Assimilation of ground-based rainfall observations in ECMWF's global 4D-Var

Transcription

1 Assimilation of ground-based rainfall observations in ECMWF's global 4D-Var Philippe Lopez 1 1 European Centre for Medium-range Weather Forecasts, Shinfield Park, Reading, UK, philippe.lopez@ecmwf.int (Dated: 2 May 2012) 1. Introduction Over recent decades, substantial efforts have aimed at extracting useful information from precipitation observations from passive and active microwave instruments on board satellites as well as from ground-based weather radars, with the hope that this would lead to an improvement in the atmospheric states (or analyses) used to initialize numerical weather prediction (NWP) models. As far as the assimilation of ground-based radar data is concerned, various techniques have been tested and sometimes implemented operationally by numerous NWP centres worldwide. These methods mainly included latent heat nudging (e.g. Macpherson 2001), diabatic initialization (e.g. Ducrocq et al. 2002) and multi-dimensional variational methods such as nd-var with n {1,2,3,4} (e.g. Sun and Crook 2001; Caumont et al. 2010; Lopez 2011) and Ensemble Kalmán Filter (Tong and Xue 2005). At ECMWF, the operational forecasting system is based on an incremental 4D-Var approach (Courtier et al. 1994), which has been extensively used to study and implement the assimilation of satellite microwave observations affected by precipitation and clouds (e.g. from SSM/I, SSMI/S, TMI, AMSR-E; Bauer et al. 2010). This paper describes the more recent developments and experimentation conducted at ECMWF towards the direct 4D-Var assimilation of both ground-based radar precipitation estimates (operational since November 2011) and synoptic station rain gauge measurements (still experimental). 2. Assimilation of rain composites from ground-based radars The work towards the assimilation of rain estimates from ground-based radars at ECMWF was initiated in 2005 in order to study their potential impact on the quality of atmospheric analyses and subsequent forecasts. First, a two-step 1D+4D-Var approach, originally applied to rain retrievals from satellite microwave observations by Marécal and Mahfouf (2003), was tested by Lopez and Bauer (2007) to assimilate NCEP Stage IV combined radar-gauge hourly precipitation estimates over the conterminous USA (Fulton et al. 1998). In this method, individual observed rain rates (RR) were first input to a 1D-Var procedure that produced a vertical profile of temperature and specific humidity increments at each associated model grid point. The specific humidity increments were then vertically integrated to yield pseudo-observations of total column water vapour that were then assimilated in the global 4D-Var. This study showed an improvement in precipitation forecasts over the first 24 hours over the USA as well as a an impact on global atmospheric forecast scores which was either neutral or slightly positive. To overcome some issues inherent in the 1D+4D-Var technique (e.g. the double use of model background information) and also to be more consistent with the treatment of all other observations in ECMWF s operations, efforts were shifted towards the direct 4D-Var assimilation of NCEP Stage IV data. Besides, in order to improve the validity of the tangent-linear hypothesis during the 4D-Var minimization, it was found to be preferable to assimilate 6-hour rather than hourly rain accumulations. Results presented in Lopez (2012) confirmed those of 1D+4D-Var, and in particular that a genuine precipitation analysis could now be obtained. The direct 4D-Var assimilation of NCEP Stage IV 6-hour rain accumulations, limited to the eastern half of the USA to avoid possible issues over the Rocky Mountains, became operational at ECMWF on 15 November On a technical standpoint, surface snowfall events are screened out based on low-level temperature, anomalous propagation situations as diagnosed from the model fields are rejected (Lopez 2009) and ECMWF s variational bias correction procedure (VarBC; Dee 2005) is applied to the remaining rain data. Lastly, the quantity to be assimilated is ln(rr[mm h -1 ] + 1) rather than RR, since this makes the distribution of observation model departures more Gaussian (as required in 4D-Var). As part of the pre-operational tests, T1279 ( 16 km) L91 4D-Var experiments were run over the period 1 April to 21 June 2010 to confirm the neutral or positive impact of American radar data assimilation on analyses and forecast scores on the global scale. Note that these runs featured the same 12-hour window as in ECMWF s operational 4D-Var and that they included all observations available in operations. As a first illustration, Fig. 1 shows the resulting improvement in Equitable Threat Score (ETS) and False Alarm Rate (FAR), which is obtained when comparing 24h precipitation forecasts to the NCEP Stage IV observations. Even though the latter cannot obviously be considered as independent data, the purpose of this comparison is to verify that the 4D-var analysis worked properly by bringing the modelled short-range precipitation closer to observations. As hoped, Fig.1 exhibits a very clear improvement of the precipitation scores with higher ETS and lower FAR values for most precipitation intensity thresholds considered.

