Nimrod: A system for generating automated very short range forecasts

Transcription

1 Meteorol. Appl. 5, 1 16 (1998) Nimrod: A system for generating automated very short range forecasts B W Golding, Meteorological Office, London Road, Bracknell, Berkshire RG12 2SZ, UK A very short range forecasting system has been developed which integrates nowcasting techniques with Numerical Weather Prediction (NWP) model products to provide forecasts over the UK and surrounding waters up to six hours ahead. There are three main components, producing analyses and forecasts of precipitation, cloud and visibility, respectively. The precipitation rate analysis uses processed radar and satellite data, together with surface reports and NWP fields. The forecast is based on an object advection technique, modified for growth and decay using model products. Related variables, such as precipitation type, are also diagnosed using the NWP fields. The cloud analysis is based largely on satellite imagery and surface reports, the forecast being carried out in a similar way to precipitation rate. The visibility analysis combines surface reports with NWP model fields and satellite imagery: Meteosat during the day and NOAA AVHRR at night. The forecast is an extrapolation using trends from the NWP model, and relaxing towards the model values themselves. Results show a substantial improvement over both persistence and raw NWP model products. 1. Introduction Weather prediction may be divided into two time frames, according to the relationship between the length of forecast and the characteristic timescale of the process of importance. For periods short compared with this characteristic timescale, attention is focused on obtaining an accurate picture of the present situation, and then deducing the future from this state. For much longer lead times, the weather distribution is seen as a resultant of the changing atmospheric state, and so the forecasting of that state is the primary task. Traditional synoptic meteorology recognises this in developing very short range forecasts of rain, fog, gales, etc. as an extrapolation of observed trends in these variables, while for longer forecasts, it uses quasi-geostrophic development coupled with air mass analysis to predict the atmospheric state, and then infers the distribution of rain, cloud and wind. In both cases, the final forecast for a given location will incorporate knowledge of the influence of local site characteristics. Manual forecasting could, therefore, be described as a judicious combination of three elements: extrapolation, large-scale development, and local climatology. These components can also be found in the automated techniques developed in recent years. Thus, nowcasting techniques are usually focused on analysis and extrapolation of the trend of a single variable, for instance the rain distribution observed by a radar (Austin & Bellon, 1974), while numerical weather prediction (NWP) resolves the larger, slower evolving, scales, and the local detail is filled in by parameterisation or statistics. Between nowcasting and NWP, an information gap has been recognised (e.g. Browning, 1982). However, this has sometimes been misinterpreted as implying a reduction in accuracy with reduced lead time and so, to avoid this, an alternative representation is given in Figure 1 following Austin et al. (1987). The downward trend in information content of the theoretical limit of predictability reflects the fact that in a chaotic system like the atmosphere, there is an inevitable loss of information as one looks further into the future. The potential information content will usually relate to larger scales as the forecast range becomes longer. For a nowcasting method, the initial information capture is perfect, or nearly so, but without representation of Figure 1. Schematic representation of the loss of information content in forecasts as a function of lead time. The solid line represents the theoretical limit of predictability. The dashed line represents NWP models and the dotted line nowcasting methods. 1

2 B W Golding atmospheric physics, the information loss is very rapid with forecast time. By contrast, limited resolution and imperfect assimilation algorithms result in a relatively poor representation of the observed state in NWP models. This, however, deteriorates only slowly in the early part of the forecast owing to the good representation of larger scales, and then parallels the perfect solution, the difference reflecting the imperfect representation of physics, and the residual effect of distortion of small scales in the initial representation of the atmosphere. During the 1980s, the UK Met. Office developed two systems which narrowed this gap. Firstly, by compositing radar data, extending their range using satellite imagery, and then using time dependent NWP model wind fields to advect the rain, it was shown that nowcasting techniques could provide skilful forecasts over longer periods than linear extrapolation of data from a single radar (Conway & Browning, 1988; Collier, 1992; Brown et al., 1994). Secondly, a mesoscale NWP model was developed to bring numerical modelling to bear on the short range forecasting problem (Golding, 1990). In order to initialise the model at such fine scales, techniques were developed to make use of satellite and radar imagery. In both of these systems, it was recognised that the use of such data posed new interpretation problems which were best handled, initially, within interactive man machine processing facilities. For the nowcasting system, FRONTIERS (Forecasting Rain Optimised using New Techniques of Interactively Enhanced Radar and Satellite) was developed (Browning & Collier, 1982), and for the model, the Interactive Mesoscale Initialisation system (Wright & Golding, 1991). Other centres have taken different development paths, particularly where the short range forecasting requirements are rather different. This is particularly marked in areas such as central USA with a high frequency of severe convection, for which even very short warning times can be valuable. There, the development path has been characterised by separate tools under forecaster control, each dealing with a particular part of the forecast problem the establishment of broad warning areas from model and/or geostationary satellite data using expert systems (e.g. Colquhoun, 1987; Lee & Passner, 1993), identification of incipient storms from satellite, radar and lightning data (e.g. Wilson & Mueller, 1993; Que et al., 1993), tracking and extrapolation of radar imagery, identification of severe features in radar imagery, etc. Numerical modelling of mesoscale features has not received priority in such centres because the available computing power has not been sufficient to resolve the important features and the delay between observation and forecast has been too great for the lead times of interest. The system described in this paper aims to address directly the divide between nowcasting and NWP 2 model guidance by integrating the two techniques in a single automated system for forecasting the main weather variables over the UK up to six hours ahead. This integration can be seen as an extension of the work of Collier (1992) and Makihara (1993) to incorporate NWP wind fields in nowcasting. It also adopts the selfoptimising approach of Gollvik (1987). The incorporation of NWP model information on growth and decay is an alternative but parallel approach to that taken by Lee & Georgakakos (1990). Finally, the method of weighted combination of independent predictions draws on the work of Fraedrich & Leslie (1987). The system does not include preparation of site-specific forecasts, and therefore makes limited use of local climatology. At the scales considered, mesoscale NWP models provide forecasts close to the representativeness error for pressure, wind and temperature, and so the present work concentrates on precipitation, cloud and visibility, which are much more poorly represented by available NWP models. The nowcasting and NWP techniques are fully integrated at all stages of processing and all the main variables are coupled. The system is also fully automatic, allowing lead times to be minimised. This potentially offers users of weather forecasts a large increase in available information with much less delay from observation time. For users requiring the highest quality achievable, it also means that manual quality control can be concentrated on the critical events. 