Transcript of Verification of Mobile weather Alert Forecasts over Lake Victoria region in Uganda Khalid Y. Muwembe...
- Slide 1
- Verification of Mobile weather Alert Forecasts over Lake
Victoria region in Uganda Khalid Y. Muwembe MSc Dissertation
University of Reading Supervisors Dr. Charlie Williams Dr. Thorwald
Hendrik Stein
- Slide 2
- Background The frequent recurrence of severe storms continues
to threaten the safety of marine navigation over Lake Victoria
since a large number of boats use the lake on a daily basis to
sustain a thriving fishing industry. Hundreds of people lose their
lives on the lake each year, with a proportion of these are related
to storm conditions. The importance of the lake as a resource that
supports community livelihood is growing and so is the need to
provide weather forecasts and warnings to fishermen and other
vessels using the lake for the safety of navigation.
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- Motivation Given the rough weather conditions common over the
lake, there was need for regular weather warnings to ensure safety
of marine operations. The MWA Service was designed to help
fishermen avoid dangerous weather hazards that may cause fatal
accidents over the lake. Issuing the weather forecasts is one step
and the other would be the trust that fishermen attach to these
forecasts which in turn depends on the accuracy and reliability of
the warnings issued.
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- Objective Verification of daily weather forecasts for Mobile
Weather Alert (MWA) against observed storm events for period
February April 2012. Assessment of the forecasting tools available
to operational forecasting at UDoM Recommendations on how to
improve the forecasting tools/process at UDoM.
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- Study area Fig 2: Major Islands with active fisheries over Lake
Victoria. Source: Google Maps Fig 1: Topographic map of Uganda and
location within Africa. Source:
Wileyonlinelibrary.com/journal/met
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- Annual cycle of rainfall Annual precipitation cycle of three
(3) Coastline stations within 50km of Kalangala Islands
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- Diurnal cycle of convection Deep convection peaks over the lake
in the early hours of the morning and dissipates in the afternoon
Evening peak in convection over land north-east of the lake and to
the east.
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- Operational 4-km Lake Victoria model The 4km model over Lake
Victoria has same science configuration as Extended UK4 model but
located in East Africa. The 4km model is run operationally twice a
day at 00 UTC and 12 UTC, out to T+48 hours, with a time step of
100 seconds. The model has 70 levels in the vertical, but only
extends from the surface to 40 km, providing greater resolution in
the boundary layer. All forecast model fields are available to
operational forecasters online
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- Case study: 4-km Lake Victoria performance on convective storms
Chamberlain and Bain ( 2012) verified the Lake Victoria model
performance on a case study of a storm that occurred on 1 March
2012 around 1930 LT (1630 UTC). This storm event and associated
high waves in the Buvuma region led to a boat capsizing, killing 7
passengers and crew with few people surviving
(http://allafrica.com/stories/201203120163.html)http://allafrica.com/stories/201203120163.html
Satellite observations showed the storm developing between 12 UTC
and 15 UTC on 1 March NE of the lake (Buvuma) and dissipated by 18
UTC although by then there were signs of new convective activity
south of the original storm.
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- Verification of storm Various model fields were analysed in
both the high resolution (4-km) and global (25-km) models over the
Lake Victoria region. From results of this case study, the global
model showed an increase in wind intensity compared to that of both
the mean and the calm day fields when a storm was present However,
the strength of the winds did not exceed the red warning threshold
of 20 kts for mean wind speed or 30kts for wind gusts
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- Results from 4-km model Most fields showed an increased
intensity over the mean state and the calm day state when a storm
was present. The spatial accuracy of this forecast appeared to be
good since the storm was developing in this area at 1200 UTC. The
results further showed that both models appear to be identifying an
increased storm risk 12 hours before the storm starts to develop.
It was concluded that the high resolution model adds value to the
forecast from the global model by capturing the features of the
event more clearly
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- Satellite imagery on 1 st March 4-km Lake Victoria model T+12
Global unified model T+12
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- Verification of the Mobile Weather Alert forecasts Data sources
MWA forecasts from UDoM Lightning data from recent convection storm
study over L. Victoria by UK Met Office international team
(Chamberlain and Bain, 2012). TAMSAT satellite algorithm data used
due to absence of rain-gauge observations over the lake. Available
dekadal rainfall estimates were downscaled to daily RFE estimates
using Daily RFE = (dekadal RFE / dekadal CCD) * daily CCD
Rain-gauge records from stations along the lake coastline sourced
from UDoM
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- Verifying TAMSAT daily RFE with rain-gauge records from Entebbe
and Makerere R = 0.796 R = 0.813 RFE and rain-gauge R= 0.813
Entebbe R=0.796 Makerere Significance tests for correlations done
at 95% level. The correlation is significant at both stations with
p-values of 0.006 and 0.002 for Entebbe and Makerere
respectively.