2 Figure 1: Impact of the assimilation of NCEP Stage IV rain observations on Equitable Threat Score and False Alarm Rate for precipitation accumulated over the first day of forecast. Verification is performed against the (non-independent) NCEP Stage IV data themselves. Various precipitation intensity thresholds are considered along the x-axis. Higher ETS and lower FAR values indicate an improvement. As far as atmospheric scores and longer forecast ranges are concerned, Fig.2 displays the change in root-mean-square forecast error (RMSFE) against radiosondes for 500 hpa temperature and wind over the northern extratropics, Europe and Asia, and for the 82 days of the experiments. (a) 500hPa wind - northern extratropics (b) 500hPa T - northern extratropics (d) 500hPa T - Asia (c) 500hPa T - Europe Figure 2: Relative change in root-mean-square forecast error against radiosondes resulting from the assimilation of NCEP Stage IV 6-hourly rain accumulations over the USA. Forecast ranges from 0 to 10 days are shown along the x-axis and RMSFE relative change along the y-axis (unitless). Score changes are displayed for (a) 500 hpa wind vector and (b) 500 hpa temperature over the northern extratropics, and for 500 hpa temperature over (c) Europe and (d) Asia. Positive y-values correspond to an improvement and purple bars that do not cross the zero line indicate a 95%-level significance. Panels (a) and (b) in Fig.1 indicate that the direct 4D-Var assimilation of NCEP Stage IV data has a an overall neutral or positive impact over the northern extratropics. In the tropics and the southern hemisphere (not shown), the impact is neutral. More interestingly, significant improvements are found over Europe around day 5 of the forecast and over Asia around days 8-9, which is consistent with previous findings using 1D+4D-Var (Lopez and Bauer 2007). This could be interpreted as the result of the eastward propagation/development of an improvement pattern originating from the USA, where radar data are assimilated. Similar results are found at other vertical levels (not shown). In addition, it is expected (but currently rather difficult to verify) that the improvement found in the analysis of the short-term precipitation over the USA (see Fig.1) will also be beneficial to the simulation of local land surface conditions, in particular soil moisture. 3. Assimilation of SYNOP rain gauge observations More recently, the feasibility of assimilating rain gauge observations in ECMWF s 4D-Var started to be investigated by taking advantage of the technical developments and findings from the operational implementation of precipitation radar