2. The Nimrod system The analyses and forecasts are generated over a fixed domain (Figure 2) on the UK National Grid (a transverse Mercator projection with vertical longitude at 2 o W). The visibility and precipitation products are generated on a 5 km grid. The cloud and most of the derived products use a 15 km grid. Grid box centres are collocated on the two grids. Cloud and visibility analyses and forecasts are updated hourly, with forecasts generated half-hourly to 6 hours ahead. The precipitation component, however, uses a 15 min cycle with forecasts also generated in 15 min intervals. The NWP model input is updated every 6 hours at present, the new data becoming available about 3 hours after data time. Although this leads to a wide variation in the age of model input, the average model quality varies only slightly in the relevant range of lead times, and so there has been little justification for the substantial cost of additional runs. This will undoubtedly change in the future, particularly with the implementation of finer model resolution. The availability time of individual products is recorded in the relevant section below. The system is implemented on a HP UNIX workstation, with the code written in FORTRAN 77.

3 Automated very short range forecasting Figure 2. The Nimrod domain and 5 km resolution orography. Most of the optimisation has been automated, with hand analysis of only the most demanding routines. Given suitable data sources, the code is portable to most UNIX platforms with only minor modifications. System-specific interfaces are provided to the satellite and radar imagery, to a local database for synoptic data, and to the NWP model output files. The hardware is duplicated, and is able to switch and restart automatically. Data are not duplicated, and so an emergency switchover causes the loss of a single forecast cycle. Internally, the code is structured to ensure reliable product delivery even when components are unavailable. Thus, the precipitation analysis/forecast availability currently runs at over 99%, well in excess of the availability of any individual radar. The code is highly modular, with each program unit typically generating only one product or a group of closely related products. The program sequence is controlled by UNIX scripts under time control. Figure 3 shows the logical sequence of processing (the real processing sequence is more complex to allow for the availability of raw data, and to deal with the reversed time arrows). The system is divided into five major components: observation processing (satellite and radar), NWP (assimilation and prediction), data blending, merged forecast and product generation. The data blending and merged forecast components are replicated for each of the core weather variables: precipitation, cloud and visibility, the remaining variables being obtained by processing of the merged and model forecasts. Figure 3. Logical processing structure of the Nimrod system. Dashed arrows denote NWP related data flows. The main sources of observations are satellite imagery, radar imagery and surface synoptic reports. Upper air information is obtained indirectly through the NWP assimilation procedure. Satellite imagery is received from both geostationary (Meteosat) and polar orbiting (NOAA) platforms. However, owing to the limited number of overpasses by the polar orbiters, they are currently only used for nighttime fog discrimination using the 3.7 µm channel not available on Meteosat. Once the Meteosat Second Generation (MSG) spacecraft is in operational use (currently scheduled for 2001), the requirement for polar orbiter data will further diminish due to the enhanced imaging capabilities planned (e.g. Morgan, 1995). Geographical registration of Meteosat data is taken as delivered from EUMETSAT. The error is typically 1 pixel (~8 km), although larger errors occur occasionally. Allowance for this is made where it may be critical. Images are resampled to the Nimrod 5 km grid prior to further processing. It is anticipated that some processing would benefit from being performed in space-view coordinates and this will be investigated in the future. Radar imagery is pre-processed at the radar site to calibrate the observed reflectivity, to remove fixed clutter and to re-map to a common 5 km grid on the UK National Grid projection. The methods of NWP for initialising and modelling the atmospheric state are too complex to discuss here. The 3

4 B W Golding Met. Office uses its Unified Model (Cullen, 1990; Lorenc et al., 1991) for all NWP and climate research applications, thus obtaining maximum benefit from the modelling expertise and diagnostic study in these different areas. For short range forecasting, the version currently in use has a grid spacing of about 17 km. Forecasts to at least 18 hours ahead are updated four times a day. The grid spacing gives a true resolution of about 70 km so that, on its own, it is incapable of meeting the key requirements for local forecast data. However, its estimates of development and decay provide a valuable source of information, complementary to the more detailed extrapolation forecasts. An important feature of the initialisation of the model is that information from the rain rate and cloud cover analyses in Nimrod are used in specifying the humidity distribution (Macpherson et al., 1996; Jones & Macpherson, 1996). In particular, this helps alleviate the deficiency of rainfall in the early part of the forecast, that most NWP models suffer. By using the NWP model as the basis for analyses, and as the asymptotic state for the forecasts, overall consistency of the variables, both those generated in Nimrod and the basic model variables, is maintained. Two basic algorithms are used for data blending. The visibility and precipitation rate analyses use a twodimensional variational scheme based on a standard descent algorithm (Shurlock & Lorenc, 1994), while the cloud analysis used a two-dimensional recursive filter algorithm applied to each level (Purser & McQuigg, 1982). The difference arises from the earlier development of the cloud analysis, and the anticipated complexity of switching to a three-dimensional variational scheme. Details of the implementation for each variable are dealt with in the appropriate section. The