AROME NWC: a new nowcasting tool based on an operational mesoscale forecasting system

Transcription

1 Quarterly Journalof the RoyalMeteorologicalSociety Q. J. R. Meteorol. Soc. : 3, July 5 A DOI:./qj.3 AROME NWC: a new nowcasting tool based on an operational mesoscale forecasting system Ludovic Auger, a * Olivier Dupont, b Susanna Hagelin, c Pierre Brousseau a and Pascal Brovelli b a CNRM GAME, Météo-France, Toulouse, France b DP/DPREVI, Météo-France, Toulouse, France c Met Office, Exeter, UK *Correspondence to: L. Auger, CNRM GAME, Météo-France, avenue Gaspard Coriolis, Toulouse, France. ludovic.auger@meteo.fr The AROME NWC nowcasting system has been developed in order to cover the nowcasting h range. It is based on the existing AROME mesoscale model, from which the lateral boundary conditions and the first-guess file are taken. The difference between those two systems is basically the observation window length and a very short cut-off time. Studies have been carried out to show that in practice it is not necessary to compute a set of background-error statistics matrices as a function of the forecast lead time of the first-guess file. The spin-up of the system has been proven to be small and the 5 min cut-off to be long enough to keep a good forecast quality. This nowcasting system has been validated against the operational mesoscale model AROME: it has been shown that it performs better for nowcasting ranges as regards temperature, humidity and wind, due to its use of more recent observations. The precipitation forecasts are less satisfactory, with some improvement during the day and some degradation at night. Key Words: nowcasting; numerical weather prediction; data assimilation Received December 3; Revised September ; Accepted 5 September ; Published online in Wiley Online Library 3 December. Introduction Nowcasting refers to weather forecasting over very short time ranges ( h), by contrast with medium-range forecasting, for which the interest lies in time-scales of the order of magnitude of half a day up to more than week. For time ranges of less than a few hours, nowcasting is expected to outperform classical meteorological weather prediction models by using more recent observations. The initial purpose of nowcasting is to deal with heavy rainfall events that can cause some severe weather hazards (Dixon and Wiener, 993; Wilson et al., 998), but today lots of meteorological parameters that have an impact on human activities, such as wind, temperature or visibility, are also of interest in nowcasting systems (Mecklenburg et al., ). The first techniques employed for nowcasting were based on extrapolation of observations, using either radar reflectivities (Li et al., 995) or satellite radiances (Menzel et al., 998). Then, with the interest in nowcasting enlarged to more weather types, more observations (wind, temperature) were used in short-term decision-making tools (Rasmussen et al., ). Those systems often combine observations and mesoscale model forecasts. The predictability of radar reflectivity extrapolation has been extensively studied (Germann and Zawadzki, ) and it is believed today that, beyond h, the quality of such nowcastings is poor. That h limit depends on the type of system and on the degree of maturity of convection development. During the s, many meteorological centres have developed operational versions of non-hydrostatic local-area models with grid sizes at the kilometric scale (Saito et al., ; Rockel et al., 8). Those configurations have first been used to forecast convectively driven phenomena better, due to their mesh refinement and non-hydrostatic set of equations. In recent years, considerable progress has been made in the use of numerical weather prediction (NWP) for nowcasting and the blending of hourly rapid cycles with traditional extrapolation-based techniques has become increasingly used by weather services (Golding et al., 3; Sun et al., 3). Today, the increase of computational power enables us to use those models to carry out nowcastings that can compete with observation extrapolation methods for the time range h. In Meteo-France, the non-hydrostatic mesoscale model AROME has been developed over the past ten years to cover the forecast time range 3 h (Seity et al., ). This model is initialized with a specific three-dimensional variational data assimilation scheme (hereafter 3DVar), which provides the initial fields relevant for an accurate forecast; this initialization system was formerly developed in the context of the ALADIN France c The Authors. Quarterly Journal of the Royal Meteorological Society This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

2 L. Auger et al. model (Fischer et al., 5). This model provides a forecast framework that can potentially be used as a nowcasting tool that would combine the most recent available observations with the most recent model forecast. In, it was decided that a nowcasting version of the AROME model (further denoted by AROME NWC) would be developed, in order to provide short-term forecasts to enhance the existing nowcasting system SIGnificant weather Object Oriented Nowcasting System (SIGOONS: Brovelli, 5), by providing a smooth transition between a purely observation-based approach and a model-based approach. The purpose of this article is twofold: (i) to describe the features of the AROME NWC system compared with the AROME model settings and how it is best fitted for the shortterm forecast and (ii) to assess the behaviour compared with the AROME forecasts valid at the same time, using both objective scores and a case study.. The AROME NWC configuration.. Description of AROME operational system AROME is the operational model that is currently operated over France at.5 km horizontal resolution (in the following text, AROME