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Weather and Climate Extremes

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Assessing forecasting models on prediction of the tropical cyclone Dineo andthe associated rainfall over Botswana

Oliver Moses∗, Samuel RamotontoOkavango Research Institute, University of Botswana, P/Bag 285, Maun, Botswana

A R T I C L E I N F O

Keywords:BotswanaNumerical weather prediction modelsRemnant lowTropical cyclone Dineo

A B S T R A C T

The tropical cyclone Dineo made landfall over southern Mozambique on 15 February 2017. It weakened to aremnant low on 17 February, which hit Botswana on the same day and triggered heavy rainfall that resulted inflooding over the country. This study assesses the performance of the National Centers for EnvironmentalPrediction Global Forecast System (GFS) and the European Center for Medium-Range Weather Forecast(ECMWF) models in forecasting the locations and intensity of the tropical cyclone and its remnant low, theassociated cloud cover and rainfall over Botswana. The assessment includes comparison of the amount of pre-dicted rainfall (areal-averaged rainfall) with rain gauge data, locations of predicted maximum rainfall withobserved maximum rainfall and estimation of root mean square errors, forecast track and intensity errors. Dataused in the performance assessment of the models are rainfall observations, best track data and Meteosat satellitevisible images. The study period was 12–19 February 2017, which covered the lifespan of the weather system.Comparing model errors in forecasting the track and intensity of the tropical cyclone, both models had averageforecast intensity errors greater than 17mb while their average forecast track errors were 1.4 km or less. ECMWFperformed better than GFS in three aspects: maximum rainfall values, location and intensity of the storm; andGFS performed better than ECMWF in three aspects: location of maximum rainfall, cloud band associated withthe storm and overall rainfall amount (generally had lower root mean square errors). The relative performanceof both models suggest that the models should be used to complement each other in forecasting tropical cycloneevents in Botswana.

1. Introduction

Formation of Tropical Cyclones (TCs) in the South-West IndianOcean (SWIO) basin (5–30°S, 90°E to the southern African mainland)occurs throughout the cyclone season that stretches from 1st July to 30th

June, but the most active season is November to April (Langlade, 2013).There are two principal areas where TCs form in the SWIO basin, whichare the Mozambique channel and the area east of Madagascar (Mavumeet al., 2009). Five percent of TCs making landfall over Madagascarsubsequently reach Mozambique; and of those making landfall overMozambique, 34.5% develop within the Mozambique channel, whilethe other 65.4% develop within the greater south Indian Ocean basin(Fitchett and Grab, 2014). TCs normally weaken after making landfallover Mozambique. At times, the weakened systems move furtherwestward and affect other countries in the southern African mainland,but they rarely move as far as Botswana.

TCs generally begin with a cluster of disorganized thunderstormsthat go through some development stages categorised by 10-min

Maximum Sustained Winds (MSW; Langlade, 2013). Developmentstages in the SWIO basin are (i) disturbed area (no clear circulationcenter), (ii) tropical disturbance (MSW < 51 km/h), (iii) tropical de-pression (MSW: 51–63 km/h), (iv) moderate tropical storm (MSW:63–88 km/h), (v) severe tropical storm (MSW: 89–117 km/h), (vi)tropical cyclone (MSW: 118–165 km/h), (vii) intense tropical cyclone(MSW: 166–212 km/h) and (viii) very intense tropical cyclone(MSW > 212 km/h).

TCs are named and classified based on the described developmentstages. They are named when their 10-min MSW reach 63 km/h (or34 kt) near the center. For example, based on the described TropicalCyclone (TC) development stages, the TC Dineo 2017 was named on 13February 2017 (NASA, 2017). It reached its peak strength of 129.6 km/h (peak within TC status) on 15 February, the day on which it madelandfall over southern Mozambique (Inhambane). It weakened to aremnant low on 17 February before it hit Botswana on the same dayand caused heavy rainfall that resulted in flooding, particularly over theeastern half of the country, before it dissipated, still on the same day.

https://doi.org/10.1016/j.wace.2018.07.004Received 16 May 2017; Received in revised form 14 July 2018; Accepted 23 July 2018

∗ Corresponding author.E-mail address: omoses@ub.ac.bw (O. Moses).

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2212-0947/ © 2018 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

Please cite this article as: Moses, O., Weather and Climate Extremes (2018), https://doi.org/10.1016/j.wace.2018.07.004

The weather system damaged infrastructure such as houses, roads,bridges and culverts in various parts of the country.

To limit the amount of damage to property, socioeconomic activityand human lives associated with TCs (Ash and Matyas, 2012; Chikooreet al., 2015), warnings of these systems are essential. Such warningsinclude information such as the central location of the system, its track,intensity and associated rainfall. Forecasters rely heavily on NumericalWeather Prediction (NWP) models to generate TC warnings and fore-casts. For this reason, assessment of the performance of NWP modelsregarding forecasting of TC events has attracted the interest of severalresearchers. Lam (2001) assessed the performance of the EuropeanCenter for Medium-Range Weather Forecast (ECMWF) model in fore-casting the tracks of tropical cyclones in the South China Sea and partsof the western North Pacific. Identification and tracking of the positionsof the TC included use of the model's Mean Sea Level Pressure (MSLP)prognostic charts, were the positions of the points of minimum MSLPwere treated as the TC centers.