forecast procedure consists of two main steps: computation of an extrapolation from recent values or trends, and optimal merging of this with other independent forecast estimates, usually persistence and the NWP model. The basic approach is to use a simple, linear technique for the extrapolation, and to incorporate the non-linearities of the evolution from the NWP model. For cloud and precipitation, the extrapolation forecast is computed by advecting the analysed fields using vectors optimised to recent motion. For visibility, the extrapolation forecast is obtained by applying NWP model trends to analysed temperature and humidity fields. For the merging procedure, weights are calculated dynamically, depending on current performance of the various contributions. However, the method of calculation differs in each case. Details are given in the relevant sections. The various users of very short range forecasts require different selections of atmospheric variables. In meeting these requirements, the main Nimrod variables described here are complemented by remapped NWP model fields of pressure, temperature and wind, and by 4 hybrid variables such as lightning rate, obtained by combining information from both sources. The products of very short range forecasting systems are very perishable and must be disseminated quickly to be of use. The requirements of some users, for instance the UK Environment Agency for flood control, and Local Authorities for winter road maintenance, are met by the direct provision of the automated forecasts. These users need to be aware of the performance characteristics of the system, particularly the situations in which it performs poorly. In most cases, they can seek advice on interpretation from a regional forecast centre. Other users, such as the Royal Air Force, receive forecasts through local briefing by a forecaster who is able to screen out any major errors first. 3. Assessment strategy As specific uses for system products arise, it is anticipated that assessment measures appropriate to each use will be developed. However, such uses are likely to be geographically and temporally biassed and so may not be the best basis for general measures to indicate the quality of the overall system and its potential contribution to new users. The Nimrod system therefore has integral verification systems which measure some specific aspects of quality in a general way. Firstly, timeliness and reliability are measured for all main products. Reliability is the percentage of all possible products completed, regardless of quality. For timeliness, both the average and the completion time for 95% of occasions are monitored. For accuracy, products are assessed against both point observations and analyses. Generally, assessment against point observations is only reported for analyses because the representativeness errors are of similar magnitude to the forecast errors. Assessments against analyses are performed over restricted geographical areas within which observational coverage is considered good. Thus for rainfall, the radar coverage is used, while for cloud and visibility, the land area of the British Isles is used. For comparison with analyses, the appropriate scale for verification has been considered in the light of the results of Bellon & Zawadzki (1993) and Hudlow et al. (1992). While users may wish for the best possible forecast at full resolution, the effects of aliasing in the observations and analysis and of chaotic development in the forecast, mean that assessment at full resolution is unlikely to be a good indicator of forecast quality. For many purposes, it will also not be the most important scale to the user. For general assessment of the distribution of the main variables, the scale chosen is 50 km, obtained by filtering with a 25 km half width recursive filter. For precipitation and visibility, this is

5 Automated very short range forecasting As an example of the problems of verification, Figure 4 shows a corrected radar precipitation rate map. Application of the standard verification measures was made to a degraded map, obtained by shifting the original by varying amounts. Using a translation speed of 10 m s 1, these shifts can also be related to a timing error in the forecast. The resulting error estimates are shown in Table 1. Figure 4. Composite radar image for 0000 UTC, 26 July 1996, showing a cold frontal rainband crossing southern Scotland and Ireland. modified to avoid spreading the rain/fog area, by applying the threshold first, then filtering the resulting array of delta functions, then thresholding again so as to preserve the number of selected points. Three main assessment techniques are used for the main variables. (a) A measure of skill in predicting the distribution of occurrence of an event: rain exceeding a threshold rate, visibility below a threshold, etc. The statistics used are derived from the 2 2 contingency table and are principally Hit Rate (HR), False Alarm Rate (FAR), and Threat Score (or Critical Success Index, CSI). (b) A measure of the closeness of forecast and analysis. For this purpose, a form of Root Mean Square (RMS) Error called the RMS Factor (RMSF) is used (see the Appendix). (c) A measure of the percentage of forecasts within a specified closeness to the observed/analysed value. This may be an absolute or fractional closeness. For an overall summary of performance, the RMSF error measure has been found to provide the most information when applied to precipitation rate and accumulation, cloud base height, and visibility. The unsmoothed results give a very pessimistic view of the quality of the shifted field, suggesting that it is of no more value than a random field after a 14 km shift. The smoothed distribution measures for the 7 km displacement, on the other hand, give scores that would be considered excellent as a one hour forecast. It should be noted that the 3.5 km shift is sub-pixel scale and could therefore occur solely as a consequence of aliasing error. These results are worse than would be obtained for a broad, well organised frontal rain band, but are not as extreme as for disorganised convective rain. 4. Precipitation 4.1. Observation processing and precipitation analysis Precipitation is a key component of very short range forecasts. It can give rise to several hazardous conditions, notably flooding and the disruptive and damaging effects of the various forms of frozen precipitation. There is also widespread sensitivity to the occurrence of even quite small rain rates, both in the general public, and in the construction and leisure industries amongst others. Finally, there is a requirement in activities such as exposed engineering work and fuel handling to avoid conditions when lightning might occur. The more hazardous conditions are the subject of warning systems allowing reaction at very short lead times, and so justify high temporal and spatial resolution forecasting. This is particularly so for flood control in the small urban drainage catchments that are prone to flash flooding. Here, the currently available 15 minute temporal and 5 km spatial resolution is deemed inadequate, and steps are being taken to obtain the higher resolution data currently only available at individual radar sites, so that 5 minute, 2 km resolution Nimrod products can be made available. Timeliness is also vital in this context. The Nimrod analysis is currently available at 9 minutes after data time on average, the main delay resulting from differences in the arrival Table 1. Errors arising from uniform displacement of an analysed rainfall field Spatial Timing Unsmoothed field Smoothed field RMS RMSF error error HR FAR CSI HR FAR CSI (mm hr 1 ) (km) (min) % %