denotes this operational configuration). AROME uses the non-hydrostatic version of the Euler equations, which makes possible an explicit description of the convection at.5 km. It also uses five hydrometeor species to take into account a detailed description of the microphysical phenomena. This model has been able to forecast heavy rain events successfully in various operational contexts and is also used as a research tool to study cyclogenesis or hurricane development. Even if the precipitation forecast is the main objective of AROME NWC, the good performance of AROME during fog or snowy events is also of interest from a nowcasting perspective. As regards data assimilation, the AROME model gets its initial condition from a 3DVar algorithm. This observation processing is performed every 3 h over a 3 h time window (Brousseau et al., ). The background-error covariances (hereafter denoted as the B matrix) used are computed from an ensemble of forecasts. This B matrix is purely climatological in the current operational version (Brousseau et al., 8). The AROME B matrix uses a multivariate formulation based on vorticity, divergence, temperature, surface pressure and specific humidity, which are the analysis control variables, under the assumptions of horizontal homogeneity, isotropy and non-separability. A long model forecast up to 3 h is carried out every h (,,, 8 UTC), whereas at intermediate times a 3 h forecast is made to provide an initial background file to the next analysis. The observations used are radiosondes, wind profilers, aircraft reports, ship and buoy reports, automated land surface stations (observations of pressure, m temperature and humidity, m wind), satellite radiances, radar reflectivities and Doppler radial winds from the same radars. In short, both the high-resolution forecast model and mesoscale assimilation capacities are the key factors for AROME to provide the backbone of a nowcasting configuration... AROME NWC system architecture AROME NWC is based on the AROME configuration. In AROME NWC, a data assimilation followed by a short-range forecast ( h) is performed every hour, with lateral boundary conditions refreshed every hour. The atmospheric variables (except hydrometeors) are not cycled, which means the model starts from a recent AROME forecast every time (indeed, the most recent AROME forecast available valid at the initial time). The background state is obtained from an adaptation on to the AROME NWC grid of the AROME forecast. There are two motives for using a background condition from the host model (the model that provides the boundary conditions). Derivative of In (ps) (s ) e 5 e 5 e+ 5 Time (min) Cycling No cycling Figure. Derivative of the logarithm of surface pressure (in absolute value and averaged over the domain) as a function of time for a 5 day period. Firstly, for technical reasons we want to be able to launch a AROME NWC forecast at a specific hour even if the forecast from the previous hour did not run correctly. Secondly, since it will not be possible to assimilate all the observations available (to be seen hereafter), especially large-scale observations such as radiosondes, the AROME NWC upper-level fields might develop biases or imbalances when run in cycled mode compared with systems that can benefit from all the observations available. Those initial imbalances might produce spurious behaviour during the first model time steps: this is referred to as spin-up. The spin-up oscillations can be assessed by looking at the derivative of the surface-pressure field as a function of time. In lots of configurations, it can be dampened with filtering techniques (Lynch and Huang, 99; Fischer and Auger, ). In AROME, no filtering is used, but whether we need such a technique in AROME NWC or not must be investigated. Oscillations that might be considered as spin-up were looked at in a series of AROME NWC forecasts over a 5 day period, for both cycled and non-cycled configurations. Figure presents as a function of time the derivative of the logarithm of surface pressure (in absolute value and averaged over the domain) up to 3 h. The AROME NWC forecast limit value for this diagnostic is roughly.3 5 s ; since after h the value is about. 5 s, we can assume that a large part of the initial field imbalance has already vanished after h. The spin-up in our system is smaller when used in a non-cycled configuration: as a matter of fact, a cycled system produces initial guess files that are further from the boundary conditions compared with a non-cycled system. In term of performance, the verification scores (not shown) do not exhibit any significant difference between the two configurations. The domain used in the current AROME NWC configuration (Figure ) covers a large area around France. It is included inside the AROME domain. The horizontal resolution is.5 km; the time step is s. Figure 3 shows the general architecture of the AROME NWC system. The top line represents the AROME cycle and the light grey arrows below correspond to the initial and lateral boundary conditions (the same host model run provides both initial and lateral boundary conditions). Depending on the starting hour of the analysis, the lead time of the forecast ranges from 9 h. In the current configuration, the best choice was to re-use hydrometeors field from a h AROME NWC forecast. As in AROME, AROME NWC uses soil prognostic variables, representing the temperature and the water content at various soil levels. At each analysis time (except at 3 UTC), those variables come from a previous h AROME NWC forecast, as shown in Figure 3 by the bottom two lines. Once a day, at 3 UTC, instead of re-using a previous AROME NWC surface field we initialize those fields with a forecast derived from AROME. This allows the model not to deviate too much from the true state of the soil 5 c The Authors. Quarterly Journal of the Royal Meteorological Society Q. J. R. Meteorol. Soc. : 3 (5)

