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Seasonal Forecast, Water Resources and Expected Outcomes

South African Weather Service SWIOCOF-5 Pre-Forum

Marc de Vos, September 2016

South Africa: Context

http://www.lib.utexas.edu/maps/africa/africa_pol_1993.gif

BCRE, 2016, adapted from Boebel et al., 2003

SWIOCOF-5, September 2016

SAWS: An Introduction

• 25 Weather Offices• 205 Automatic Weather Stations• 165 Automatic Rainfall Stations• 11 Climate Stations• 1159 Rainfall Stations• 11 Upper Air• 23 Sea Surface Temperature• ~ 60 Weather buoys in the South Atlantic,

South Indian & Southern Oceans • 24 Lightning Detection Stations• 14 Meteorological Radar (C-, S- and X-

band)• 17 Air Quality Measuring and Monitoring

Stations• 2 Dobson Spectrophotometer• 3 Aircraft• 1 Baseline Surface Radiation Network

Station in De Aar • 1 Global Atmosphere Watch Station at

Cape Point • 12 Voluntary Observing Ships• 13 Solar Radiations

SAWS: An Introduction


NFC, PretoriaGlobal Producing Centre

Seasonal Forecast

SAWS: 2 Seasonal Forecasting Systems

1. Dynamical global ensemble prediction system (EPS)• ECHAM 4.5 AGCM

2. Statistical Forecasting System • Model output statistics (MOS) approach• Downscaled to SADC region

In house verification, based on an IRI methodology implemented some years ago.


Monthly Consensus discussion

Combining algorithm(not trivial!)

Multi-model ensembleof N1+N2+N3+…Nn members

Ensemble 1

(e.g. ECHAM4.5)

N1 members

Ensemble 2

(e.g. CCM3)

N2 members

Ensemble 3

(e.g. CFS)

N3 members

Ensemble n

(other forecast centre)

Nn members


Landman et al., 2008

Seasonal Forecast

Seasonal Forecast

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997−4

−3

−2

−1

0

1

2

3Central Interior DJF Simulations; ECHAM4.5

Ensemble MeanObserved Landman et al., 2008


Seasonal Forecast

• Model Output Statistics (MOS) applied to output from the following GCMs:• ECHAM4.5 (SAWS; 12 ensemble members)• CFS (NCEP; 40 ensemble members) • CCM3 (IRI; 24 ensemble members)

• Forecast probabilities calculated by CPT• Forecast probabilities averaged



Seasonal Forecast: ECHAM4.5 at SAWS• All runs performed on NEC SX-8• Climatological (6 members) and operational ensemble runs

- 24hr LAF• Atmospheric initial conditions from ECMWF (1979 to 1996)

analysis• Climatological dataset (1979-2003) constructed using AMIP

physics; model constrained by lower boundary conditions generated from a high resolution AMIP2 dataset for SST and sea-ice

• Operational set-up: persisted and forecast SSTs obtained from a high resolution observed SST (optimum interpolation v-2) and IRI (mean) respectively (6 members each)

• 12-member ensemble operational runs on 18th of each month for 6 consecutive months (i.e., 0-6 months lead-time)

Landman et al., 2008SWIOCOF-5, September 2016

Seasonal Forecast: CFS at NCEP• CFS is run twice a day from initial conditions

for the atmosphere and ocean, which are 7 days old

• The atmospheric initial conditions are obtained from NCEP Reanalysis-2 and the ocean initial condition is obtained from NCEP GODAS (Global Ocean Data Assimilation)

• The integrations are complete for the first partial month + 9 full months into the future

• 4 ensemble members are obtained each day for 10 days to create a 40 member ensemble

Landman et al., 2008SWIOCOF-5, September 2016

Seasonal Forecast: CCAM at CSIR


Seasonal Forecast: CCM at IRI

• All runs performed at IRI• Forced with persisted SST anomalies• 24-member ensemble

