RAMS Input Data Types of input data n

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RAMS Input Data

RAMS Input Data

Types of input data n Meteorological data ¡ ¡ ¡ n First guess gridded

Types of input data n Meteorological data ¡ ¡ ¡ n First guess gridded data – NCEP, ECMWF, etc. Surface observations (single level measurements at the ground) Vertical profile observations (rawindsonde, multi-level met tower, wind profiler, RASS, sodar, etc. ) Surface characteristic data ¡ ¡ ¡ Topography Water surface temperature Land use/vegetation type NDVI Soil type

Where to get data? – Gridded data n Easiest to use in GRIB format

Where to get data? – Gridded data n Easiest to use in GRIB format ¡ NCEP n n ¡ ¡ Reanalysis/Reanalysis 2/GFS data – http: //nomad 3. ncep. noaa. gov/ncep_data GFS – ftp: //ftpprd. ncep. noaa. gov/pub/data/nccf/com/gfs/prod NCAR – some datasets online, others can be purchased ECMWF – must go through EU country, difficult and expensive INPE/CPTEC (Brasíl) – operational forecasts for South America, not sure if/how they distribute Others?

Where to get data? - Surface, upper air n A variety of formats available:

Where to get data? - Surface, upper air n A variety of formats available: ¡ NCAR – past year available online for free, past years can be purchased n n n ¡ ¡ http: //dss. ucar. edu/datasets/ds 464. 0/ http: //dss. ucar. edu/datasets/ds 353. 4/ ADP format – converter available on our web site NCEP – current global obs in METAR format Others?

Where to get data? - Topography n n n Global 30 -second resolution (about

Where to get data? - Topography n n n Global 30 -second resolution (about 1 km) dataset available on our web site Many other sources Typically, local sources may have higher resolution

Where to get data? – Water temp n n n Global 1 -degree resolution

Where to get data? – Water temp n n n Global 1 -degree resolution climatological dataset available on our web site (monthly) Sea-surface temperature sometimes available in first-guess files NCEP has separate files (somewhere!) None of these sources do a good job with inland water (bays, rivers, lakes) because of coarse resolution Some higher-resolution, satellite-derived datasets available for US coast, any for Colombia?

Where to get data? – Land use n n n Global 30 -second resolution

Where to get data? – Land use n n n Global 30 -second resolution (about 1 km) dataset available on our web site from USGS Any simulation should be carefully checked Many regions outside of the US used old data sources Local sources usually best for small regions Must be able to translate to RAMS vegetation class categories

Where to get data? – NDVI n n n Only needed in RAMS v

Where to get data? – NDVI n n n Only needed in RAMS v 6. 0 Global 30 -second resolution (about 1 km) dataset available on our web site (monthly) Used to determine several vegetation characteristics (roughness length, albedo, etc. ) Should be checked carefully for any simulation For short runs and small areas, it may be better to use a specified constant value

Where to get data? – Soil type n n More specifically, soil textural class

Where to get data? – Soil type n n More specifically, soil textural class Only used in RAMS v 6. 0 Global dataset about 5 km resolution available on our web site from UN/FAO Simulations not usually very sensitive, unless soil type is very wrong (e. g. , sand vs. clay)

Format Conversions - Met data n n RAMS requires meteorological data to be in

Format Conversions - Met data n n RAMS requires meteorological data to be in RALPH format (being renamed to GDF) Text format that is easily edited and transferred. Document describes the details Examples:

