, Fig. 6 shows that this made little difference to forecast scores. Further, there was very little change in surface wind speed between 1D-Var first guess and analysis (not shown). The most likely explanation is that in cloudy, rainy atmospheres, brightness temperatures are far more sensitive to changes in cloud properties than to changes in surface wind speed. In the retrieval, the specified background errors constrain the wind quite tightly, but allow moisture and cloud properties to vary strongly. The

9 change was technically justified but it is clear that problems in the southern hemisphere were not linked to the surface wind. Originally, bias correction used TCWV as the only predictor, and all three SSM/I instruments were assumed to have the same biases. Doing the SSM/I bias correction individually for each of the three satellites eliminates some small regional inter-satellite biases from the TCWV increment field. Along with two additional predictors (surface wind speed and rain amount) this revised bias correction was tested in experiment C, and was responsible for substantial (though not in general statistically significant) improvements in forecast scores in the SH (Figs. 5 and 6). We saw in the previous section that retrievals appear to be unreliable where there is a lot of falling snow. Our solution (experiment D) was to reject all retrievals with integrated column snow amounts greater than 30% of the column rain amount. This eliminated retrievals such as the example shown in the previous section, and it removes most observations south of 45 S in August Observation numbers are also reduced in areas of tropical convection, and at high northerly latitudes. Figs. 5 and 6 show this change had a positive (though again in general not significant) influence on forecast scores in the southern hemisphere. The final change at 30R2 (experiment E) was to make improvements to the linearised moist physics used in the 1D-Var retrieval. It was hoped that this would reduce discrepancies between the 1DVar and the 3D model first guesses of rain and cloud. These changes had a minor positive impact on forecast scores. In total, however, the improvements between the Experiment A and Experiment E are close to significant at 90% confidence: together, these improvements prevent the SSM/I rain assimilation from degrading forecast scores in the SH in winter. The new version of the assimilation system, cycle 31R1, included all the above changes to SSM/I rain assimilation, plus a number in unrelated areas such as a better treatment of orographic drag and a fix for the bug in upper tropospheric water vapour. Experiment F is a cycle 31R1 experiment but again at a resolution of T511. Comparing experiment F to experiment E shows that these other modifications at 31R1 led to statistically significant improvements in the tropics and smaller improvements in the SH. Cycle 31R1 has been the current operational version of the system since September These experiments suggest that a large part of the forecast improvements in the SH winter are due to the changes to SSM/I rain assimilation. Note however that rather than providing additional forecast benefit, these changes were made simply to prevent rain assimilation from degrading forecast scores in that area. The next modification to SSM/I rain assimilation was made on top of cycle 31R1 and tested in experiment G. We had found that in roughly 15% of cases the 1D-Var retrieval was unable to move away from the first guess profile and closer to observations, yet the resulting TCWV pseudo-observations were still being assimilated in 4D-Var. These were suspected to be cases with strong non-linearity, resulting in cost functions which the minimisation algorithm could not deal with. In these cases, first guess TCWV amounts were being supplied back to 4D-Var as new observations. In experiment G we applied a convergence criterion to eliminate these

10 profiles. Figure 6 shows that forecast scores were degraded in the SH relative to cycle 31R1, though not by enough to be statistically significant. A final modification (experiment H) was intended to better handle instances of negative humidity in the 1D-Var retrievals. In the initial implementation of rainy SSM/I assimilation, approximately 10% of retrievals were being rejected because negative humidities were detected in the control vector during the 1D-Var minimisation. In almost all of these cases, the atmospheric profile consisted of a moist, cloudy, boundary layer (necessary to trigger the rainy, rather than the clear sky, SSM/I assimilation stream) overlaid by very dry layers in the mid and upper troposphere. Such cases are particularly prevalent in the subtropical oceanic high pressure areas. When the 1D-Var retrieval attempted to make the atmospheric column drier and less cloudy in such circumstances, it was possible for humidities in the dry upper layers to become negative. Since the forward model would fail if given negative humidities, such retrievals were rejected. This, however, meant there was a bias in such areas towards retrievals where the atmospheric column was being moistened instead. A simple solution was to reset all negative humidities to zero. As a result, first guess departure biases were reduced in the TCWV pseudo-observations (figures not shown) and a there was a minor, though not significant, improvement in SH forecast scores, particularly at the longer forecast ranges (Fig. 5). All of the modifications to rainy SSM/I assimilation described in this section appear to be justified in technical terms, and have either been, or will soon be, implemented operationally. However, only three of these modifications had much of an impact on forecast scores in the experiments shown here, and only in the SH. It is to be expected that changes would be largest in the SH since SSM/I observations are only assimilated over ocean. Hence, the largest number of observations are found in the SH, along with the largest changes to the number of those being assimilated. The improvements in forecast scores were not statistically significant except when the SSM/I changes were combined with other modifications to create cycle 31R1 (experiment F). Also, experience shows that we should be cautious about identifying changes as significant based on experiments as short as one month (e.g. Andersson et al., 1998; Bouttier and Kelly, 2001). Nevertheless, it has been possible to identify those modifications that do affect forecast scores. It is worth trying to understand why they did so. The changes to SSM/I bias correction (experiment C) improved forecast scores. Given the importance of bias correction in data assimilation this would come as no surprise. Experiment D improved scores by removing a significant number of mainly high latitude SH observations from the system that were thought to be unreliable because of excessive falling snow, and the strongly moistening first guess TCWV departures associated with them. Experiment G eliminated observations that were representative only of the first guess, not the new information from SSM/I, and appears to have degraded forecast scores. In both experiments D and G, it seems that a decrease in the usage of information from SSM/I corresponds to an improvement in forecast scores. This might suggest a continuing problem with the rainy SSM/I assimilation, at least in the SH winter. Alternatively, it has been noted (Bouttier and Kelly, 2001) that when more observational data are in included in the analyses, particularly in data-poor areas such as the SH, this can sometimes result in an increase RMS forecast errors simply because the observations have added additional perturbations to the system.