3 AROME NWC: A New Nowcasting Tool 5 Figure. The AROME domain (shaded) and the AROME NWC domain inside. parameters, since soil variables are refreshed with observations every 3 h in AROME. The lateral boundary conditions are taken from the same AROME forecast that provides the background file. The coupling, as in AROME, is refreshed every hour using a relaxation method..3. Adaptation of the background-error covariances The 3DVar data assimilation system from AROME used in AROME NWC requires the specification of the B matrix, as already discussed. Since the B matrix represents the average error between the best previous forecast of the atmosphere and the true state of the atmosphere, it could be expected that this B matrix is dependent on the lead time of the background, since it is known that the longer a forecast is, the farther from the observations (hence the true state) it is. For testing purposes, B matrices were computed using an offline ensemble of forecasts, which are run with boundary conditions taken from ensemble forecasts of the global model in use in Meteo-France and with initial conditions generated with an assimilation cycle of perturbed observations (Brousseau et al., ). Since those statistics are only a rough approximation of the true background-error values, we think that a specification of the B matrix dependent on the lead time of the background is inaccurate and does not bring any improvement to our system. To assess this assumption further, a sensitivity experiment with a modification of the B matrix depending on the background lead time was run. First, a set of B matrices adapted to each starting hour were computed: the B matrix that will be used when a n hour AROME forecast is used as a first guess will be computed from an ensemble of n hour forecasts. Diagnostics from the 3 h and h B matrices can be seen in Figure as a function of vertical height and in Figure 5 as a function of wave number. The differences between the set of two curves give the order of magnitude of the background-error differences of the two background states at two different lead times. It can be noted that, for each parameter, the statistics are very close. The small decrease of the error computed from the h ensemble compared with the 3 h ensemble is due to the randomly perturbed observation ensemble method, which produces less spread with time during the first hours of AROME forecast; still, it gives a relevant estimation of the B matrix. We then performed two experiments for the period October 3 October 3: for the first one, for each analysis the appropriate B matrix is used as background-error statistics, whereas in the other the 3 h B matrix is used instead. The results of these experiments (not shown) are very close, no matter which parameter is looked at; precipitation-score differences, for example, are significantly equal. To assess the sensitivity of the AROME system to the initial background-error covariance matrix further, two AROME NWC experiments were performed over the same period of time, one using a reference B (REF) and the other with 9 B instead (XP). The multiplicative coefficient 9 was chosen in order to overestimate background errors by a large amount; this coefficient is usually set to values smaller than 3. These matrices were used for every time of day, independently of the lead time the background file. The difference in terms of root-mean-square error (RMSE) against both temperature at m (Tm) and m wind observations can be seen in Figure (i.e. (RMSE(XP) RMSE(REF))/RMSE(XP)) for the forecasts issued from the previous analyses. First it can be seen that, at the analysis time, the difference is huge (around %), but then after h this difference is much lower: it stays between 5 and %. These forecast-score differences are not negligible at all and prove that REF performs better, but this also shows that a huge difference in term of B statistics results in a much smaller difference in terms of objective score. Therefore, the sensitivity experiment of using a 3 h B or a B computed from an ensemble of forecasts ranging from + to +9 h does not show any impact, due to the much larger uncertainties that lie in the data assimilation system and in the way the background-error statistics are computed. There is much more variability due to the chosen sampling period of time for the statistics computation, for example. As for the observation errors, which consist of instrumental and representativeness errors, the same values as in AROME are used. Figure 3. Operational AROME NWC system architecture. The top and bottom lines represent the AROME cycle and the light grey arrows below the top line correspond to the lateral and initial boundary conditions. The middle line shows the starting hour of successive AROME NWC runs. Once a day, a surface initial condition is obtained from AROME France (dashed black line). c The Authors. Quarterly Journal of the Royal Meteorological Society Q. J. R. Meteorol. Soc. : 3 (5)