Tartaglione et al. (2005) verified a numerical model [BOlognaLimited Area Model (BOLAM)] in forecasting a relatively heavy rainfallassociated with a cyclone in the Cyprus Island during the period 5–6March 2003, using rain gauge data. They focused on the ContiguousRain Area analysis (CRA; Ebert and McBride, 1998, 2000; cited inTartaglione et al., 2005) since they were interested in assessing patternand volume differences between observations and model forecasts for asingle event. They indicated that verification can be done using stan-dard verification methods such as visual verification, continuous andcategorical statistics or joint distributions (Wilks, 1995; cited inTartaglione et al., 2005). Chen et al. (2013) evaluated TC track fore-casts from global models [ECMWF, National Centers for EnvironmentalPrediction Global Forecast System (GFS), Global Spectral Model ofJapan Meteorological Agency (JMA), Unified Model system of UnitedKingdom Meteorological Office (UKMO), Global spectral model ofChina Meteorological Administration (CMA)] during 2010 and 2012 forthe western North Pacific region. To evaluate the performance of themodels, they determined forecast track and intensity errors. They foundout that the performance of GFS in 2012 was quite close to ECMWF(recognised as the best global model by forecasters). Generally, theyfound out that the performance of the models was better at short leadtimes. Brassill (2014) assessed why ECMWF produced better trackforecasts of the Hurricane Sandy 2012 than the GFS model. The studyemployed the Advanced Research Weather Research and Forecasting(WRF) version 3.3.1 (Skamarock et al., 2008; cited in Brassill, 2014) tocreate local simulations of the ECMWF and GFS model tracks. In Sandy'scase, the study attributed the differences between the models mainly todifferences in cumulus parameterization schemes than to differences inresolution or initial conditions. Lei et al. (2016) evaluated the perfor-mance of typhoon forecasts (position and intensity) over the westernNorth Pacific in 2015. The models that they evaluated included theGlobal Data Assimilation and Prediction System of Korea Meteor-ological Administration (KMA), ECMWF, JMA, CMA, GFS and UKMO.Their evaluation included forecast track and intensity errors. Theyfound out that forecast track errors decreased from 2010 to 2015 whileforecast intensity errors were almost constant.

Assessment of TC forecasts from different NWP models providescrucial information that can assist forecasters to choose the best modelfor forecasting TCs in their local area. The assessments are also crucialto modelers who can use them to direct future development and im-provements of the models. Both forecasters and modelers are interestedin, amongst others, the ability of the model to determine the locationand intensity of the TC (Ebert, 2013). The purpose of the present studyis to assess the performance of GFS and ECMWF in forecasting the track(center locations) and intensity (minimum central pressure) of the TCDineo 2017, its remnant low, the associated cloud cover and rainfallover Botswana. These models are of interest in this study because theBotswana Department of Meteorological Services (BDMS) relies onthem for its operational weather forecasting. The assessment therefore

aims to improve understanding of the performance of these models inconnection with forecasting TC events in Botswana. Despite BDMShaving installed two regional NWP models, namely, the WRF andCOnsortium for Small-scale MOdelling (COSMO; Schättler, n.d.) foroperational forecasting purposes, their outputs are not always availableto forecasters on daily basis mainly due to internet connectivity chal-lenges and insufficient modelling personnel. For this reason, BDMSrelies on ECMWF and GFS forecast charts which are freely availableover the internet. However, some ECMWF GRIdded Binary (GRIB) dataare not freely available over the internet (ECMWF, n.d.1). In the as-sessment, this study uses the best track dataset of the Regional Spe-cialized Meteorological Center (RSMC) of La Reunion (RSMC - La Re-union), Meteosat satellite visible images and rain gauge data. Theperiod of study is 12–19 February 2017: 12–17 February covers thelifespan of the TC Dineo and its remnant low, on 17 February the TC'sremnant low dissipated but 24 h accumulated rainfall associated with itwas recorded on 18 February. Rainfall that occurred on 18 February,i.e., the first day after the dissipation of the remnant low was recordedon 19 February.

2. Materials and methods

2.1. The study area

The study area is Botswana, a landlocked country (Fig. 1) with aclimate that is generally described as hot and semi-arid to arid (Moses,2007). Its weather is influenced mainly by synoptic weather systemssuch as the Indian Ocean Anticyclone, the Atlantic Ocean Anticyclone,surface lows, frontal systems, Inter Tropical Convergence Zone (ITCZ),upper level anticyclones, cut-off lows, easterly and westerly troughs(Moses and Parida, 2016). TCs that form over the SWIO and makelandfall over Mozambique normally weaken to lower status weathersystems such as ex-tropical cyclones and remnant lows as they movewestward into southern Africa mainland, although they seldomly moveas far as Botswana. They weaken due to loss of moisture and energyover land (Met Office, n.d.). When these weakened weather systemsreach the country, they drop heavy rainfall that contribute substantiallyto the country's total seasonal rainfall. An example of this is the ex-tropical cyclone Eline, which in February 2000 caused widespreadheavy falls over southern Africa, including Botswana (Reason andKeibel, 2004).

Another example is the remnant low of the TC Dineo 2017, which isbeing investigated in the present study.

Monitoring of rainfall in Botswana is the responsibility of BDMS,which has 17 synoptic weather stations, 30 Automatic Weather Stations

Fig. 1. Location of Botswana (source: Open Libraries, n.d.).

