Africa Rainfall Climatology Version 2 for Famine Early Warning Systems Nicholas S. Novella 12, Wassila M. Thiaw 1 1 Climate Prediction Center, National Centers for Environmental Prediction (NCEP) 2 Wyle Information Systems 1600 International Drive, Suite 800, McLean, VA FEWS-NET Science Meeting Oct 2-3, 2012 Santa Barbara, CA

 To complement the RFE, the Africa Rainfall Climatology (ARC1) product was developed as a climatological tool to generate anomaly fields for analysis  The ARC shares the same algorithm and output dimensions as RFE, buts uses a subset of the inputs with decreased IR sampling to enable a long-term record  GTS and IR data were selected as framework for the ARC dataset, as these inputs exhibit most homogeneity over historical record.  An initial reprocessing of these inputs performed by (Love et al., 2004) had led to the construction of high resolution precipitation dataset from 1995-present (ARC1).  Love et al., 2004 found ARC method still maintains high correlation/low bias with in-situ measurements.  The exclusion of PM inputs tends to underestimate locally heavy rainfall, however the ARC performs well in capturing the large scale distribution of precipitation.

 Due to inconsistencies found in the initial ARC reprocessing, the ARC1 dataset no longer responded to current needs for operational climate monitoring.  Random outliers, and a large multi-year dry bias associated with erroneous calibrations in the Meteosat First Generation (MFG) IR data from EUMETSAT.  The acquisition of historical, recalibrated IR data and daily summary GTS gauge data has prompted CPC to reconstruct the ARC climatology dataset from 1983-present. R = CC (C nt – SC)T b = B / [ ln(R) – A ]  Analytic relationship derived from Planck’s law was applied to relate Radiance to Temperature:  For ARC2, we acquired tables listing relationship between Radiance (R) and Brightness temperatures (T b ) for each MFG satellite from EUMETSAT.  From these tables, constants A & B were recomputed using best fit logarithmic model per Meteosat satellite 2-7 ( ).

 After IR correction, a reprocessing of IR and GTS data was performed from to reconstruct the ARC2.  From 2006-present, pre-calibrated Meteosat Second Generation (MSG) IR data has been continuously forwarded to CPC for operations on a daily basis. This was used to complete the ARC2 to present.  Mean spatial Max/Min’s consistent with Congo rain forest, lower Gulf of Guinea region, Sahara, St. Helena’s high.  Annual maximums (Feb-May) occur when convection extremely active within ITF over GoG, equatorial central and eastern Africa  Annual minimum (May-Sep) confined to N. tropical belt (WAM), followed by gradual increase in the south (SAM) by fall.

 Product Inter-comparison  Interannual Variability Product correlation matrix of mean monthly rainfall (n=324) ARC2ARC1(95-09)RFE (01-09)GPCPCMAPPREC/L ARC (0.78)0.97 (0.84)0.86 (0.85)0.86 (0.79)(0.82) ARC1 (95-09)0.83 (0.78)-0.95 (0.81)0.73 (0.71)0.74 (0.71)(0.71) RFE2 (01-09)0.97 (0.84)0.95 (0.81)-0.90 (0.90) (0.87) GPCP0.86 (0.85)0.73 (0.71)0.90 (0.90)-0.94 (0.93)(0.91) CMAP0.86 (0.79)0.74 (0.71) (0.93)-(0.89) PREC/L(0.82)(0.71)(0.87)(0.91)(0.89)-

Product Inter-comparison of annual and seasonal rainfall means in mm/day ( ) AnnualDJFMAMJJASON ARC21.57 (1.55)1.77 (1.85)1.88 (1.71)1.18 (1.15)1.45 (1.50) ARC1 (95-09)1.47 (1.34)1.70 (1.54)1.77 (1.38)1.10 (1.11)1.32 (1.35) RFE2 (01-09)1.51 (1.44)1.70 (1.62)1.73 (1.42)1.20 (1.25)1.40 (1.48) GPCP1.64 (1.72)1.78 (1.93)1.79 (1.73)1.44 (1.50)1.57 (1.72) CMAP1.63 (1.62)1.76 (1.80)1.81 (1.63)1.44 (1.47)1.50 (1.59) PREC/L(1.68)(1.86)(1.71)(1.50)(1.66)  Product Inter-comparison  Annual Cycle

 Predominant area of summer disagreement found in the Gulf of Guinea region  Next step was to develop independent (non-GTS) gauge datasets for validation to determine either: 1.An artifact introduced during the reprocessing of the ARC2 2.A systematic bias in the ARC2  Product Inter-comparison  Mean Seasonal Spatial Differences