4 L. Auger et al. (a) (b) Pressure Pressure...8. σ(q) (g kg ) σ(t) (K) (c) (d) Pressure Pressure 3 σ(div) ( 5 s ) 3 σ(vor) ( 5 s ) Figure. Background-error standard deviation for (a) humidity, (b) temperature, (c) divergence and (d) vorticity as a function of vertical pressure. The statistics are shown for the 3 h (solid lines) and h (dashed lines) B matrices Wave number 3h B ensemble h B ensemble Figure 5. Background-error standard deviation for vorticity at 85 hpa as a function of wave number for the 3 h (solid lines) and h (dashed lines) B matrices. Figure. Difference in terms of RMSE against m wind observations ((RMSE(XP-obs) RMSE(REF-obs))/RMSE(XP-obs)... The short cut-off impact The cut-off time selection is crucial: on the one hand the forecast model needs as many observations as possible to perform well, but on the other hand the forecasters, especially in a nowcasting context, need to have the model outputs as soon as possible. For the AROME NWC configuration, the aim is to have the model deliver its forecasts 3 min after the analysis time. To do so, the cut-off time was set to 5 min. Even with the analysis time window being ( 5 min, +5 min), that cut-off time value is rather short compared with the usual cut-off time for the AROME configuration: 3 h (associated with a ( 9 min, + 9 min) time window). The observations loss of AROME NWC is assessed by comparing the number of data received in the information processing centre with a 5 min cut-off and with an infinite cut-off time (Figure 7). Some observations are little affected by the short cut-off time: radar reflectivites and Doppler winds, screen-level observations, profilers. As a matter of fact, those data coming from the French observing network are processed rapidly. The geostationary satellite radiances also have a short delivery time. As for the aircraft-based observations and low-orbit satellites, roughly half of these data manage to arrive on time. Radiosondes and GPS are too late to be used in the assimilation step. This could be an issue, since those data are considered to be a useful source of information for models. The numbers in Figure 7 are obtained from averaging over a few days and over all analysis times. The actual observation number used can vary during the day; this is particularly true for aircraft-based data or low-orbit satellites. This observation diagnostic is performed over a period of time that was characterized by numerous rain events, hence the weight of the radar-based observations. Since there is more interest with AROME NWC in the boundary layer rather than free atmosphere initialization, it is expected that screen-level observations or radar reflectivities have a larger influence compared with radiosondes, since those former observations induce a larger modification inside the boundary layer. This is consistent with what has been shown in Montmerle (): the B matrix correlation length-scales are smaller inside the boundary layer than in the free atmosphere. Fortunately, most of the observations that have a large impact inside the boundary layer, such as radar and screen-level observations, have the shortest delivery time, whereas radiosondes and most satellite radiances can only be used at least h after the observation time. c The Authors. Quarterly Journal of the Royal Meteorological Society Q. J. R. Meteorol. Soc. : 3 (5)

5 AROME NWC: A New Nowcasting Tool FAR... no hydrometeor coupling hydrometeor coupling Figure 7. Number of observations used in AROME NWC assimilation for different categories, for a 5 min cut-off (black) and no cut-off (grey)..5. Importance of the screen-level observations Recent studies highlight the importance of screen-level observations ( m temperature and relative humidity, m wind) together with radar data and aircraft data (Brousseau et al., ), particularly as regards boundary-layer forecast quality. Consequently, it is hoped that in spite of the AROME NWC short cut-off, since most of the radar and screen-level observations are still available, the analysis and the following forecasts will remain of good quality. Since short forecast ranges are targeted in AROME NWC, care must be taken to use near-soil observations properly. An important issue is their strong relationship with the soil temperature and water content. The 3DVar algorithm involves the use of an observation operator. In the case of temperature at m (Tm), not taking into account the horizontal interpolation, this operator can be written as T m (T s, T n )(T s is the surface temperature and T n is the model temperature on the level closest to the soil), i.e. the operator is a function of both T s and T n. During the minimization stage, the 3DVar algorithm somehow tries to fit the control variables towards the observations by minimizing a cost function, which contains as one of its term the difference Y T m (T s, T n ) (Y is the observation value). However, since T s is not a part of the control variable, using that observation in that context might be problematic. It could, for example, result in too strong an impact of the observation on the parameter T n in the control variable, since the minimization stage tries to draw the observed values towards the modelled ones by modifying the values of the control variables. To overcome this difficulty, as in Ingleby (), the solution in AROME is to consider instead a pseudo-temperature observation on the last model level, at the same horizontal location, and with an increment (that is to say observations minus model equivalent) that has the same value as the one of the real observation at m. That solution was implemented in AROME and it allows a positive impact of Tm and Hum without having the spurious behaviour that was sometimes observed during the night. This way of dealing with Tm and Hum is an important feature for AROME NWC, since no specific surface parameter assimilation is performed... Cycling of hydrometeors The AROME NWC assimilation system only deals with dynamical variables (vorticity, divergence, temperature, surface pressure) and humidity. Many operational systems only used those prognostic variables until the beginning of the s and as a consequence their data assimilation systems relied only on those variables. Today, weather forecast models also use prognostic hydrometeor variables in their microphysics scheme, in order better to represent all the wet processes of the atmosphere (Heymsfield and Donner, 99; Lean et al., 8; Hong