SWIOCOF-5, September 2016 Landman et al., 2008


Seasonal Forecast: Interface


Seasonal Forecast• Experimentation with one-tier (fully coupled)

models e.g. Landman et al., 2012• 1-tier Systems

• ECHAM 4.5v3 MOM3-DC2• ECHAM 4.5-GML-NCEP CFSSST• Computationally expensive

• 2-tier System• ECHAM 4.5 AGCM, forced with SST

derived from statistical model• Lower relative computational cost


Seasonal Forecast• Downscaled coupled systems outscored

downscaled 2-tier systems• Neither outscored reference system (AGCM

forced with simultaneously-observed SST)• Therefore, room for further development

remains• Acknowledged in SA that we need to spend

time and resources developing coupled systems



Current State & Outlook: Summary

• Neutral ENSO state• Weak La Niña looking less likely • Spring rainfall: lower than normal

• Indian Ocean conditions (negative IOD) –consequence for moisture transport

• Higher than normal rainfall prediction –uncertain (LT & La Niña)

• Spring & summer temperatures higher than normal


Current State & Outlook: Rainfall & Water


• Most of SA still experiencing drought conditions• Likely to persist despite summer rainfall• Forecast particularly uncertain

• Long LT• Uncertainty regarding weak La Niña



Dept. Water Affairs & Sanitation, 2016


Province Nett FSC million m^3 This Week (%) Last Week (%) Last Year (%)

Eastern Cape 1833 65 66 81

Free State 15971 55 55 74

Gauteng 115 82 82 90

Kwazulu‐Natal 4669 43 44 62

Lesotho* 2376 38 40 61

Limpopo 1508 49 50 76

Mpumalanga 2539 53 53 76

North West 887 63 64 63

Northern Cape 146 62 63 80

Western Cape 1870 62 62 72

Total 31913 53 53 71


Dept. Water Affairs & Sanitation, 2016

Current State & Outlook: Temperatures


• Most of SA: higher than normal for spring into summer

• West & southern coasts: lower than normal temperatures


Current State & Outlook: Temperatures

Templ ref: PPT-ISO-colour.001 Doc Ref no:

Expected Outcomes

• Generally• Insights into seasonal forecasting• Statistical methods and approaches to

atmospheric climate data• Updates w.r.t current best practice to take

back to my colleagues at the NFC

• Specifically• Look for potential areas in which

oceanographic section can add value

References• BCRE. (2016). Sea Atlas - Retroflection. Available:

http://www.bcre.org.za/seaatlas/index.php?p=retroflection.php. Last accessed 19th Sep 2016.• Boebel, O., Rossby, T., Lutjeharms, J., Zenk, W. and Barron, C., 2003. Path and variability of the

Agulhas Return Current. Deep Sea Research Part II: Topical Studies in Oceanography, 50(1), pp.35-56.

• Dept. Water & Sanitation. (2016). Status of surface water storage.Available: https://www.dwa.gov.za/hydrology/Weekly/Storage.aspx. Last accessed 19th Sep 2016.

• Dept. Water & Sanitation. (2016). Status of surface water storage.Available:https://www.dwa.gov.za/hydrology/Weekly/SumProvince.aspx. Last accessed 19th Sep 2016.

• Johnston, P.A., Archer, E.R.M., Vogel, C.H., Bezuidenhout, C.N., Tennant, W.J. and Kuschke, R., 2004. Review of seasonal forecasting in South Africa: producer to end-user. Climate Research, 28(1), pp.67-82.

• Landman, W.A., DeWitt, D., Lee, D.E., Beraki, A. and Lötter, D., 2012. Seasonal rainfall prediction skill over South Africa: one-versus two-tiered forecasting systems. Weather and Forecasting, 27(2), pp.489-501.

• Landman, W.A., Kgatuke, M.J., Mbedzi, M., Beraki, A., Bartman, A. and Piesanie, A.D., 2009. Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspective. International Journal of Climatology, 29(11), pp.1535-1549.

• SAWS. (2016). Seasonal Forecast. Available: http://www.weathersa.co.za/home/seasonal. Last accessed 19th Sep 2016.

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