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- The Contingency table and verification measures Forecast
performance measures and scores used in the study are computed from
4 elements of the table, hits, false alarms, misses, and correct
rejections. Performance measures used are i)Frequency bias index
(FBI) ii)Proportion Correct (PC) iii)Critical success Index (CSI)
iv)Equitable threat score (ETS) v)True Skill Statistic (TSS ) Five
rainfall observation thresholds were used in this study to
calculate the verification scores; 0.1mm (obs), 2mm, 5mm, 10mm and
20mm
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- Determining the rainfall thresholds Observations and forecasts
analysed to determine thresholds. Top left most obs near zero
considered light showers ~2mm_T Top right Most obs between 0 10mm
considered moderate showers ~5mm_T Bottom left most Obs between 10
20mm considered heavy showers ~10mm_T Anything beyond 20mm
considered severe showers ~20mm_T
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- Hazard thresholds and recommended actions
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- Results
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- Monthly forecast performance False alarms are fewer during the
drier month of February compared to March and April. This could be
attributed to fewer storm forecasts issued during a dry month.
However, a higher number of misses is observed as the occasional
storms are not forecasted.
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- Performance measure variations at different thresholds Hits and
misses reduce with increasing rainfall threshold False alarm
decreases with increasing rainfall threshold
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- Forecast success rate Hits plus CR (correct rejections) are the
successful forecasts while false alarm and miss indicate
unsuccessful forecasts. Around 70% success rate against ARF and
RFE, however lower success rate against ATD
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- Verification skill scores
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- Frequency bias February is dry month, the FBI was computed for
the entire 3 month period as well as a period minus the dry month
(February). Including dry month forecasts tendency of
over-forecasting and without dry month under-forecasting at higher
thresholds
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- Critical Success Index and Proportion Correct Forecasts capture
heavy storms fairly well high values of PC/POD Does not include CR
CSI basically depict reliability of forecast system
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- Verification scores At higher value thresholds, both ETS and
TSS computed scores are higher, indicating highest forecasting
skill. The scores for both ETS and TSS are lowest for persistence
forecast (depicted as persist) implying that persistence forecast
is less skilful when verified against either of the observation
data sets.
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- Discussion of results The verification results from the
analyses indicate that there are no substantial differences in
skills of the forecasting system when verified against either
averaged rainfall or RFE. Observation thresholds are used in the
study in response to the hazard thresholds criteria that the MWA
forecast system uses to categorise the forecasts issued in
accordance with established hazard alert criteria
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- Using PC/POD The PC measure is important since forecasting calm
days (correct rejection) is equally critical to fishermen using the
MWA forecasts in making decisions on when to going-out fishing on
the lake. High PC scores determine the confidence forecast users
(fishermen) attach to the MWA forecasts they receive on daily
basis. High PC scores indicates how forecast system forecasts storm
days as well as calm days and every time we get it correct builds
the integrity of the service by users
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- Using CSI Plots of CSI shows at both lower and higher
thresholds, CSI values are moderate indicating the forecasting
system is reliable but demonstrate no special skills in forecasting
light or severe storms. For persistence forecast, CSI values are
lower depicting less reliability of the persistence when verified
against either of the observation data.
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- Using ETS and TSS ETS and TSS show measures of accuracy of the
forecast Both scores show that the forecast system has better
skills at the higher observation threshold values, but the scores
are slightly lower at lower thresholds implying poor forecast skill
in forecasting light storms. Therefore results indicate that
forecasters are able to predict the severe storms
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- Recommendations to improve the forecasting Development of
hazard indicators from the model products Extreme forecast index
for all fields Hazard diagnostics for lightning, wind gusts,
extreme rainfall indicators and wind shear to determine convection
type and severity. Improving observations over the lake Already
plans have been finalised to install AWS on 2 ferries operating
over Lake Victoria Improving configuration of the 4km Lake Victoria
Model Operational training for forecasters
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- Summary TSS and ETS are the best scores for verifying the MWA
forecasts since both measures indicate that the forecast system has
skill in predicting severe storms as well as calm days. Forecasts
show good hit rate for both storm and calm days. This is reflected
in the high values of PC/POD more so for higher threshold
observations. Better results are anticipated using actual gauge
observations over the lake.
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- They look keen on our service Accurate and reliable weather
information will save lives
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- The End