et al., Threshold (mm h ) Figure 8. False-alarm rate for precipitation events during the first forecast hour as a function of the threshold (in mm per hour) and for two experiments: without any hydrometeor coupling (plain line) and with hydrometeor coupling (dotted line). ). The difficulty in the initialization process of hydrometeors is that their correlation with other variables depends a lot on the meteorological situation. As a result, the climatological background correlations between hydrometeors and other parameters cannot be properly described and it is difficult to obtain a balanced state of the atmosphere directly using hydrometeors. As a consequence, the analysis step of AROME NWC does not modify their concentration and, instead of starting from empty fields, a preceding AROME NWC forecast valid for the same time is used to provide initial hydrometeor values. The benefit of hydrometeor cycling compared with starting from empty files was assessed over a seven-day rainy period. Both the false-alarm ratio (Figure 8) and probability of detection (not shown) of the forecast precipitation in comparison with raingauges during the first hour are improved for the experiment with hydrometeor cycling. Diagnostics have also been performed to assess whether the hydrometeor variables are all rained out during the first time steps. It appears this is not the case: a slight decrease of the global hydrometeor quantity of less than % can be observed during the first five time steps, corresponding to a balancing of the different water species, but this balancing is small in magnitude and can be neglected. 3. AROME NWC validation 3.. Objectives scores An AROME NWC experiment was conducted on 3 convective cases that occurred within a three-month period (April, May, June ). These 3 cases comprise various precipitation situations that took place over France, some are linked with large-scale westerly flows producing stratiform precipitation. At the end of the period, the cases are more convectively driven heavy rain situations, with some so-called Mediterranean situations: south winds coming from the Mediterranean Sea resulting in important rainfall accumulations over the southeast of the country. The methodology used hereafter to assess AROME NWC performance consists of measuring the distance between the forecast and the verifying observations for both the experiment and the AROME operational model. That means that the AROME forecast that is available at the same time in an operational context is used as a comparison. Table shows the difference in forecast ranges between the AROME NWC forecast and the latest AROME forecast valid for the same time in an operational context. The better performance of AROME-NWC over AROME is due to this time difference that lies between and 9 h. In the following graphs, the output c The Authors. Quarterly Journal of the Royal Meteorological Society Q. J. R. Meteorol. Soc. : 3 (5)

6 8 L. Auger et al. Table. Connection between the AROME NWC initial forecast time and the term of the AROME forecast used as a background Analysis time Difference of forecast range Analysis time Difference of forecast range Figure. m humidity RMSE score (in % humidity) difference between the AROME forecast and the AROME NWC forecast as a function of the analysis time of the AROME NWC forecasts (x-axis) and forecast range (y-axis). Grey shaded areas indicate negative values (AROME NWC worse than the reference) Figure 9. m temperature RMSE score (in K) difference between the AROME forecast and the AROME NWC forecast as a function of the analysis time of the AROME NWC forecasts (x-axis) and forecast range (y-axis). Grey shaded areas indicate negative values (AROME NWC worse than the reference). frequency is every hour, hence when a data point is given at the forecast range it means at the analysis time and it has no forecasting potential. The scores shown hereafter are RMSE differences from observations. In Figure 9, the difference in scores between AROME NWC and AROME shows a general improvement of the nowcasting system for temperature scores. For most of the forecast ranges and most of the analysis times, the nowcasting experiment is closer to the observations. The gain observed at the analysis time is still present after h and can persist for a few hours during the day. At night-time, especially for analysis times, 3,, 5 and UTC, the improvement is small and AROME is even better for some time ranges. This effect is certainly linked with the smaller representativity of boundary-layer observations during stable night-time conditions. Since the additional observations of AROME NWC mainly consists of boundary-layer data (since these observations are available every hour), as a result the 3 h cycle of AROME, which discards two-thirds of those observations compared with AROME NWC, has a forecast skill that is not very different during the night-time. It is interesting to note the difference between the 9 and the UTC AROME NWC runs: the former shows a larger improvement because the difference in terms of forecast range is maximum (9 h), whereas for the latter the small improvement corresponds to a h forecast range difference. The same score, but for humidity at m, looks like the score for temperature (Figure ). The improvement of AROME NWC is important during the day up to a few hours. The better AROME NWC forecasts during the day in terms of temperature and humidity inside the boundary layer are mostly related to the data assimilation of more recent screenlevel temperatures, as already explained. It should be noted that screen-level observations are not used at night in some operational assimilation systems, due to their lack of representativity. Figure. m wind force RMSE score difference between the AROME forecast and