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(AWS) and over 700 manual rainfall stations spread across the country.Twenty-four hours accumulated rainfall measurements are made at06:00 UTC every day. Daily rainfall data are not always available fromthe stations, particularly from manual ones during weekends. Thishappens when those responsible for taking the readings are absent fromstations due to factors such as illness.

2.2. Models and verification data

The GFS (NOAA, n.d.1) and ECMWF (ECMWF, n.d.2) models as-sessed in this study are run operationally by the US National WeatherService and the European Center, respectively. The operational GFSmodel has four forecast cycles (i.e., it is initialized four times per day at0000, 0600, 1200, and 1800 UTC) while the operational ECMWF modelis initialized twice per day at 0000 and 1200 UTC (UTC is used inter-changeably with Z in this study). There is no significant differencebetween the four forecast cycles of GFS in the southern hemisphere(Yang, 2015). In this study, the models’ 1200 UTC forecast cycles wereused. The model data of interest were MSLP, cloud cover, 500mbgeopotential heights and daily accumulated rainfall. GFS data weredownloaded from NOAA (n.d.2) while ECMWF data were downloadedfrom ECMWF (n.d.1). Forecast lead times of 24 h were used. Such shortforecast lead times have average location errors that are much less thanthose of 48 h. Forecast lead times of 72 h and beyond have averagelocation errors that are even worse than those of 48 h forecasts (NOAA,n.d.3; Chen et al., 2013; Chan et al., 2014). The model data were for theperiod 12–19 February 2017, which covered the lifespan of the TCDineo.

Model performance was verified using best track data, rain gaugedata and Meteosat satellite visible images. Typical best track dataconsists of TC's center location, maximum surface wind speed and itsminimum central pressure prepared for every TC by using all availabledata (Lam, 2001; NOAA, n.d.3; Ebert, 2013). The best-track dataset ofRSMC - La Reunion (Meteo France, 2017), which is the regional centerof the World Meteorological Organization (WMO) responsible for theSWIO basin, were used in this study. From the best-track dataset,minimum central pressures of the TC Dineo were available from 11February 2017 18:00 UTC to 15 February 2017 18:00 UTC, while itscenter locations were available from 11 February 2017 18:00 UTC to 17February 2017 12:00 UTC. TC information such as intensity and posi-tion differ in most cases between TC regional centers due to lack ofsufficient surface observations for TCs and different techniques used indataset production (Lee et al., 2012; cited in Chen et al., 2013). Me-teosat satellite visible images were obtained from the National Aero-nautics and Space Administration (NASA, 2017). Twenty-four hoursrain gauge data, obtained from BDMS and unadulterated by any post-processing (WMO, 2009), were used in the assessment. Conventionalsurface and upper level weather charts were also obtained from BDMSand were analyzed to determine conditions that existed before theremnant low of the TC Dineo hit Botswana and to determine the actualweather conditions that existed when the remnant low hit the country.The conventional weather charts were for the period 15–17 February2017, 1200 UTC.

2.3. Assessment of the models’ forecasts

The models' track and intensity forecasts were assessed against thebest track data in a quantitative way by using forecast track and in-tensity errors. To assess the models' performance regarding the TC'scenter locations, firstly center locations were identified by inspectingthe models' MSLP plots over successive 24 h periods. The center loca-tions were taken as the central points of the semi closed or closedminimum isobars. The identified cyclone's center locations were com-pared with the best track data (center locations) to estimate the forecasttrack error. This error refers to the great-circle difference between theforecast position and the TC's best-track center location (Chen et al.,

2013; Lei et al., 2016). The smaller the magnitude of this error, thebetter the forecast model. The forecast track error (E) in km was esti-mated from the zonal (Ez) and meridional (Em) errors using Equations(1)–(3) (Argete1and Francisco, 2008)

= − −E x x y y( )cos(( )/2)z f f0 0 (1)

= −E y ym f 0 (2)

= +E E Ez m2 2

(3)

where x and y are longitude and latitude respectively, (xf, yf) is theforecast position of the storm while (x0, y0) is the best track position.

Assessment of the performance of the models in forecasting the in-tensity of the cyclone was based on its minimum central pressures(Ebert, 2013), although TC intensity can also be represented by theMSW averaged over a particular time interval. To estimate forecastintensity error, forecasts of the cyclone's minimum central pressureswere compared with best track data. This error was estimated as theabsolute value of the difference between the TC forecast minimumcentral pressure and best track data at successive 24-h periods (NOAA,n.d.3). Intensity forecasts are less accurate than track forecasts becausethe inner structure and dynamic behaviour of tropical systems, whichplay a key role in determining the actual TC intensity are not yet wellunderstood (NOAA, n.d.4).

For the models' rainfall forecasts, the assessment involved compar-ison of forecast rainfall (areal-averaged rainfall) with rain gauge data atstation scale, as well as comparison of locations of predicted maximumrainfall with observed maximum rainfall. To measure the averageperformance of each model in forecasting rainfall, we performed sta-tistical analysis focusing on the Root Mean Square Error (RMSE). Themodels’ cloud forecast maps were compared with Meteosat satellitevisible images.