 The Guinean Rainfall Independent Dataset (GRID) consisted of 248 stations covering GoG and lower Sahel from June-September,  8-years largest overlapping period for validation  GRID rainfall computed over 2.5° grid and compared with respective grid estimates for 32 months  All products showed reasonable agreement with GRID, however GPCP, CMAP, and PREC/L outperformed ARC2.  Suggested ARC2 summer dry bias is systematic  GTS data? Corr = 0.83 RMSE = 2.52 Bias = 1.02 Corr = 0.65 RMSE = 3.50 Bias = 0.90 Corr = 0.85 RMSE = 2.60 Bias = 0.83 Corr = 0.67 RMSE = 3.71 Bias = 0.76

 An examination of daily GTS reporting frequency  GTS reporting rates in The Gambia, Guinea-Bissau, Guinea, Sierra Leone, and Nigeria as low as 30% from  During validation period, reporting rates as low as 6% in Guinea- Bissau and Sierra Leone, which ranked the lowest compared to all Africa countries ( ~22 viable ground measurements per year).  Areas with lowest reporting rates collocated over areas with highest RMSE, and lowest correlation between ARC2 and GRID data. The opposite is observed further north in Sahel.  Histogram suggests relationship between availability of daily GTS data and estimation performance in the ARC2 -> Helps explain summer dry bias over long term record.

 West Africa Sahel Validation (WAGA) Jun-Sep, (n=133)  ARC2 outperforms 3B42v6, CMORPH with lowest RMSE, as others show tendency to overestimate rainfall in Sahel  Findings are consistent with Jobard et al., 2011  Ethiopia Validation (EGA) Jun-Sep, (n=30)  ARC2 clearly underestimates rainfall with lowest bias, poorer validation scores when elevation was taken into effect.  Two Key Observations  1) 3B42v6 and CMORPH outperform ARC2 despite no real-time gauges are used.  2) However, GTS reporting rates higher over Ethiopia than GoG (~60%). Methodologically, the 3B42v6 uses microwave data to calibrate the IR estimates, while CMORPH does not use IR rainfall retrievals in the final rainfall estimates. The fact that 3B42v6 and CMORPH predominately use microwave information, with no real-time gauge adjustments suggests that underestimations in ARC2 over Ethiopia are likely the result of constant brightness temperature threshold used in the IR rainfall retrieval in both RFE / ARC2 algorithm (i.e. GPI)., which fails to capture warm-cloud precipitation common over coastal and orographic areas Corr = 0.46 RMSE = Bias = 0.99 Corr = 0.43 RMSE = Bias = 1.06 Corr = 0.51 RMSE = Bias = 1.46 Corr = 0.31 RMSE = Bias = 0.55 Corr = 0.32 RMSE = Bias = 0.57 Corr = 0.43 RMSE = 9.67 Bias = 0.77

 Operational Climate Monitoring:  2 Case Studies: East Africa Oct-Dec, 2010 Drought & Southern African Monsoon Season Oct, 2008 – May, 2009 Season Obs (mm/day)GaugeARC23B42RT Mandera, Kenya Nyahururu, Kenya Season Obs (mm/day)GaugeARC23B42RT Ondangwa, Namibia Blantyre, Malawi

 Case Study: Dekadal Evolution of ARC2 & Monthly/Seasonal Comparisons  Taken during core of southern Africa rains season

 The ARC2 dataset is from 1983-present, and updated on a daily basis at CPC  ftp://ftp.cpc.ncep.noaa.gov/fews/AFR_CLIM/ARC2/ ftp://ftp.cpc.ncep.noaa.gov/fews/AFR_CLIM/ARC2/  “Africa Rainfall Climatology Version 2 for Famine Early Warning Systems”: Journal of Applied Meteorology and Climatology (in press)  Comparisons and Validation:  ARC2 vs. ARC1:  Improvement in data quality, and removal of large dry bias.  ARC2 vs. GPCP, CMAP, PREC/L  Spatial distribution, annual cycle, and interannual variability consistent with other climatological rainfall datasets  Systematic differences were observed during summer (dry bias)  However, ARC2 still maintains high correlation (r=0.86) with comparison data over 27-year period.  Validation  ARC2 showed reasonable agreement with independent gauge data, and summer dry bias associated with the unavailability of GTS reports, as well as, deficiencies in the IR based estimates to capture warm cloud precipitation over coastal and orographic areas. These findings have been reported in previous literature (Dinku et al. 2007); (Herman et al. 1997).  Summer dry bias evidenced in the RFE every summer.  Benefits of using the ARC2:  Daily resolution of historical rainfall enables users to examine/conduct studies of extreme events, wet & dry spells, number of raindays, onset/departure of rainfall seasons, etc.  Expected to enhance our understanding of the mechanisms associated with climate variability on shorter time scales (e.g. MJO)  High resolution allows users to examine rainfall phenomenon on local scales that cannot be captured by coarser climate datasets  Instrumental in assessing impacts of rainfall on agriculture  Maintains the same two inputs that remain continuous and homogenous over a long term period, and eliminates the possibility of introducing biases associated with asynchronous inputs, or new remotely-sensed inputs  Important in the context of understanding climate change.