6 B W Golding time of data from different radars. The full 6-hour forecast is available about 14 minutes after data time, on average. Development of the automated radar processing step has been the most important component of the work to date. All processing is carried out on single site images, before compositing. There are four main components: identification and removal of corrupt images; identification and removal of spurious echoes; correction of echo intensity for range, bright band contamination, and orographic enhancement below the radar beam; and gauge calibration. The result is a best estimate of the surface rainfall rate distribution in the area observed by the radar. Development of an automated technique for removing corrupt images using pattern recognition has been rejected, for the present, in favour of an approach based on the observation of Cheng & Brown (1993) that the area-averaged rain rate is highly correlated with the fractional exceedance of a threshold rate. This is currently applied so that images are rejected with average rain rates exceeding 20 mm h 1, and a warning is raised for those exceeding 5 mm h 1. Spurious echoes within an image are currently identified and removed using a Bayesian approach with three predictors based on satellite imagery, surface synoptic reports and a forecast. Surface present weather and cloud reports are interpreted as either supporting or contradicting a wet pixel, and are applied with an exponential range-dependent weight whose decorrelation length is specified according to the report code. Satellite imagery is used in two ways. Firstly, cloud free areas are identified by comparing the infra-red (IR) radiance temperature with the model surface value, and, when available, comparing the visible image, normalised for solar elevation, with a climatological surface reflectivity. Cloud free areas are given a very high probability that wet pixels are spurious. Then for cloudy areas, the probability of a wet pixel being spurious is estimated using the satellite IR radiance temperature and the normalised visible reflectivity, when available, according to the climatological occurrence obtained by Cheng et al. (1993). Bayes theorem is applied sequentially to combine these sources of information as described in Pamment & Conway (1996). Finally, a threshold probability is set beyond which any wet pixel is diagnosed as spurious. There is considerable scope for tuning in the scheme, and this will be a continuing process since users are particularly sensitive to both the removal of real echoes, and the retention of spurious ones. 6 Correction of echo intensity is carried out with a onedimensional physical parameterisation of the reflectivity profile, calibrated by the radar observation, and used to determine the surface rainfall rate (Kitchen et al., 1994). This is a particularly good example of the integrated approach. The one-dimensional parameterisation uses cloud top from the satellite imagery, freezing level from the NWP model, reflectivity from the radar, low level humidity and wind from the NWP model and prior information on the radar beam characteristics and calibration. A by-product of modelling the intersection of the radar beam with the precipitating cloud is a position-dependent assessment of whether the radar can see any precipitation that may be present. The scheme has been further developed (Kitchen, 1996), to optimise the shape of the reflectivity profile relative to the four radar beam elevations observed by the UK network using a variational minimisation algorithm. Off-line tests were encouraging, and the scheme will be implemented when upper beam data become available centrally in real time. A key assumption in the correction process is that the radar calibration is known. Since, in reality, there is significant uncertainty, real time hourly gauge accumulations are used to provide corrections. The hourly realtime gauge data used for these comparisons are currently obtained from the dedicated networks associated with some radars, and from the UK synoptic network. Some radars, notably those on Jersey and near Stornoway in the Outer Hebrides, have few gauges close to the radar, where comparisons are most useful. However, for most, a good spread of directions and ranges is obtained. Corrections are altered on a weekly basis if there have been sufficient valid comparisons since the previous week, and if they have been sufficiently consistent in indicating the need for a significant change in the radar to gauge ratio. A valid comparison is made only when both the radar and the gauge report more than 0.2 mm in an hour. Rain gauge data are not currently used to provide spatially varying corrections since the representativeness error of each gauge is comparable with the required correction, and the physical correction method deals with the main sources of such variability. If there was a significant increase in the availability of real-time gauge data, it might, however, be worth computing a range-dependent correction, since inspection of monthly mean images indicates that several radars show such a dependence. A more detailed description of the gauge correction scheme is in Hackett & Kitchen (1995). The resulting corrected single site radar images are composited using a pre-defined preference map to form a single radar rainfall image covering most of the British Isles. Extension of the data beyond radar range and to fill in missing radars, is performed with the variational analysis technique using satellite and surface information (Wright, 1994). The satellite rain rate field is obtained from combined infra-red and visible imagery using an improved version of the FRON- TIERS radar correlation algorithm (Brown, 1995). For each of three rain rates (0.125, 0.5 and 2.0 mm h 1 ) the satellite radiances associated with radar pixels which exceed the threshold are accumulated. Theshold radi-

7 Automated very short range forecasting ances are then determined which maximise the skill in reproducing the radar distribution. These thresholds are applied to satellite images covering the whole domain to provide estimates of rain rate outside radar range Precipitation forecast The critical component of the advection forecast scheme is selection of the motion vectors. The method is described in Ryall (1994). The rainfall field is first segmented into discrete objects, separated by at least three dry pixels. Only large objects exceeding a threshold size are advected separately, the remainder being grouped with their nearest large object. Starting from vectors selected in the previous forecast cycle, a new vector is selected for each object, which optimises the correlation, for that object, when the previous hour s field is displaced. An alternative displacement is obtained by advecting the previous hour s field with a range of model wind fields, encompassing levels below 6 km and times within ±2 hours of the current time. The level and time giving the optimum correlation for each large object are again selected. The correlation measures