3 assimilation. As a first step, the focus was put on synoptic station (SYNOP) rain gauge (RG) 6-hourly accumulations, which are available in quasi-real time through the Global Telecommunication System (GTS). Prior to running assimilation experiments, a wind-induced error bias correction procedure was developed to eliminate or at least reduce the precipitation undercatch usually seen in raw RG measurements. A simple formulation was obtained by synthesizing the results of Nešpor and Sevruk (1999), which mainly depends on the wind speed just above the gauge (derived from SYNOP 10-m wind speed and RG height above ground), with a crude dependence on RG type (influence of gauge size and shape on the airflow). Strong winds and bigger gauges all contribute to increase the undercatch, the largest effect being that of wind. Overall, the resulting precipitation underestimation usually ranges between 2 and 15% for rainfall, while much larger values can be reached for snowfall. This is why snowy events have been discarded in the experimentation run so far. Also note that wetting and evaporative losses, which are usually smaller, have currently been neglected. A rather crude formulation of representativity errors that arise from the necessity of comparing model grid-averaged precipitation with RG point measurements during the assimilation process, was also designed. At the moment, these errors are assumed to depend only on the time of the year in the extratropics, on the number of RGs used during the superobbing (i.e. averaging) over a given model grid box, and on precipitation horizontal correlations. Furthermore, a fixed error of 0.05 (in log space) is added to the representativity error computed for each superobs. One should note that the same logarithmic transform as for radar data (see section 2) is applied to each RG superobs before the assimilation. Finally, in addition to the wind-induced error bias correction which is added to each original raw SYNOP rain gauge, a third-order polynomial bias correction, based on model background departure long-term statistics, is applied to each rain superobs in order to account for all other sources of precipitation bias. Two direct 4D-Var assimilation experiments using SYNOP RGs were performed according to the set-up described in Table 1. The control run (ERA_CTRL) featured, on purpose, a sparse coverage of SYNOP surface pressure observations only, on top of which the SYNOP RG data were added (in ERA_NEW). The aim was to mimic the data coverage and relatively coarse resolution (T km) of a future reanalysis of the early 20 th century. One should note that highresolution assimilation experiments with full observational coverage were also run to mimic ECMWF s operations, but the impact of SYNOP RGs turned out to be either neutral or only slightly positive. Therefore, the focus will be laid here on the much more interesting results from the data-poor experiments of Table 1. Experiment Truncation Period Observational coverage Trajectory Minimizations ERA_CTRL T511 T95/T159/T255 Apr-Jun 2011 SYNOP surface pressure obs only ERA_NEW T511 T95/T159/T255 Apr-Jun 2011 SYNOP surface pressure obs only + SYNOP RGs Table. 1 Experimental set-up to study the impact of the direct assimilation of SYNOP rain gauges in 4D-Var. Figure 3 displays the mean density of assimilated SYNOP RG superobs per 2 2 grid box and per 4D-Var cycle (i.e. every 12 hours) from experiment ERA_NEW. The highest densities of RG superobs is obtained over Europe, North America, China, and to a lesser extent over South America, South Africa and New Zealand. It should be stressed that tropical RG data were purposefully rejected in the experiments because of their potentially larger representativity errors (due the predominance of convective situations and the relatively sparse data coverage). Overall, 600 superobbed 6-hour RG accumulations were used in each 4D-Var cycle. Figure 3: Mean density of SYNOP rain gauge superobs per 2 2 grid box and per 4D-Var cycle from experiment ERA_NEW (April-June 2011). Figure 4 allows to verify that the 4D-Var minimization with SYNOP RGs works well since it substantially improves shortrange precipitation forecast scores (here ETS and FAR; see section 2) with respect to SYNOP RG themselves. Again, this is

4 clearly a non-independent verification and should be seen as a mere sanity check. ETS increases while FAR is reduced over Europe, the USA and China for all rain intensities considered along the x-axis. Figure 4: Impact of the assimilation of SYNOP RG observations on Equitable Threat Score and False Alarm Rate for precipitation accumulated over the first 6 hours of forecast, from experiments ERA_CTRL and ERA_NEW. Verification is performed against the (non-independent) SYNOP RG data themselves. Layout is as in Fig.1. More importantly, Fig.5 demonstrates that the assimilation of SYNOP RGs in addition to SYNOP surface pressure data is able to significantly improve traditional atmospheric forecast scores (geopotential, temperature and wind) over Europe for forecast ranges up to 10 days. The drop in root-mean-square forecast error (computed against radiosondes, here) is particularly strong over Europe, which benefits from the highest density of SYNOP RG observations (see Fig.3), but better scores are also found over North America and Asia (not shown). Scores are more neutral over the southern hemisphere (not shown). These results suggest that one can better constrain the analysis and forecast of the entire troposphere by assimilating RG data when only a limited coverage in surface pressure observations would otherwise be available. The assimilated RG data seem to be able to provide useful information not only about the thermodynamical state of the atmosphere but also about its dynamics (e.g. convergence/divergence), as evidenced in Fig.5.c for upper-tropospheric wind. (c) 500 hpa geopotential - Europe (b) 850 hpa temperature - Europe (a) 200 hpa wind vector - Europe Figure 5: Relative change in root-mean-square forecast error against radiosondes resulting from the assimilation of SYNOP 6-hour rain gauge accumulations. Forecast ranges from 0 to 10 days are shown along the x-axis and RMSFE relative change along the y-axis (unitless). Score changes are displayed for (a) 500 hpa geopotential, (b) 850 hpa temperature and (c) 200 hpa wind vector over Europe. Positive y-values correspond to an improvement and purple bars that do not cross the zero line indicate a 95%-level significance. The period is April-June To assess the absolute magnitude of the improvement displayed in Fig.5, atmospheric scores of ERA_CTRL and ERA_NEW can also be compared to those of the ECMWF s operational suite, which of course benefits from high-resolution (T km) and full coverage in surface, profiling and satellite measurements. For this purpose, Fig.6 compares the curves of forecast anomaly correlation for forecast ranges from 0 to 10 days and for geopotential, temperature and wind vector over Europe. As expected, the scores for experiments ERA_CTRL and ERA_NEW are substantially degraded compared to operations, as a result of their coarser resolution and drastically reduced data coverage. However, one can also see that the assimilation of SYNOP RG observations allows a significant recovery towards the operational scores. This finding is particularly obvious for Europe, but is also valid for North America and Asia (not shown).