3. Results and discussion

3.1. Existing conditions before and when the storm hit Botswana

The conditions that existed two days before (15 and 16 February2017) the remnant low of the TC Dineo hit Botswana and those thatexisted when the remnant low hit the country (17 February) are shownon the conventional weather charts in Fig. 2a–f. Apparently, the east-erly trough in Fig. 2 was part of the cyclone and its remnant low.Easterly troughs over the SWIO, particularly those that are deep, arenormally linked to Pacific La Nina conditions (Chikoore et al., 2015). Infact, La Nina conditions occurred over the central and eastern PacificOcean from the end of 2016 to the beginning of 2017 (NOAA, 2017)and can therefore be associated with the easterly trough in question.Additionally, the upper level conventional charts show that a high-pressure system prevailed over the bulk of southern Africa. TC move-ments are known to be influenced by high-pressure systems’ large-scalesteering winds, particularly at mid-tropospheric levels being 500 and700hPa (Chan and Gray, 1982; Camargo et al., 2007). Therefore, thehigh-pressure system in Fig. 2 induced an easterly steering flow over theMozambique channel which guided the storm onto southern Africamainland which includes Botswana.

Over the period for which the conventional weather charts wereplotted (15–17 February 2017), GFS and ECMWF picked the alreadymentioned 500mb weather systems, i.e., the easterly trough and thehigh-pressure system that prevailed over most of southern Africa. Themodels’ 500mb forecast maps are shown in Fig. 3a–f. Throughout thispaper, GFS forecasts are shown on the left panel while ECMWF forecastsare shown on the right panel. The intensity and locations of the easterlytrough and the high-pressure system are quite similar on 500mb fore-cast maps of both models.

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3.2. Track and intensity forecasts assessment

The models' location and intensity forecasts of the TC Dineo and itsremnant low are shown in Fig. 4a–b, 5a-f, 6a-d and Table 1. The fore-casts were assessed using best track data (Table 2). The weather sys-tem's semi-closed or closed minimum isobar also indicates its forecast

Fig. 2. Conventional weather charts showing condi-tions that existed before and when the TC Dineo'sremnant low hit Botswana. Pre-existing conditions atthe surface are shown in a and c, at 500mb in b andd. Conditions that existed at the surface when theremnant low hit Botswana are shown in e, while at500mb are shown in f. “L-D” and “X” denote thecyclone or its remnant low and the easterly troughrespectively. “H” and “A” denote high pressure sys-tems at the surface and upper levels respectively. “L”and “C” denote low pressure systems at the surfaceand upper levels respectively.

Fig. 3. 500mb geopotential heights of the GFS and ECMWF for 16 February2017 (a and b), 17 February (c and d) and 18 February (e and f), showing aneasterly trough labelled “X”. Model forecast cycles used were for 12Z, 24 h leadtime.

Fig. 4. MSLP for (a) GFS and (b) ECMWF models showing the forecast storm'scenter locations (represented by “R”) for 12 February 2017. The best trackcenter locations of the storm are marked with a “*”. Model forecast cycles usedwere for 12Z, 24 h lead time.

Table 1Storm center locations and intensity (minimum central pressures) forecast bythe models.

Date (12UTC)

Forecast center location (°S,°E)

Forecast minimum central pressure(mb)

GFS ECMWF GFS ECMWF

12 Feb 2017 21.0, 38.5 18.0, 36.5 1008 100813 Feb 2017 22.8, 40.0 21.1, 39.4 1008 100814 Feb 2017 22.8, 39.3 21.7, 39.3 1000 ≤99215 Feb 2017 23.1, 36.8 23.3, 36.1 ≤984 ≤98416 Feb 2017 23.1, 32.9 22.5, 32.5 996 99617 Feb 2017 21.1, 23.9 21.7, 24.2 1004 1008

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intensity. For 12 February, GFS forecast the storm to be located over theMozambique channel (Fig. 4a–b), which is in accord with the best trackdata. On the other hand, ECMWF forecast the system to be located overthe Mozambican coast, so GFS performed better than ECMWF in fore-casting the location of the storm for 12 February.

However, for 13–17 February, the models were generally in agree-ment in terms of their center location forecasts.

Both models predicted Dineo's minimum central pressure to be1008mb on 12 February (Table 1) as opposed to 1003mb from the besttrack dataset (Table 2), indicating that both models underestimated theintensity of the system by 5mb. For 13 February (the day on which theTC Dineo was named), the models predicted the TC to have a closedminimum isobar of 1008mb (as for the previous day). This value ishigher than that from the best track dataset (991mb), so both modelsunderestimated the TC's intensity. For 14 February, GFS forecast the TCto have a minimum central pressure of 1000mb (Fig. 5c) while ECMWFforecast it to be lower than 992mb (Fig. 5d). Despite both modelshaving underestimated the system's intensity (978mb from the besttrack dataset), ECMWF performed better than GFS since it predicted alower value. Both models predicted the system to be most intense(minimum central pressure less than 984mb) on 15 February(Fig. 5e–f). This date is consistent with that reflected in the best trackdataset, even though the best track data indicate that the system was

more intense (965mb) than forecast by the models. For 16 February(Fig. 6a–b), both models predicted the minimum central pressure of thesystem to be 996mb. For 17 February (Fig. 6c–d), GFS and ECMWFpredicted the system's minimum central pressures of 1004mb and1008mb respectively. Unfortunately, the system's intensity values werenot available from the best track dataset from 16 February, hence it wasnot possible to use them to assess the models' intensity forecasts for thisperiod.