the AROME NWC forecast as a function of the analysis time of the AROME NWC forecasts (x-axis) and forecast range (y-axis). Grey shaded areas indicate negative values (AROME NWC worse than the reference). Figure shows wind force improvement, mostly during the day. At night and beyond 3 h, AROME NWC wind force forecasts are in general slightly worse. It is worth noticing that for the runs from 9 9 UTC the positive difference of the RMSE score of AROME NWC over the reference decreases between the analysis and the first forecast hour, then, although small, is stable up to 7 h. The wind direction scores (Figure ) have the same tendency. The rain prediction performance is one important feature for model assessment. Limited-area models are more and more useful in mesoscale meteorological forecasts, where emphasis is placed on events that might produce hazards such as thunderstorms and flooding. Moreover, precipitation scores give useful information on the overall model performance in the troposphere, since rain is the result of complicated three-dimensional physical and dynamical phenomena. Rain scores were made by comparing model h rain accumulation and the same accumulation from rain-gauge observations. The rain-gauges (more than 8 over c The Authors. Quarterly Journal of the Royal Meteorological Society Q. J. R. Meteorol. Soc. : 3 (5)

7 AROME NWC: A New Nowcasting Tool Figure. m wind direction RMSE score difference between the AROME forecast and the AROME NWC forecast as a function of the analysis time of the AROME NWC forecasts (x-axis) and forecast range (y-axis).greyshadedareas indicate negative values (AROME NWC worse than the reference). 8 Figure 3. Brier Skill Score for h rainfall accumulation; the difference between the AROME forecast and the AROME NWC forecast as a function of the analysis timeofthe AROME NWCforecasts (x-axis) andforecastrange (y-axis) is shown. the model domain) are issued from the French operational raingauge network. The relative performance of the model over the central part of the domain was measured. The method used is a version of the Brier Skill Score (hereafter BSS): BSS(s) = (P(X i > s) F(Y i > s)), A X i A where P(X i > s) is the probability that the modelled rain accumulation X i is bigger than a certain threshold s, F(Y i > s) is the observed frequency for the rain observation Y i to be bigger than the same threshold s and the X i and Y i are computed on a partition A of the domain. To compute these probabilities, boxes of km were used. Figure 3 presents BSS for precipitation over the same period of time as previous scores. Here the BSS is summed over every threshold, the sum being weighted with the number of observations. The aim of this weighting is to give more importance to the small thresholds for which, due the larger number of events, the uncertainty is smaller. The comparison is made with the operational AROME cycle, which performs a long-term forecast every h. The improvement is to be seen mostly during the day, from the 9 UTC forecast until 8 UTC. In contrast, during the night and in the morning, AROME NWC is often worse than AROME beyond h but still performs well for the first h. It seems that once the convection is triggered, AROME NWC manages to improve the rain forecast because of the relevant wind and humidity from all the most recent observations such as radar reflectivities and wind, screen-level observations. Consequently the most recent data assimilation process of AROME NWC will result in a better rain forecast up to a few hours. In contrast, at night-time there are fewer rapidly changing rain events and so no relevant information is to be found in the most recent observations and the older AROME forecast can compete with AROME NWC. The probability of detection (POD) and falsealarm ratio (FAR) have also been looked at (not shown) and are coherent with BSS scores. When averaged over each initial time as a function of the rain threshold, the weight of the night situations is limited and the same tendency can be observed: both the FAR and POD are improved for every threshold. The different scores prove that AROME NWC improvement compared with AROME is more obvious for temperature, humidity and wind in the surface layer than for precipitation. This might be explained by the types of extra observation used by the AROME NWC system: mostly observations inside the surface layer. The improvement of precipitation even for short time ranges also needs a good description of the free atmosphere and there is a lack of observations representative of these layers available for AROME NWC. Note that no performance drop is observed when new AROME runs are available as new reference runs (at and 5 UTC); indeed, a decrease in score exists but is limited. 3.. Case studies It has been shown that AROME NWC provides forecasts closer to temperature, humidity and wind inside the boundary layer. As a nowcasting tool, AROME NWC is meant to be used to anticipate heavy rain events and consequently must also be evaluated with case studies. First, the 9 April case is examined. This situation is characterized by a low-pressure centre located over western France with a trough positioned a bit further west over the Atlantic ocean. Low-level warm air from the Mediterranean Sea is advected northward and spreads over eastern France, whereas cold air is advected over the western part of France and the Atlantic Ocean. This meteorological situation produces wind and precipitation for a significant part of France. This large organized system is typical of convective situations that might produce hazards. For this type of large-scale driven situation, AROME NWC might not perform better than the older AROME forecast. Figure shows a comparison between AROME NWC and AROME in terms of h rainfall