5 (a) 500 hpa geopotential - Europe (b) 850 hpa temperature - Europe (c) 200 hpa wind vector - Europe Figure 6: Curves of forecast anomaly correlation (against ECMWF s operational analyses) as a function of forecast range from 0 to 10 days: operational suite (blue line), ERA_CTRL (red line) and ERA_NEW (green line). The higher the forecast anomaly correlation is, the better the forecast. Parameters are described at the top of each panel. The period is April-June Finally, an independent verification of experiments ERA_CTRL and ERA_NEW using MeteoSat µm infrared brightness temperatures (TBs) is presented in Fig.7 for Europe, in terms of the correlation coefficient between short-range model forecasts and MeteoSat observations. Simulated TBs were computed by applying the RTTOV-9 radiative transfer code to the output model fields from both experiments. Figure 7 shows that the correlation coefficient of 10.8 µm TBs is increased when SYNOP RG data are assimilated, which means that the distribution of clouds is improved as well, but only over the first day of forecast. Beyond that, the improvement vanishes, as already found in previous studies. Figure 7: Curves of correlation coefficients between simulated and observed MeteoSat 10.8µm brightness temperatures over Europe as a function of forecast ranges up to 24 hours: ERA_NEW (blue line), ERA_CTRL (red line). The period for statistics is April-June Summary and prospects Recently, the direct 4D-Var assimilation of 6-hour rain accumulations from NCEP Stage IV radar and rain gauge precipitation composites over the eastern half of the USA was successfully implemented in ECMWF s operations leading to some improvement in short-range precipitation forecasts and a neutral or slightly positive impact on atmospheric longerrange forecast scores. Future efforts will focus on extending the assimilation of radar composites to other continents (e.g. Europe with Odyssey products, Chinese network, ). In parallel, experimental work towards the 4D-Var assimilation of SYNOP rain gauge observations has also begun and preliminary results suggest that substantial benefits might be obtained in the particular context of the future reanalysis of periods with data-sparse observational coverage, such as the early part of the 20 th century. Indeed, the addition of 6-hour rain gauge accumulations to surface pressure observations alone in the assimilation process was found to significantly improve forecasts of the three-dimensional atmospheric state, especially over the northern hemisphere where most rain gauges are available. Further work will aim at investigating the feasibility of assimilating 24-hour rather than 6-hour rain gauge accumulations. Issues might arise with such longer rain accumulations because of the weaker constraint they impose to the model during the assimilation and because of the possible degradation in the validity of the linear assumption over a longer 4D-Var window. If successful, this might open the door to the usage of historical rain gauge records (rather than just SYNOP observations) in a reanalysis framework. Acknowledgment NCEP should be acknowledged for granting access to their Stage IV radar and gauge precipitation analysis data.

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