The models' TC track and intensity forecasts were also assessedquantitatively by computing the model errors presented in Table 3(computation based on Tables 1 and 2 and the methods described inSection 2.3). For 12 February, GFS had a forecast track error of 2.3 kmwhile that of ECMWF was 4.8 km, so GFS performed better thanECMWF in forecasting the position of the system. However, for 13–17February, ECMWF performed better than GFS (average forecast trackerrors for ECMWF and GFS were 1.2 and 1.4 km respectively). From 18February, center locations of the system were missing from the besttrack dataset as discussed in Section 2.2. Regarding the system's in-tensity for 12 February, the models had the same forecast intensityerror of 5mb. For 13–15 February ECMWF performed better than GFS(the former's average forecast intensity error was greater than 17mbwhile that for the latter was greater than 19mb). Overall, ECMWFperformed better than GFS in terms of forecasting the location and in-tensity of the TC Dineo and its remnant low.

3.3. Cloud forecasts assessment

A well-defined cloud band associated with a TC is a definite in-dicator of the system's center location, particularly a cloud band with a

Table 2Best track and intensity data for the TC Dineo (Source: Meteo France, 2017).

Date (12 UTC) Center location (°S, °E) Minimum central pressure (mb)

12 Feb 2017 21.19, 40.08 100313 Feb 2017 21.49, 39.93 99114 Feb 2017 22.26, 38.90 97815 Feb 2017 23.5,4 36.46 96516 Feb 2017 22.83, 32.82 Not available17 Feb 2017 21.97, 27.65 Not available

Fig. 5. As in Fig. 4 but for 13 February 2017 (a and b), 14 February (c and d)and 15 February (e and f).

Fig. 6. As in Fig. 4 but for 16 February 2017 (a and b) and 17 February (c andd).

Table 3The models’ forecast track and intensity errors.

Date (12UTC)

Forecast track error (km) Forecast intensity error (mb)

GFS ECMWF GFS ECMWF

12 Feb 2017 2.3 4.8 5 513 Feb 2017 1.3 0.7 17 1714 Feb 2017 1.1 0.7 22 >1415 Feb 2017 0.5 0.4 > 19 >1916 Feb 2017 0.3 0.5 – –17 Feb 2017 3.8 3.5 – –Average

error1.4 (13–17Feb)

1.2 (13–17Feb)

> 19 (13–15Feb)

>17(13–15 Feb)

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well-defined eye (Hong Kong Observatory, n.d.), so the models' cloudband forecasts were compared with satellite images. The models' TCassociated forecasts for 13, 15 and 17 (the day on which the TC wea-kened to a remnant low) February 2017 are shown in Fig. 7a–f. For 13February, the models forecast the cloud band to be located mainly overthe Mozambique channel stretching to southern Madagascar, with atotal cloud amount of 90% or more (Fig. 7a–b). There is consistencybetween the models' cloud band forecasts and the satellite image of thecorresponding date (Fig. 8a), as this satellite image places the cloudband over the Mozambique channel. For 15 February, ECMWF forecast

the cloud band (with a cloud amount of 90% or more) to shift slightlywestward (compared to 13 February) to cover mainly Mozambique andits channel, south eastern Zimbabwe and Malawi (Fig. 7d). However,for the same areas covered by ECMWF's cloud band, GFS forecast lessamount of clouds (50–75%) over eastern Zimbabwe and northern Mo-zambique (Fig. 7c). Comparing the models' cloud band forecasts(amount and extent) with the corresponding satellite image (Fig. 8b),GFS performed better than ECMWF. However, the models agree interms of extending their cloud bands to southern Mozambique on 15February (the day on which the TC Dineo made landfall over southernMozambique). A well-defined eye of the TC is visible on the satelliteimage for 15 February, but it is not identifiable on the models' cloudband forecasts. For 17 February, both models (Fig. 7e–f) forecast thecloud band to move further westward off the Mozambique channel tocover areas such as southern Mozambique, north-eastern South Africa,Swaziland, the bulk of Zimbabwe and Botswana. However, GFS pre-dicted a cloud “gap” [relatively less amount of clouds (50–90%)] overthe areas around the north-eastern border of Botswana with Zimbabwe(the cloud “gap” was not picked by ECMWF). GFS cloud band forecast isin accord with the corresponding satellite image (Fig. 8c). Apart fromthe models' cloud “gap” discrepancy, their forecasts are generallyconsistent with the satellite image in terms of their cloud bands arealextent. Overall, for 13, 15 and 17 February, GFS out performed ECMWFin forecasting the cloud band associated with the TC and its remnantlow.

A cloud band associated with a TC is not only useful in locating thecyclone's center location, but it is also linked with heavy rainfall asso-ciated with the system. Therefore, the cloud band forecast for 17February (associated with the TC's remnant low) is linked with theheavy rain that fell over Botswana on the same date. The performanceof the models in connection with forecasting this rainfall is discussed inthe next section (Section 3.4).

