{"id":4032,"date":"2022-10-25T16:52:35","date_gmt":"2022-10-25T14:52:35","guid":{"rendered":"https:\/\/acmad.org\/?page_id=4032"},"modified":"2022-11-12T23:15:44","modified_gmt":"2022-11-12T22:15:44","slug":"long-range-forecasting","status":"publish","type":"page","link":"https:\/\/acmad.org\/index.php\/long-range-forecasting\/","title":{"rendered":"LONG RANGE FORECASTING"},"content":{"rendered":"\t\t
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Product name<\/th>Short name<\/th>Description<\/th>Time period<\/th>Download<\/th>\n<\/tr>\n<\/thead>\n
Verification Forecast<\/td>Verification<\/td>

this step show the verification of foreacst precipitation issue from previous forecast in precipitation map in percent of average, performance index map forecast verifcation<\/p><\/td>

Monthly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Performance forecast<\/td>Verification<\/td>

Using the forecast map issue 3 to 5 years ago, to identify the good performance of foreacast<\/p><\/td>

<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Climate variability including climatological averages, persistence and trends analyses<\/td>CliVaT<\/td>

Climate variability and trends for the target seasons is generate with monthly precipitation data downloaded using script. These are graphs processed with Excel or R package (see practical guide for seasonal climate forecast step1)<\/p><\/td>

Monthly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Drivers, teleconnections, including composites on the drivers <\/td><\/td><\/td>Monthly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Analog years<\/td>Analog-Years<\/td>

Analog years are the previous years where the behavior of SSTs in ocean basins is close to that of the current year. Comparing the temporal variability and the SST values of the historical years (from 1980 up to date) with current year and the forecast for next three months (see practical guide for seasonal climate forecast step2).<\/p><\/td>

Monthly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Dry and wet years<\/td>wet & dry<\/td>

The wet and dry years are identified on the basis of percent of average precipitation. Years with percent of average precipitation above a threshold (120 or 125% to be chosen) are considered as wet years. Years are considered to be years in which the percent of average precipitation are less than a threshold (75% or 80% to be chosen) are taken as dry years. The choice of thresholds depend on the basis of the climatology of the location (see practical guide for seasonal climate forecast step3).<\/p><\/td>

Monthly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Global sst and precipitation composite for wet and dry year<\/td>SST Composite<\/td>

It is the process of averaging SST anomaly data for wet and dry years. Related maps are used to identify SST patterns associated with wet and dry years, respectively. These patterns are compared with the current and expected evolution of SST anomalies leading to determination of whether or not the coming season can be close to either a wet or a dry year (see practical guide for seasonal climate forecast step4). <\/p><\/td>

Monthly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Cumulative estimated precipitation and anual cycle<\/td>Profile<\/td>

Cumulative precipitation and percent of average time series graph are generated with the current year, cumulative average year 75% and 125% of the average year and analog year. The Graph facilitated drought analysis at grids point or at station (see practical guide for seasonal climate forecast step5).<\/p><\/td>

Monthly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
statiscal forecast with climate predictability tools<\/td>CPT<\/td>

Statistical seasonal precipitation forecast is generated using a statistical package: Climate predictability Tool (CPT). Statistical seasonal precipitation forecast is based on relationships between precipitation as predictants and predictors (example: SST). Output products are probabilistic precipitation with 3 categories (Above average, Near average and Below average) (see practical guide for seasonal climate forecast step6).<\/p><\/td>

Monthly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
global sigles dynamical models long range ensembles forecast <\/td><\/td>

Analysis on SST, temperature and precipitation maps downloaded (see practical guide for seasonal climate forecast step7)<\/p><\/td>

Monthly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Globals dynamical multi models long range ensembles forecats products<\/td>LRF<\/td>

Analysis on SST, temperature and precipitation maps downloaded (see practical guide for seasonal climate forecast step8)<\/p><\/td>

Monthly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Forecast probabilities for exceedance of given tresholds<\/td><\/td><\/td><\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Consoludated continental long range forecast<\/td><\/td><\/td><\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Seasonal Probability of Exceeding threshold<\/td><\/td>

Probability of exceeding a given threshold during the season.<\/p><\/td>

Seasonal<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Onset Monitoring and Forecast<\/td><\/td>

Map showing the onset status (Observe\/Forecased).<\/p><\/td>

Weekly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Cummulated precipitation distribution time serie<\/td><\/td>

Map to plot the rainfall profils for all synoptic stations.<\/p><\/td>

Weekly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t
Product name<\/th>Short name<\/th>Description<\/th>Time period<\/th>Download<\/th>\n<\/tr>\n<\/thead>\n
Ranked annual temperature anomalies<\/td>Rnkd-Temp-Ano<\/td>

Mean annual temperature anomalies computed and ranked<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Temporal annual temperature anomaly <\/td>Temp-Ano-Trnd<\/td>

Mean annual temperature anomalies computed and a graph of temperature anomalies ploted including temperature trend<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Spatial annual temperature anomaly map<\/td>ATemp-Ano-Map<\/td>

Mean annual temperature anomalies computed at all grid-points and a spatiol map produced<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Spatial seasonal
\ntemperature anomaly map<\/td>		STemp-Ano-Map<\/td>

Mean seasonal temperature anomalies computed at all grid-points and a spatiol map produced<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Annual cycle of temperature<\/td>Ann-Cycle<\/td>

Mean annual cycle of temperaturegenerated and a graph produced <\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
spatial annual precipitation in
\n percent of average<\/td>		APrcp-percnt-Ave<\/td>

Mean annual precipitation in percent of average is computed and a map is produced<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
spatial seasonal precipitation in
\n percent of average<\/td>		SPrcp-percnt-Ave<\/td>