are then used to select between the linear displacement and model wind advection techniques, and the choice for each object is stored for use in the forecast algorithm. The forecast is created by computing a forward trajectory from each initial pixel. Weighted contributions from all trajectories with an end point within one grid length are used to form the forecast value at each target point. For rates, the target values are computed every 15 minutes, but a shorter time step is used for accumulations, which depends on the advection velocity. The NWP wind fields used in the computation are updated every hour of forecast time. For precipitation, only two forecast estimates are merged, the advection forecast, described above, and the NWP model forecast. Since the model forecasts are of much lower resolution, the weights are computed empirically in a manner that ensures maximum weight to the advection forecast at short lead times. Given the correlation coefficients, C A and C M for advection and model, respectively, calculated from the previous hour, the advection forecast correlation with reality is assumed to fall exponentially with the value C A at 1 hour, and the value C 0 (=0.2) at 6 hours, while the model is assumed to have a constant value. The weights, W A, W M are then computed as follows: C dt 0[( 1) / 5] WA = exp Ln CA W C C C C M( A 0 ) M = 0 + C A where dt is the forecast time in hours. The minimum value reported in the radar images is 1/32 mm h 1, and so, for consistency, forecast values of less than this are set to zero. Verification of the precipitation fields is performed on both rates and accumulations. The analyses are assessed by integrating 15 minute rates to give hourly totals for periods which match the observing timetable and comparing the results with hourly rain gauge reports. The latter are made in either 0.1 mm or 0.2 mm increments according to the gauge specification. Statistics are computed both when analysis or observation exceeds 0.2 mm and also when either exceeds 1.0 mm. In all cases, lower values are set to 0.1 mm to avoid the implication of large errors when small amounts are being compared. Both RMS and RMSF statistics are computed for both thresholds. Results for March 1996 to February 1997, using the lower threshold, are shown in Table 2. They indicate an almost unbiased estimate with a large RMSF of In the light of Kitchen & Blackall (1992), however, it is difficult to partition this between the representativeness and analysis errors. The error component is significantly inflated by retention of spurious echoes, especially when they have high rates. Rather more than one third of comparisons are within a factor of two. Table 2. Comparison of hourly accumulations derived from precipitation analyses against point rain gauge observations Period Mean RMS Mean F RMSF (mm) (mm) Mar Feb Forecasts are verified against analyses. Distribution is compared using filtered fields of rain rate at mm h 1 threshold. Amounts are compared on 15 km averages of both rain rate and hourly accumulation, using the RMSF statistic, and including locations where either forecast or analysis exceed mm h 1 for rates, and 0.2 mm for accumulations. The forecast performance was compared with the interactive FRONTIERS system during the period 5 August 13 September 1995, with subjective assessment for the period 5 13 September The first part of the period was predominantly dry with only isolated showers, while the last two weeks were wet with a succession of frontal rain belts crossing the British Isles. The objective statistics are therefore dominated by the wet period, and the subjective statistics refer only to this period, resulting in some bias to the results. The main objective scores are given in Table 3. As can be seen, Nimrod scored significantly more hits at all lead times, and increasingly more as the lead time increases. This is partially counter-balanced by an 7

8 B W Golding Table 3. Comparison of objective verification statistics for Nimrod (N) and FRONTIERS (F) during the trial period 5 August September 1995 Statistic Method T+1 T+2 T+3 T+4 T+5 T+6 HR N F FAR N F RMSF N F increasing false alarm rate. The overall superiority of Nimrod is confirmed by the RMSF accumulation scores. Subjective assessment was carried out for three forecast times and recorded in terms of percentage of occasions when each system was significantly better. It thus has quite different weighting from the objective statistics which are recorded per wet pixel. The results are given in Table 4. Table 4. Number of cases when each method was subjectively assessed as significantly superior from the intensive trial period, 5 13 September 1995 These results support the objective verification conclusions, indicating that Nimrod is better throughout, but with an increasing margin for longer lead times. A full report on this comparison trial was made by Golding (1995). The reliability of Nimrod is substantially higher than that of FRONTIERS, and the delivery time of 13 minutes, on average, represents a reduction of more than 50% in the delay. Figure 5 shows the profile of RMSF accumulation error with lead time over a full year, comparing the NWP model, the advection forecast, and the Nimrod forecast. In the trial period, the advection forecast had similar error characteristics to FRONTIERS, hence this comparison gives some indication of the benefit that has been obtained from moving to the Nimrod system. Two conclusions may be drawn from the comparison. The first is the steep increase in error in the advection forecast, consistent with Figure 1, which is much improved, but nevertheless still rather steep in the Nimrod forecast. The second is the flattening out of the Nimrod curve below the NWP model, probably resulting from a correction of part of the model bias in the merging process. This bias is evident in a very high false alarm rate (typically about 65%) in the model distribution results. The Nimrod forecast false alarm rate is much lower with only a slight reduction in hit rate. 8 T+1 T+3 T+6 Nimrod better 17% 44% 50% FRONTIERS better 6% 6% 11% 4.3 Precipitation products Two forecast products are diagnosed using the precipitation forecast in conjunction with NWP model estimates of atmospheric structure. They are precipitation type and lightning rate. Diagnosis of lightning rate and hail requires knowledge of updraught velocity in convective clouds. This is estimated from the NWP model by calculating the CAPE of an entraining plume and assuming that the potential energy released will be converted to vertical motion. The lightning flash rate is then deduced using the method of Price & Rind (1992). They combined empirical relationships between flash rate and cloud height, and between peak updraught velocity and cloud height to give: w max = F 0.22 (1) where w max is the peak updraught velocity in m s 1 and F is the flash rate in strikes min 1. Given the updraught velocity, inversion of equation (1) yields the required flash rate subject to constraints imposed to ensure that mixed phase hydrometeors are present. Where the rain rate (R) exceeds 10 mm h 1 the Figure 5. Fractional RMS errors in 15 km average rainfall accumulation forecasts as a function of lead time, March 1996 February The solid line represents Nimrod merged forecasts, the dashed line advection forecasts alone, and the dotted line model forecasts.