accumulation. For AROME NWC, the accumulation is between the and h forecasts, starting at 5 UTC. For AROME in an operational context, only the forecast at UTC is available: consequently, the h accumulation from until 7 UTC will be the reference. Rain-gauge observations are used to assess the performance of both models. In the northern part of the country (7 N 5 N, E E), the rainy area is larger and more intense according to the observations. The rainy patch that follows the Rhone valley ( N N, E) is also closer to observations in the AROME NWC model. Another rainy patch over the Jura mountain range (.5 N, E) is present in the AROME NWC forecast, in agreement with the observations, whereas it is absent for AROME. For some other part of the country, thanks to its more recent data assimilation, AROME NWC seems to have a better field initialization and consequently provides a better h rainfall accumulation. In some locations like the Paris area (9 N, E) neither forecast performs well. For other cases the c The Authors. Quarterly Journal of the Royal Meteorological Society Q. J. R. Meteorol. Soc. : 3 (5)

8 L. Auger et al. (a) (b) (c) Figure. One-hour rainfall accumulation from 7 UTC, 9 April for (a) the AROME NWC 5 UTC run, (b) the AROME UTC run and (c) observations from rain gauges. impact is sometimes less obvious, but, as shown in Figure 3, the average BSS is better for AROME NWC for most of the forecast leading times and for most of the starting hours. The impact in Figure 3 is due to the assimilation of the radar and m relative humidity observations, which produced a humidification of the boundary layer and lower troposphere at relevant places, resulting in an intensification of rain. At some other places, the model background state was dried following the method used in radar reflectivities (this technique dries the lower atmosphere, where no rain is observed and where rain is present in the model, as explained in (Wattrelot et al., ). A comparison with the SIGOONS system is also worthwhile. Two situations will be looked at: the 7 June 3 case, for which the synoptic situation is similar to the previous case, with warm and moist air being advected from the sea, triggering convection over the domain; and the May 3 case, for which a temperature gradient with cold air from the north advected into the western part of the domain and warmer air from the south coming into the eastern part favours heavy precipitation over a large part of the country. Figure 5 shows the SIGOONS contours, which are h and 5 min extrapolations based on radar observations, compared with 3 h AROME NWC forecasts to simulate an operational use, with AROME NWC being available 5 min after the analysis time and so being compared with extrapolations of observations (at analysis time + 5 min). The contours correspond to observed or simulated 35 dbz radar isolines. The 7 June case clearly shows a situation where AROME NWC is beneficial: the convective line that is initiated in the southwestern part of the country is scarcely seen by the SIGOONS method, since it is based on extrapolation, whereas AROME NWC manages to initiate convective systems that are quite well-positioned as regards observations. As for the May situation, the impact of AROME NWC is less beneficial: AROME NWC manages to improve the SIGOONS extrapolation in the southwestern part of the country, whereas in the northeastern part both SIGOONS and AROME NWC have difficulties in localizing the highly convective areas with precision. Even if it is difficult to say how representative each of the above situations is, out of seven strongly convective days that were looked at carefully, for at least four AROME NWC was proven to be useful, at least during the initiation phase of the convection.. Summary and conclusion The AROME NWC nowcasting system has been developed in order to cover the nowcasting h range. It is based on the existing AROME mesoscale model, from which the background and lateral boundary conditions are taken. The difference between those two systems is basically the observation window length and a very short cut-off time. The choice not to cycle the prognostic variables has been made in order to keep the system simple and to get rid of the potential spin-up issues. Since this configuration runs with the same grid size, the physical settings are the same as AROME. The surface prognostic variables are refreshed only once a day, then are cycled from one run to the other, since soil parameters are less affected by spin-up issues and do not need to be refreshed with observations so often. Studies and testings have been carried out that show that it is not necessary to use a B matrix dependent on the lead time of the background; it is more important to use a large enough ensemble with forecasts starting at different times. The spin-up of the system has been proven to be small enough that the forecasts can safely be used beyond the first min. The cut-off time of 5 min seems not to deteriorate the quality of the forecasts too much while enabling a c The Authors. Quarterly Journal of the Royal Meteorological Society Q. J. R. Meteorol. Soc. : 3 (5)