3.4. Rainfall forecasts assessment

The models' daily accumulated rainfall forecasts for 16–18 February2017 are shown in Fig. 9a–f. The location of the predicted rainfall as-sociated with the system is similar to that of the cloud band in Fig. 7a–fand 8a-c. The forecasts were assessed using station daily (24 h) accu-mulated rainfall observations. Since it is standard practice that 24 haccumulated rainfall observations are recorded the following day at0600Z (NOAA, 1998), the actual daily accumulated rainfall that oc-curred over Botswana on 17 February (triggered by the remnant low)

Fig. 7. Models' cloud forecasts for 13 February 2017 (a and b), 15 February (cand d) and 17 February (e and f). The models' forecast cycles used were for 12Z,24 h lead time.

Fig. 8. Meteosat satellite visible images showing clouds associated with the TC Dineo on (a) 13 February 2017, (b) 15 February while (c) shows the cloud bandassociated with the TC's remnant low on 17 February.

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was recorded on 18 February. The actual daily accumulated rainfallthat occurred on 16 February (a day before the remnant low hit thecountry) was recorded on 17 February while that which occurred on 18February (a day after the dissipation of the remnant low) was recordedon 19 February. Similarly, the models' 24 h accumulated rainfall fore-casts for 17 February are presented in Fig. 9c–d, while those for 16 and18 February are presented in Fig. 9a–b and Fig. 9e–f respectively. Thedaily accumulated rainfall observations used in the assessment of themodels' forecasts are presented in Table 4 while the results of statisticalanalysis (RMSE values) are presented in Table 5 [which also containseach model's Mid-Point of the Area of Maximum Rainfall Forecast(MPAMRF) and maximum predicted rainfall values]. Werda rainfallstation (Table 4) is situated in the south-western part of Botswana whileChangate and Maunatlala are situated in the northern half of thecountry.

For 16 February, GFS predicted a maximum rainfall of 40mm overthe south-western Botswana (Fig. 9a, Table 5). The MPAMRF accordingto GFS was (−23.50 S, 21.50 E; Table 5). Even though the actual max-imum rainfall was only 17mm [recorded at Werda (−25.30 S, 23.30 E),Table 4], GFS was in accord with the rainfall observations in terms ofplacing its maximum rainfall values. On the other hand, ECMWF(Fig. 9b, Table 5) predicted a maximum rainfall of 33 mm but placed itover the eastern part of the country [MPAMRF (−20.60 S, 27.60 E)].

The location of ECMWF's maximum rainfall was not consistent with theactual maximum rainfall value recorded at Werda (even though thisstation's rainfall cannot be linked with the remnant low which ap-proached the country from the east since the station is situated in thesouth-western part of Botswana). While both models overestimatedmaximum rainfall values, GFS placed its maximum rainfall closer toWerda than ECMWF (comparing the last two columns of Table 4 withthe MPAMRF column of Table 5). Furthermore, while both modelsoverestimated maximum rainfall values, ECMWF performed better thanGFS since it predicted a lower value. Statistical analysis revealed thatfor 16 February, GFS RMSE value was 13mm while that of ECMWF was16mm (Table 5), indicating that the average performance of the formerin forecasting rainfall amount was better than that of the latter. For 17February, the day on which the cyclone's remnant low hit Botswana,GFS (Fig. 9c, Table 5) and ECMWF (Fig. 9d, Table 5) models predictedmaximum rainfall values of 135 and 150mm respectively. However,model forecasts and actual rainfall values are not expected to be equalsince NWP models predict areal mean rainfall on model grid points butnot point values (Tartaglione et al., 2005; Shrestha et al., 2013; UCAR,n.d.). The models predicted maximum rainfall to occur over thenorthern half of the country, which corresponds with the actual max-imum rainfall recorded at Changate (−20.50S, 27.10 E). Changaterainfall was 270mm, which is about double the models' predictedrainfall. While both models underestimated maximum rainfall, ECMWFwas better than GFS since it predicted a greater value. Statistical ana-lysis indicated that the models had almost the same RMSE values(52mm for GFS and 51mm for ECMWF). Availability of more ob-servational data would improve the assessment results. For 18 February(the day after the remnant low hit the country), GFS (Fig. 9e, Table 5)and ECMWF (Fig. 9f, Table 5) forecast maximum rainfall values of 115and 24mm respectively. None of the models' maximum rainfall valuesmatched the observed maximum rainfall value of 55.5 mm recorded atMaunatlala (−22.60S, 27.60E).

However, ECMWF performed better than GFS in forecasting themaximum rainfall value since it under estimated it by 31.5mm whileGFS overestimated it by 59.5 mm. For 18 February, statistical analysisrevealed that RSME values associated with GFS and ECMWF were 8 and22mm respectively, so the average performance of GFS in forecastingrainfall amount was better than that of ECMWF.

Based on the models' RMSE values, maximum rainfall values andtheir MPAMRF for the period 16–18 February, GFS’ average perfor-mance in forecasting rainfall amount was better than that of ECMWF.

Fig. 9. GFS and ECMWF 24 h accumulated rainfall for 16 February 2017 (a andb), for 17 February (c and d; the day on which the remnant low of the TC Dineohit Botswana) and for 18 February (e and f). The models' forecast cycles usedwere for 12Z, 24 h lead time.

Table 4Observed rainfall associated with the storm over Botswana.