Mean seasonal precipitation in percent of average is computed and a map is produced (for continental and regional cases)<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Tropical cyclone tracks<\/td>Trop-Cyclne<\/td>

Recorded systems during the tropical cyclone season over
\nsouthwest Indian Ocean basin is considered<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Tropical cyclone frequency<\/td>Trop-Cyclne-Frq<\/td>

Recorded occurances during the tropical cyclone season over
\nsouthwest Indian Ocean basin is considered<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Observed significant harzards and
\ntheir impacts map<\/td>		Sig-hazards-impct<\/td>

observed significant harzards and their impacts in the year of study obtained from various sources. <\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Table of extreme weather and climate events experienced<\/td>Extrm_Wx_Tble<\/td>

A table of recorded extreme weather and climate events<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Table of observed mean surface temperature anomalies<\/td>Surf_temp_Ano<\/td>

A table of observed mean surface temperature anomalies in the region<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Table of warmest years on record over Africa<\/td>Warmest_years<\/td>

A table of 10 warmest years on record over Africa since 1950<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Table of rate of temperature change over the African continent and sub-regions<\/td>Rte_temp_chnge<\/td>

A table of recorded rate of temperature change over the African continent and sub-regions<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Variability of ENSO<\/td>ENSO<\/td>

A graph showing variability of ENSO<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Variability of Tropical North Atlantic index<\/td>TNA<\/td>

A graph showing variability of TNA<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Variability of Tropical South Atlantic<\/td>TSA<\/td>

A graph showing variability of TSA<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Variability of Tropical Atlantic index<\/td>TAI<\/td>

A graph showing variability of TAI<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n
Variability of Indian Ocean Dipole.<\/td>IOD<\/td>

A graph showing variability of IOD<\/p><\/td>

yearly<\/td>PNG CSV Geojson<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t
Product name<\/th>Short name<\/th>Description<\/th>Time period<\/th>Download<\/th>\n<\/tr>\n<\/thead>\n
Significant wather and climate events map <\/td>SWCE
\n<\/td>

It is based on the analysis of continental seasonal forecasts providing likely expected hazards related to predicted climate anomalies ( see practical guide for seasonal forecasting)<\/p><\/td>

Seasonal <\/td>PNG GeoTiff Geojson<\/td>\n<\/tr>\n
Monthy precipitation in percent of average\t<\/td>Prec%<\/td>

Total precipitation in percentage of average is based on the formula: P(%)= Total current precipitation\/long term (1981-2010) average of precipitation X 100<\/p><\/td>

Month <\/td>PNG GeoTiff Geojson<\/td>\n<\/tr>\n
Standardized Precipitation Index <\/td>SPI<\/td>

The SPI is a statistical monthly indicator that compares the cumulated precipitation during a period of n months with the long-term cumulated precipitation distribution for the same location and accumulation period<\/p><\/td>

Month <\/td>PNG GeoTiff Geojson<\/td>\n<\/tr>\n
Soil moisture anomaly<\/td>SMA<\/td>

The soil moisture product has been chosen for agricultural drought monitoring. It is estimated through atmospheric\/hydrological models. Soil moisture anomaly is the difference between current value and long term average (1981-2010).<\/p><\/td>

Month<\/td>PNG GeoTiff Geojson<\/td>\n<\/tr>\n
vegetation index anomaly<\/td>NDVI<\/td>

The NDVI can be used to measure and monitor plant growth, vegetation cover, and biomass production. <\/p><\/td>

Month <\/td>PNG GeoTiff Geojson<\/td>\n<\/tr>\n
Water level<\/td>WL<\/td>

Sentinel-3A use Altimetric gauging of river and lake water levels as one of mission objectives connected to requirements for water resources management, elevation of the surface of certain inland water bodies and large rivers and flood risk monitoring (see procedure)<\/p><\/td>

Month <\/td>PNG GeoTiff Geojson<\/td>\n<\/tr>\n
Seasonal precipitation forecast <\/td>Prec_FC<\/td>

It is a forecast of how the coming season is likely to be different from climatology in 3 categories Above, Near and Below normal<\/p><\/td>

Seasonal<\/td>PNG GeoTiff Geojson<\/td>\n<\/tr>\n
Seasonal temperature forecast<\/td>Temp_FC<\/td>

It is a forecast of how the coming seasonal temperature is likely to be different from climatology in 3 categories Above, Near and Below normal<\/p><\/td>

Seasonal<\/td>PNG GeoTiff Geojson<\/td>\n<\/tr>\n
African drought monitor<\/td><\/td>

The Africa Drought Monitor focuses on continental, broad-scale conditions by creating composites of Meteorological drought and agricultural with four drought indicator categories. Variables used include; precip in percent of average, soil moisture anomaly, NDVI water levels and SPI ( see practical guide for drought monitor)<\/p><\/td>

Month <\/td>PNG GeoTiff Geojson<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","_eb_attr":"","footnotes":""},"class_list":["post-4032","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/acmad.org\/index.php\/wp-json\/wp\/v2\/pages\/4032","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/acmad.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/acmad.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/acmad.org\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/acmad.org\/index.php\/wp-json\/wp\/v2\/comments?post=4032"}],"version-history":[{"count":27,"href":"https:\/\/acmad.org\/index.php\/wp-json\/wp\/v2\/pages\/4032\/revisions"}],"predecessor-version":[{"id":4886,"href":"https:\/\/acmad.org\/index.php\/wp-json\/wp\/v2\/pages\/4032\/revisions\/4886"}],"wp:attachment":[{"href":"https:\/\/acmad.org\/index.php\/wp-json\/wp\/v2\/media?parent=4032"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}