9 AROME NWC: A New Nowcasting Tool (a) (b) Figure 5. Comparison between SIGOONS and AROME NWC. The SIGOONS contours (red lines) are the h and 5 min extrapolations of the radar reflectivity observations. The green contours are issued from a 3 h AROME NWC forecast valid for the same time. In brown, the same contours based on radar observations can be seen as a reference. Each contour represents the 35 dbz reflectivity isolines and is a marker of intense convective activity. For panel (a), the validity time is 7 June 3 at UTC; for panel (b), the validity time is May 3 at 8 UTC. reasonable delivery time for the nowcasting. The model run lasts 5 min and the forecasts are available 3 min after the analysis time. Due to its short cut-off time, AROME NWC can be seen as the production cycle of the AROME model. It is important to note that proper handling of screen-level observations must be performed, since the impact of these observations is even more important for nowcasting. The AROME NWC system has been validated compared with the operational AROME system operating on the same domain. A global improvement in terms of temperature, humidity and wind has been observed for most of the starting hours up to a few hours. For precipitation, subjective scores seem not to be as good as expected, with an improvement during the day and degradation for some hours during the night. For each parameter evaluated, the use of more recent observations is more beneficial during the daytime, in association with more rapidly changing weather conditions. A case study with a significant rain forecast improvement has been shown, for which the nowcasting system helped in correcting rain patches that had been underestimated by the reference forecast. Two other examples in two convective cases show that AROME NWC was valuable, especially during convection initiation, compared against the purely radar-based SIGOONS nowcasting tool. To conclude, it has been proven that a nowcasting system such as AROME NWC based on a mesoscale system but with some simplifications (no cycling) can still perform better for nowcasting ranges, due to its use of more recent observations. References Brousseau P, Bouttier F, Hello G, Seity Y, Fischer C, Berre L, Montmerle T, Auger L, Malardel S. 8. A prototype convective-scale data assimilation system for operation: The Arome RUC, HIRLAM Technical Report 8, SMHI, Norrköping, Sweden, 3 3. Brousseau P, Berre L, Bouttier F, Desroziers G.. Background-error covariances for a convective-scale data-assimilation system: AromeFrance 3D-Var. Q. J. R. Meteorol. Soc. 37: 9. Brovelli P. 5. Nowcasting thunderstorms with SIGOONS a significant weather object oriented nowcasting system. Proceedings World Symposium Nowcasting. Météo-France (ed): Toulouse, France. Dixon M, Wiener G Titan: Thunderstorm identification, tracking, analysis, and nowcasting A radar-based methodology. J. Atmos. Oceanic Technol. : Fischer C, Auger L.. Some experimental lessons on digital filtering in the Aladin France 3DVAR based on near-ground examination. Mon. Weather Rev. 39: Fischer C, Montmerle T, Berre L, Auger L, Stefanescu S. 5. An overview of the variational assimilation in the ALADIN/France numerical weather-prediction system wave-driven circulation of the mesosphere. Q. J. R. Meteorol. Soc. 3: Germann U, Zawadzki I.. Scale-dependence of the predictability of precipitation from continental radar images. Part I: Description of the methodology. Mon. Weather Rev. 3: Golding BW, Ballard SP, Mylne K, Roberts N, Saulter A, Wilson C, Agnew P, Davis LS, Trice J, Jones C, Simonin D, Li Z, Pierce C, Bennett A, Weeks M, Moseley S. 3. Forecasting capabilities for the London Olympics. Bull. Am. Meteorol. Soc. 95: Heymsfield AJ, Donner LJ. 99. A scheme for parameterizing ice-cloud water content in general circulation models. J. Atmos. Sci. 7: Hong SY, Lim KSS, Lee YH, Ha JC, Kim HW, Ham SJ, Dudhia J.. Evaluation of the WRF double-moment -class microphysics scheme for precipitating convection. Adv. Meteorol. :. Ingleby B.. Global assimilation of air temperature, humidity, wind and pressure from surface stations. Q. J. R. Meteorol. Soc., doi:./qj.37. Lean HW, Clark PA, Dixon M, Roberts NM, Fitch A, Forbes R, Halliwell C. 8. Characteristics of high-resolution versions of the Met Office unified model for forecasting convection over the United Kingdom. Mon. Weather Rev. 3: Li L, Schmid W, Joss J Nowcasting of motion and growth of precipitation with radar over a complex orography. J. Appl. Meteorol. 3: 8 3. Lynch P, Huang XY. 99. Initialization of the HIRLAM model using a digital filter. Mon. Weather Rev. : 9 3. Mecklenburg S, Joss J, Schmid W.. Improving the nowcasting of precipitation in an Alpine region with an enhanced radar echo tracking algorithm. J. Hydrol. 39: 8. Menzel WP, Holt FC, Schmit TJ, Aune RM, Schreiner AJ, Wade GS, Gray DG Application of GOES-8/9 soundings to weather forecasting and nowcasting. Bull. Am. Meteorol. Soc. 79: Montmerle T.. Optimization of the assimilation of radar data at the convective scale using specific background-error covariances in precipitation. Mon. Weather Rev. : Rasmussen R, Dixon M, Hage F, Cole J, Wade C, Tuttle J, McGettigan S, Carty T, Stevenson L, Fellner W, Knight S, Karplus E, Rehak N.. Weather support to deicing decision making (WSDDM): A winter weather nowcasting system. Bull. Am. Meteorol. Soc. 8: Rockel B, Will A, Hense A. 8. The Regional Climate Model COSMO CLM (CCLM). Meteorol. Z. 7: Saito K, Fujita T, Yamada Y, Ishida J, Kumagai Y, Aranami K, Ohmori S, Nagasawa R, Kumagai S, Muroi C, Kato T, Eito H, Yamazaki Y.. The operational JMA nonhydrostatic mesoscale model. Mon. Weather Rev. 3: 98. Seity Y, Brousseau P, Malardel S, Hello G, Bénard P, Bouttier F, Lac C, Masson V.. The AROME France convective-scale operational model. Mon. Weather Rev. 39: Sun J, Xue M, Wilson JW, Zawadzki I, Ballard SP, Onvlee-Hooimeyer J, Joe P, Barker DM, Li PW, Golding B, Xu M, Pinto J. 3. Use of NWP for nowcasting convective precipitation: Recent progress and challenges. Bull. Am. Meteorol. Soc. 95: 9. Wattrelot E, Caumont O, Mahfouf JF.. Operational implementation of the D+3D-Var assimilation method of radar reflectivity data in the AROME model. Mon. Weather Rev. : Wilson JW, Crook NA, Mueller CK, Sun J, Dixon M Nowcasting thunderstorms: A status report. Bull. Am. Meteorol. Soc. 79: c The Authors. Quarterly Journal of the Royal Meteorological Society Q. J. R. Meteorol. Soc. : 3 (5)