9 Automated very short range forecasting Table 5. Specification of precipitation type in terms of snow probability, cloud top temperature and updraught velocity Precipitation type Specification Drizzle Coldest cloud temperature > 1 o C Hail Cloud base temp > 15 o C, Cloud top temp < 5 o C, Precipitation rate > 0.1mm h 1, Maximum updraught > 5m s 1 Snow Coldest cloud temperature < 1 o C, Snow probability > 0.6 Mixed Coldest cloud temperature < 1 o C, Snow probability > 0.01, < 0.6 Rain Coldest cloud temperature < 1 o C, Snow probability < 0.01 relationship between flash rate and precipitation rate of Buechler et al. (1994) is also used. Modified to give strikes per minute over a 15 km square, this gives: F = R / 28.7 and equation (1) is used to obtain the peak updraught. Assessment of the lightning results shows that many more pixels are diagnosed containing strikes than are observed. One reason is that the ATD lightning detection system, used in the UK, samples a small fraction of lightning strikes. Secondly, a 30 minute sampling period is used, whereas the diagnosis includes lightning rates as low as one in 200 minutes, centred on each forecast time. Following early results dominated by positional errors due to storm movement during the sampling period, the predicted lightning areas are spread by one 15 km pixel, prior to verification. The resulting distribution achieves a hit rate of 84% over the land area of the British Isles. Further development of the system is planned, using the convective life-cycle ideas incorporated in the GANDOLF thunderstorm prediction system (Hand, 1996). Snow probability is estimated using the techniques of Baldwin et al. (1994) and Ramer (1993). It is calculated only where precipitation is forecast and the coldest temperature in the cloud is colder than 1 o C. Given these conditions, the height integral (in K m) of positive wet bulb temperatures, from ground to freezing level, is calculated for each 5 km sub-square. The probability of snow varies linearly between 0 for values of the integral exceeding 450 K m and 1 for values of less than 25 K m. Specification of the precipitation type uses snow probability, cloud top temperature and updraught velocity as given in Table 5. Following Huffman & Norman (1985), liquid or mixed precipitation is classified as freezing rain or drizzle if the wet bulb temperature is below 0 o C throughout the lowest 50 m. Note that the term mixed is used here to refer both to a mixture of rain and snow at one location (sleet), and to the presence of rain in one part of the grid square and snow in another. Assessment of the diagnosed T+0 precipitation type against present weather reports has been carried out using a contingency table since January Entries are made only when precipitation is both observed and diagnosed. Since the precipitation type is derived from model parameters, this corresponds to forecasts varying between 3 and 9 hours in length. Mixed precipitation can be a correct diagnosis of snow, sleet or rain, so care has to be taken in interpretation of the results. During the winter months of early 1996 and 1996/97, observations were assessed, of which were forecast or observed snow. The assessment of snow gave the results shown in Table 6. Table 6. Assessment of snow diagnosis, Jan/Feb/Dec 1996 and Jan/Feb 1997 Statistic Value Hit rate (snow observed, snow or mixed diagnosed) 85% False alarm rate (Rain or sleet observed, 25% snow diagnosed) However, in the spring, the figures deteriorated substantially owing to recurrent reports from a small number of mainly high level stations outside the British Isles. As a result, modifications to the use of orography information will be made before winter 1996/67 to broaden the mixed precipitation area in mountainous country. Freezing rain is very rare in the British Isles, but two significant events occurred in winter 1995/96. The first, on 30 December 1995, was described in Pike (1996), and occurred when a spell of cold weather, associated with easterly winds from the interior of Europe, was broken by the advance of warmer Atlantic air from the southwest. Across much of south Wales and southern England, the sub-zero air persisted near the surface as warmer air spread at about 1 km, resulting in rain which froze on impact with the ground, and many accidents. Figure 6 shows the precipitation type diagnosis for 1200 UTC 30 December 1995 using the T+6 NWP model forecast, compared with present weather reports. The general area of freezing rain is well captured but there were several errors of detail, notably the failure to capture the observations in South Wales and Anglesey, and the positional error in Northern France. Further precipitation type comparisons from early 1996 were presented in Golding (1996). 9

10 B W Golding Figure 6. Diagnosed (left) and observed (right) precipitation types at 1200 UTC, 30 December Cloud The main requirement for prediction of cloud comes from aviation, especially military aviation and general aviation, both of which require visibility of the ground from low flying altitudes. There is also a general requirement for overall cloud cover from the general public and from the utility and leisure industries, mainly due to the impact of cloud on sunshine amounts. A coarser resolution (15 km) is used for these products since, except for very low cloud, observations show less variability than for precipitation and visibility. For very low cloud, fine scale variability is associated with orography, and this may be added later. In order to achieve sufficient vertical resolution above ground, a level spacing of 30 m (~100 ft) is used from sea level to 150 m. Above that, the spacing expands to 55 m at 300 m, 130 m at 600 m, and 2000 m above 5000 m. In order to use information from the precipitation forecast in advecting the cloud, this component is run after the precipitation, with products available by 30 minutes after data time on average. A cloud analysis has formed part of the input to the mesoscale NWP model for many years (Wright & Golding, 1991; Macpherson et al., 1996), and the present scheme is developed from that. The main observation source is half-hourly Meteosat satellite imagery, which is first processed to separate