Date Number of stations thatrecorded rainfall

Mean observed rainfall(mm)

Maximum observedrainfall (mm)

Station name that recordedmaximum rainfall

Location of station that recordedmaximum rainfall (°S, °E)

16 Feb 2017 12 12.6 17 Werda 25.3, 23.317 Feb 2017 66 46.4 270 Changate 20.5, 27.118 Feb 2017 49 14.3 55.5 Maunatlala 22.6, 27.6

Table 5Maximum (Max) predicted rainfall over Botswana, Mid-Point of the Area ofMaximum Rainfall Forecast (MPAMRF) and Root Mean Square Error (RMSE).

Date Max predicted rainfall(mm)

MPAMRF (°S, °E) RMSE (mm)

GFS ECMWF GFS ECMWF GFS ECMWF

16 February2017

40 33 23.5, 21.5 20.6, 27.6 13 16

17 February2017

135 150 21.0, 26.5 21.0, 25.5 52 51

18 February2017

115 24 19.0, 25.0 22.1, 20.1 8 22

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GFS also performed better than ECMWF in forecasting the location ofmaximum rainfall while ECMWF performed better than GFS in fore-casting maximum rainfall values. From Table 3, the average forecasttrack errors are 1.4 km or less for both models (13–17 Feb 2017). Theseerrors are too small to account for the differences that have been no-ticed between the locations of the forecast and observed maximumrainfall. Still from Table 3, the forecast intensity errors have been foundto be greater than 17mb for both models. Such forecast intensity errorsare high and have therefore contributed to the underestimation ofmaximum rainfall by the models.

In summary of the forecast assessment that has been conducted(cloud cover, rainfall, track and intensity forecasts), differences havebeen found between ECMWF and GFS forecasts. The differences be-tween the models’ rainfall and cloud forecasts (linked with the trackand intensity of the system) could be mainly due to differences inparameterization schemes rather than to differences in the sets ofequations used, initial conditions and spatial resolutions (Brassill,2014). Chen et al. (2013) indicated that ECMWF was recognised byforecasters as the best global model, but the results of the present studyindicate that GFS is better than ECMWF in some aspects such as inforecasting location of maximum rainfall. This indicates that the per-formance of NWP models may also depend on other factors such asgeographic locations and certain weather events (Haby, n.d.).

4. Conclusion

A 500mb high-pressure system prevailed over most of southernAfrica in the study period. This high-pressure system induced an east-erly steering flow over the Mozambique channel which guided the TCDineo and its remnant low onto southern Africa mainland. There wasalso an easterly trough at 500mb, which was part of this weathersystem. The performance of GFS and ECMWF in forecasting the loca-tions and intensity of the TC Dineo and its remnant low, the associatedcloud band and rainfall over Botswana has been assessed. ECMWFperformed better than GFS in three aspects (forecasting maximumrainfall values, location and intensity of the TC Dineo and its remnantlow). GFS outperformed ECMWF by the same number of aspects(forecasting location of maximum rainfall, overall rainfall amount andthe cloud band associated with the system). Regarding the errors in TCtrack and intensity forecasts, both models' average forecast intensityerrors were greater than 17mb, while their average forecast track er-rors were 1.4 km or less. The forecast track errors are too small to ac-count for the differences between the locations of predicted maximumrainfall and the observed maximum rainfall, but the forecast intensityerrors are high and have therefore contributed to the underestimationof maximum rainfall by the models. The differences in the models’forecasts that have been noticed can be attributed mainly to differencesin parameterization schemes used than to differences in initial condi-tions, spatial resolutions and the sets of equations used. Based on theirrelative performance, both models should be used to complement eachother in forecasting TC events in Botswana. The results of the study arecrucial mainly to forecasters and to those involved in weather model-ling.

Acknowledgments

The following are appreciated for availing the data: BotswanaDepartment of Meteorological Services availed rainfall observationaldata, RSMC La Reunion availed the best track dataset for TC Dineo andits remnant low, Meteosat availed satellite visible images, NOAA andECMWF availed their model GRIB data. The University of Botswana'soffice of research and development is also appreciated for supportingthis research. The authors also acknowledge Dr E. Bennitt for languageediting.

References

Argete, J.C., Francisco, R.V., 2008. Detection and tracking of tropical cyclones on aseasonal scale in the Philippines. Sci. Diliman 19, 35–48.

Ash, K.D., Matyas, C.J., 2012. The influences of ENSO and the subtropical Indian Oceandipole on tropical cyclone trajectories in the southwestern Indian Ocean. Int. J.Climatol. 32 (1), 41–56.

Bassill, N.P., 2014. Accuracy of early GFS and ECMWF Sandy (2012) track forecasts:evidence for a dependence on cumulus parameterization. Geophys. Res. Lett. 41 (9),3274–3281.

Camargo, S.J., Robertson, A.W., Gaffney, S.J., Smyth, P., Ghil, M., 2007. Cluster analysisof typhoon tracks Part II: large-scale circulation and ENSO. J. Clim. 20 (14),3654–3676.

Chan, J.C., Gray, W.M., 1982. Tropical cyclone movement and surrounding flow re-lationships. Mon. Weather Rev. 110 (10), 1354–1374.

Chan, S.T., Davidson, J., Caroff, P., Courtney, J., Grant, D., Kato, K., Qian, C.H., 2014.Track, intensity, and structure changes at landfall - forecasting challenges. In: TheThird International Workshop on Tropical Cyclone Landfall Processes, Jeju, Republicof Korea, pp. 8–10.