11 Remaining Issues This section outlines two areas where further modifications to the SSM/I rain assimilation seem necessary. The first area is the design of the 1D+4D-Var assimilation, and specifically in the way that information from SSM/I is transferred into the analyses via a TCWV pseudoobservation. The information content of SSM/I is largely that of rain and cloud, yet this information is lost. 1D-Var retrieves a full vertical profile of temperature and moisture; again this information is lost when converted into TCWV. The 1D-Var retrievals often make substantial changes to humidity in the boundary layer. The corresponding 4D-Var moisture increments tend to appear instead at 700hPa to 800hPa, where the humidity background errors are larger. To use the observational information more completely, a direct 4D-Var assimilation of SSM/I rainy radiances is being developed. One of the principle remaining difficulties in 4D-Var is the screening of problem retrievals, such as those identified in 1D- Var in experiment G. If there are too many non-linear or poorly-converging rain observations included in 4D-Var, then it is possible that the whole minimisation could fail. In 4D-Var there are fewer opportunities for screening such observations. Work is ongoing. A second major issue lies in the treatment of observations in rainy and cloudy areas. Such areas are usually close to saturation. The current normalised relative humidity control variable (Hólm et al., 2002) strongly penalises positive moisture increments in areas close to saturation. Figure 7 shows an example from the ITCZ in the eastern Pacific. First guess departures from the SSM/I rain pseudo-observation indicate some areas where the first guess needs to be moistened and some where it needs to be dried. However, there are no moistening increments in 4D-Var in this region, only drying ones. What does a moist first guess departure really mean in an area that is already close to saturation? In the case of the SSM/I rainy retrievals, this typically indicates a lack of cloud or rain in the first guess. Along with the use of TCWV pseudo-observations, the current moisture control variable also prevents such information reaching the analyses. Work has started on a total moisture control variable, i.e. one that contains both water in both its vapour and cloud forms. Combined with the direct 4D-Var of SSM/I rainy radiances, we hope to see significant improvements. Conclusion This paper has given an overview the 1D+4D-Var assimilation of rain and cloud affected SSM/I observations at ECMWF, which became operational in June A number of recent modifications have been examined. These were mostly intended to address problems in the SH winter, where the assimilation of SSM/I rainy radiances originally caused a degradation in forecast scores. Forecast scores were improved by a better bias correction scheme and by the elimination of observations affected by excess falling snow. Other modifications had a small or neutral impact, and when poorly-converged retrievals were removed, there was a slight degrading impact. Nevertheless, because all these changes were technically justified, and have either been included in the latest operational system (cycle 31R1) or will be included soon.

12 a Figure 7: (a) Map of first guess departures for SSM/I rainy TCWV pseudo-observations in a region of the E Pacific from 120 W to 95 W and from the equator to 15 N. Blue shows areas where the observations indicate a moistening is required; yellow and red indicate areas where drying is required. (b) Map of the TCWV increments resulting when these observations are assimilated in 4D-Var. It appears that surface wind speeds are not a problem area in the 1D-Var retrieval. It was hypothesised that there might be errors in the first guess wind speed and that these could be aliased into the 1D-Var moisture retrievals. Consequently, surface wind speed was added to the 1D-Var control vector as a sink variable. However, this had little impact on the 1D-Var retrievals: First guess wind speeds in the ECMWF system are already well constrained by the assimilation of scatterometer winds, and the primary sensitivity in rainy and cloudy areas is to changes of atmospheric moisture, rather than to the surface. The current assimilation of SSM/I rainy observations has some limitations. First, it is likely that the two-step 1D+4D-Var approach, with its use of TCWV pseudo-observations, is losing a lot of the information contained in the SSM/I observations. Second, the normalised relative humidity control variable in 4D-Var is penalising moistening increments, and acting to prevent the SSM/I observations from creating more cloud and rain in the analyses. ECMWF

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