clear and cloudy pixels using the technique described by Jackson (1995). At night, only IR imagery is available and the algorithm identifies cloud if the IR radiance temperature is at least 5 K colder than the NWP model surface (skin) temperature. This is susceptible to errors in model temperature, particularly over land where an error in model 10 cloud can lead to substantial temperature errors. It is also affected by variations in atmospheric IR transmission. During daylight, the threshold for the IR test is increased to 7 K, but cloud can also be identified using visible imagery. This is first normalised for solar elevation, and then compared with a monthly cloud-cleared radiance image obtained during 1995/96. Cloud is diagnosed if the normalised radiance exceeds the clear value by at least 16 counts (10% albedo). As noted in Jackson (1995), apparent albedos are anomalously high at local noon, and so a higher threshold (15%) is applied to those images. Subjective investigation has shown that these parameters result in a good discrimination between mainly clear and mainly cloudy pixels when both IR and visible images are available. Cloud top height is calculated using NWP model estimates of atmospheric structure in a two pass procedure. This is necessary because, for boundary layer cloud, the height obtained by direct comparison with the temperature profile is very sensitive to small errors in the model boundary layer height. The method has been described by Hand (1993) and is based on the premise that low cloud normally has its top immediately below the boundary layer inversion. For radiance temperatures warmer than 20 o C, an attempt is first made to match the IR radiance temperature by lifting a parcel with the NWP model boundary layer potential temperature and specific humidity. Where this is inapplicable or fails to produce a sensible match, the model temperature profile is used, directly, to assign cloud top height. A significant problem remains in the treatment of thin cirrus, which is ignored at present, but for which a correction, using water vapour imagery, is under development based on Appendix C of Schmetz

11 Automated very short range forecasting et al. (1993). Following computation of the cloud top height, a parallax correction is made to all the satellite fields to allow for the satellite viewing angle. The multi-level cloud analysis, which follows, is a complex procedure, bringing together forecast first guess, satellite derived data, and surface observations with varying levels of detail. It is carried out on horizontal levels above sea level on the coarse 15 km grid. This takes advantage of the generally layered form of cloud sheets. First, the recursive filter analysis refines the satellite derived cloud cover using surface reports, and the forecast first guess amounts at each level are corrected to agree with the satellite cloud top height. A first guess profile is then obtained for each observation location and modified to be consistent with the observed cloud layer information. The modifications are spread, using the recursive filter analysis on horizontal levels. Finally, the layer cloud amounts are adjusted to agree with the analysed total cover. At each stage, a record is kept of where information has been added to the first guess, so that only new information is used in the subsequent forecast, and for input to the NWP model assimilation. The cloud forecast is closely tied to the precipitation forecast, to ensure that cloud remains associated with the major rain areas. This is achieved by identifying precipitating cloud and advecting it with the same vectors as used for the precipitation. Such cloud is identified by searching in the vertical for a contiguous layer with its top at or above the 15 o C level, and then assigning all cloud in or below this as precipitating. This procedure enables a separation between precipitating stratus and non-precipitating cirrus, for instance. Non-precipitating cloud is advected with the local NWP model wind. The advection procedure is identical to that used for precipitation except for the presence of missing data areas arising from the analysis. The merged forecast is generated from three sources: the advection forecast; persistence of the analysis; and the NWP model forecast. The first two are used only where new information was present in the analysis, as described above. Each contribution has a separate weight, estimated for each level and forecast time, except for the model for which only one comparison time is available, and which is therefore taken to be representative. The weights are calculated by combining the three error variances of cloud cover as follows: s2 s3 w1 = etc. ss + ss + ss where s 1, s 2, s 3 are the three error variances and w 1, w 2, w 3 are the weights. After combining the forecasts, the required diagnostics of cloud cover and base are computed for dissemination and assessment. The current version of the scheme, described above, has been in use since March 1996, and assessment results Figure 7. Fractional RMS errors in 15 km average cloud base forecasts as a function of lead time, March 1996 February The solid line represents Nimrod merged forecasts, the dashed line persistence, and the dotted line model forecasts. are available for the period since then. Statistics of the smoothed distribution of more than 4 oktas of total cloud, cloud below 5000 feet, and cloud below 1000 feet are computed together with RMSF statistics of cloud base and 3 oktas cloud base. In addition, the percentage of 3 oktas cloud bases within 30% of actual and the RMS total cloud cover error are computed. Figure 7 shows the key assessment measure, the RMSF error in cloud base of 3 oktas or more. Comparison with the persistence forecast and the NWP model shows a substantial gain in accuracy at all forecast times. For overall cloud cover, Figure 8 shows the RMS error versus analyses compared with NWP model results. Figure 8. RMS errors in 15 km average total cloud cover forecasts as a function of lead time, March 1996 February The solid line represents Nimrod merged forecasts, the dashed line persistence, and the dotted line model forecasts. 11