Chen, G.U.O.M.I.N., Yu, H., Cao, Q.I.N.G., Zeng, Z.H.I.H.U.A., 2013. The performance ofglobal models in TC track forecasting over the western North Pacific from 2010 to2012. Tropical Cyclone Research and Review 2 (3), 149–158.

Chikoore, H., Vermeulen, J.H., Jury, M.R., 2015. Tropical cyclones in the Mozambiquechannel: January -March 2015. Nat. Hazards 77, 2081–2095.

Ebert, B., 2013. WWRP/WGNE Joint Working Group on Forecast Verification Research.ECMWF (n.d.1) TIGGE data retrieval. http://apps.ecmwf.int/datasets/data/tigge/

levtype=sfc/type=fc.ECMWF (n.d.2). Advancing global NWP through international collaboration. https://

www.ecmwf.int/.Fitchett, J.M., Grab, S.W., 2014. A 66-year tropical cyclone record for south-east Africa:

temporal trends in a global context. Int. J. Climatol. 34 (13), 3604–3615.Haby, J. (n.d.). The different forecast models. http://www.theweatherprediction.com/

habyhints/122/.Hong Kong Observatory (n.d.). Tracking and forecasting tropical cyclones. http://www.

hko.gov.hk/informtc/tracking.htm.Lam, C.C., 2001. Performance of the ECMWF model in forecasting the tracks of tropical

cyclones in the South China Sea and parts of the western North Pacific. Meteorol.Appl. 8 (3), 339–344.

Langlade, S., 2013. The south west Indian Ocean cyclone basin. http://severe.worldweather.wmo.int/TCFW/RAI_Training/Cyc_Bassin_SWI_oct2013_LANGLADE.pdf.

Lei, X.T., Chen, G.M., Zhang, X.P., Chen, P.Y., Yu, H., 2016. In: Performance of TropicalCyclone Forecast in Western North Pacific in 2015. 48th Session ESCAP/WMOTyphoon Committee.

Mavume, A.F., Rydberg, L., Rouault, M., Lutjeharms, J.R., 2009. Climatology and landfallof tropical cyclones in the south-West Indian ocean. West. Indian Ocean J. Mar. Sci. 8(1), 15–36.

Meteo France, 2017. Dineo 2017-02-11 to 2017-02-17. http://www.meteo.fr/temps/domtom/La_Reunion/webcmrs9.0/anglais/archives/saisons_archivees/20162017/2016RE05.html.

Met Office (n.d.). Tropical cyclone facts. http://www.metoffice.gov.uk/weather/tropicalcyclone/facts.

Moses, O., 2007. Study of Stochastic and Statistical Behavior of Some Climatic Parametersin Botswana. Dissertation. University of Botswana.

Moses, O., Parida, B.P., 2016. Statistical modelling of Botswana's monthly maximum windspeed using a four-parameter Kappa distribution. Am. J. Appl. Sci. 13 (6), 773–778.

NASA, 2017. Dineo southern Indian Ocean. https://www.nasa.gov/feature/goddard/2017/dineo-southern-indian-ocean.

NOAA, 1998. Training guide in surface weather observations. http://www.nws.noaa.gov/om/forms/resources/SFCTraining.pdf.

NOAA, 2017. ENSO: recent evolution, current status and predictions. http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/lanina.

NOAA (n.d.1). Global Forecast System. https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs.

NOAA (n.d.2). FTP directory. ftp://nomads.ncdc.noaa.gov/GFS/Grid4/201702.NOAA (n.d.3). National hurricane center forecast verification. http://www.nhc.noaa.

gov/verification/verify2.shtml.NOAA (n.d.4). Tropical cyclone forecasting. http://www.prh.noaa.gov/cphc/pages/

FAQ/Forecasting.php.Open Libraries (n.d.). World regional geography: People, places and globalization. http://

open.lib.umn.edu/worldgeography/chapter/7-6-southern-africa/.Reason, C.J.C., Keibel, D.A., 2004. Tropical cyclone Eline and its unusual penetration and

impacts over the Southern. Weather Forecast. 19 (5), 789–805.Schättler, U. (n.d.). Overview on the COSMO-Model System. http://www.clm-

community.eu/dokumente/upload/Training16_Model_Overview_Schaettler.pdf.Shrestha, D.L., Robertson, D.E., Wang, Q.J., Pagano, T.C., Hapuarachchi, H.A.P., 2013.

Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose. Hydrol. Earth Syst. Sci. 17 (5), 1913–1931.

Tartaglione, N., Mariani, S., Accadia, C., Speranza, A., Casaioli, M., 2005. Comparison ofrain gauge observations with modeled precipitation over Cyprus using contiguousrain area analysis. Atmos. Chem. Phys. 5 (8), 2147–2154.

UCAR (n.d.). Climate modelling. https://scied.ucar.edu/longcontent/climate-modeling.WMO, 2009. Recommendations for the verification and inter-comparison of QPFs and

PQPFs from operational NWP Models. http://www.wmo.int/pages/prog/arep/wwrp/new/documents/WWRP2009-1_web_CD.pdf.

Yang, F., 2015. Comparison of forecast skills between NCEP GFS four cycles and on thevalue of 06Z and 18Z cycles. https://ams.confex.com/ams/27WAF23NWP/webprogram/Paper273676.html.

O. Moses, S. Ramotonto Weather and Climate Extremes xxx (xxxx) xxx–xxx

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