12 B W Golding 6. Visibility Prediction of poor visibility is a key requirement for all forms of transport, though the range of values of interest varies. For low level fighter aircraft training flights, even haze can be a serious problem, but for most applications it is fog that is important, particularly when visibilities drop below about 200 m. For land transport, the rapid variations associated with patchy radiation fog are a particular problem and so Nimrod attempts to resolve variations at 5 km resolution. An hourly update cycle is used to make use of the hourly surface visibility reports, and processing is carried out after the cloud forecast to allow its influence to be incorporated if required. Since fog formation is largely a local response to air becoming supersaturated, the analysis and forecast are carried out using temperature and moisture variables, specifically the liquid water temperature (T L ) and total water (q T ) given by: where q is the water vapour mixing ratio, q L the liquid water mixing ratio, T the temperature, c p the specific heat at constant pressure, and L the latent heat of evaporation. When required, visibility is diagnosed from these variables and an aerosol content obtained from the NWP model, using the method described in Wright & Thomas (1996) which first diagnoses the liquid water content, if any, and then uses this or the relative humidity, combined with the aerosol content, to estimate visibility. The analysis uses forecast first guess values of T L and q T, together with surface observations of temperature, dew point and visibility, and a satellite deduced fog mask. In daylight, fog is inferred from the Meteosat IR and visible radiances as cloud that is less than 15 K cooler than the model surface temperature. At night, the NOAA- AVHRR imagery is used in place of the visible, the difference between the IR and the 3.7 µm window channel being used in the manner described by Eyre et al. (1984). Neither method reliably distinguishes fog from low stratus, and so the pixels identified by these techniques are associated with the nearest visibility report lying in the area, and are classified as fog or not accordingly. The analysis procedure simultaneously minimises the error of fit to T L and q T using the variational analysis method. The resulting diagnosed visibility distribution contains considerably more spatial detail than could be obtained from surface reports alone. The extrapolation forecast is obtained by applying the trends of T L and q T obtained from the NWP model, to the analysed values. This successfully corrects for any initial bias in the model, and is particularly successful in the radiation fog formation period. It is less successful in the clearance phase, when the trends depend more on 12 T T Lq L = c p q = q+ q T L T Figure 9. Fractional RMS errors in 5 km average visibility as a function of lead time, October 1996 February The solid line represents Nimrod merged forecasts, the dashed line persistence, and the dotted line model forecasts. whether fog is present or not, and the method is liable to mislead if the NWP model diurnal cycle has the correct magnitude but erroneous phase. Three forecast estimates of T L and q T are combined to form the merged forecast: the extrapolation described above, persistence and the NWP model. The error variances for T L and q T are determined as a function of the RMSF visibility error, against all available point observations at T+0 using forecasts from the previous hour. For persistence and extrapolation, a prescribed increase with lead time is included. The weights are then calculated so as to optimise the reduction in error variance. The scheme was implemented in October 1996 so only limited routine statistics are so far available. Trials of the scheme involved testing in eight situations, all but one of which involved fog over part of the UK. The full results from these case studies are discussed in Wright & Thomas (1996). Subjective assessment of the analyses in these trials was undertaken for regions of approximately 100 km size, and showed that the character and extent of fog was broadly correct in 88% of the 1014 comparisons. This figure falls to 78% of 574 comparisons when only regions containing forecast or observed fog are included. The routine forecasts are assessed against analyses for thresholds of 200 m and 1000 m visibility, and using the RMSF statistic for observed or analysed visibilities less than 5 km. Figure 9 shows the RMSF error for October 1996 to February 1997, with curves for model, persistence and Nimrod forecasts. The results show a substantial improvement over the prior estimates, but for this variable must be seen as preliminary, due to the rarity of fog during the period. Figure 10 shows an example from one of the trial cases, 23 December 1994, where the NWP model

13 Automated very short range forecasting Figure 10. One hour forecast and analysis of surface visibility for 1300 UTC, 23 December component was particularly poor. The one hour forecast has successfully forecast the distribution of the thick fog areas around London, but has missed the developing fog in the Midlands. 7. Other variables Temperature predictions are generated during preparation of the visibility forecast described above. Assessment against analyses for the period October 1996 to February 1997 is shown in Figure 11. The improvement over mesoscale model results obtained by finer resolution and use of up-to-date observations gives a reduction of nearly 50% in the error of one hour forecasts. Wind predictions are currently used directly from the NWP model. The model products have only been assessed against point observations to date, and Figure 12 shows 12-month running mean values of the RMS vector wind error for the available forecast runs as a Figure 11. RMS errors in 5 km average temperature forecasts as a function of lead time, October 1996 February The solid line represents Nimrod merged forecasts, the dashed line persistence, and the dotted line model forecasts. Figure 12. Average monthly RMS errors in site-specific vector wind forecasts as a function of time of days, July 1995 June The four initialisation times are: solid line 0000 UTC, dashed line 0600 UTC, dotted line 1200 UTC, striped line 1800 UTC. 13