RALPH (GDF) gridded data 999999 2004 2 7 12 0 0 17 144 73

RALPH (GDF) gridded data 999999 2004 2 7 12 0 0 17 144 73 1 2. 5000 -90. 0000 1 1000 925 850 700 600 500 400 300 70 50 30 20 10 0. 700 1. 200 1. 700 2. 100 2. 600 4. 500 4. 900 5. 300 5. 700 6. 200 7. 700 8. 000 8. 400 8. 700 9. 000 10. 200 10. 400 10. 500 10. 700 11. 100 10. 800 10. 700 10. 600 10. 400 10. 200 9. 300 9. 000 8. 700 8. 400 8. 100 6. 600 6. 200 5. 800 5. 400 5. 000 3. 200 2. 700 2. 200 1. 700 1. 200 -0. 700 -1. 200 -1. 700 -2. 100 -2. 600 -4. 500 -4. 900 -5. 300 -5. 700 -6. 200 -7. 700 -8. 000 -8. 400 -8. 700 -9. 000 -10. 200 -10. 400 -10. 500 -10. 700 -11. 100 -10. 800 -10. 700 -10. 600 -10. 400 -10. 200 0. 0000 250 200 3. 100 6. 600 9. 200 10. 800 11. 100 10. 000 7. 700 4. 500 0. 800 -3. 100 -6. 600 -9. 200 -10. 800 -11. 100 -10. 0000 150 100 3. 500 6. 900 9. 500 10. 900 11. 000 9. 800 7. 400 4. 100 0. 300 -3. 500 -6. 900 -9. 500 -10. 900 -11. 000 -9. 800 4. 000 7. 300 9. 700 11. 000 10. 900 9. 500 7. 000 3. 600 -0. 200 -4. 000 -7. 300 -9. 700 -11. 000 -10. 900 -9. 500

RALPH (GDF) surface obs data 999999 2 5 WINDSPEED m/s WIND_DIRECTION deg TEMPERATURE C

RALPH (GDF) surface obs data 999999 2 5 WINDSPEED m/s WIND_DIRECTION deg TEMPERATURE C DEWPOINT C STN_PRES Pa 2004 07 10 2100 2004 07 10 2100 33663 FTTJ DFFD GOGS GABS GBYD DRRN 48. 450 12. 130 12. 350 12. 400 12. 530 13. 350 13. 480 27. 780 15. 030 -1. 520 -16. 750 -7. 950 -16. 800 2. 170 78. 295. 316. 380. 36. 223. 0. 00 1. 54 3. 60 1. 03 0. 00 2. 57 000 000 360. 140. 160. 280. 300. 360. 100. 000 000 19. 8 25. 0 28. 0 30. 0 000 000 17. 7 23. 0 24. 0 25. 0 000 000 99810. 0 -999. 0 000 999 999 999

RALPH (GDF) upper air data 999999 2 2004 7 11 1200 100000. 000 92500.

RALPH (GDF) upper air data 999999 2 2004 7 11 1200 100000. 000 92500. 000 85000. 000 79300. 000 78900. 000 000 77400. 000 70000. 000 69900. 000 68200. 000 67700. 000 67500. 000 64800. 000 56700. 000 54700. 000 50000. 000 33. 000 1367. 000 2930. 000 5510. 000 7120. 000 9070. 000 10280. 000 11780. 000 13720. 000 16440. 000 18810. 000 21040. 000 24460. 000 01152 0036 31. 497 000 677. 766 000 1367. 511 000 1931. 823 000 1972. 813 000 2076. 026 000 2128. 052 000 2930. 796 000 2942. 086 000 3135. 921 000 3193. 678 000 3216. 913 000 3536. 534 000 4565. 487 000 4837. 922 000 5510. 000 1. 03 000 4. 12 000 8. 23 000 14. 92 000 8. 23 000 2. 06 000 5. 14 000 4. 12 000 2. 06 000 6. 17 000 8. 23 000 0013 67. 28000 12. 00 000 5. 60 000 4. 40 000 3. 20 000 2. 80 000 2. 20 000 2. 00 000 -4. 10 000 -5. 70 000 -5. 30 000 -5. 10 000 -7. 10 000 -13. 50 000 -15. 10 000 -20. 70 000 5. 00 000 220. 00 000 280. 00 000 320. 00 000 340. 00 000 335. 00 000 320. 00 000 40. 00 000 90. 00 000 75. 00 000 95. 00 000 14. 45001 0. 7214 000 0. 9320 000 0. 6007 000 0. 3466 000 0. 5967 000 0. 4365 000 0. 5947 000 0. 7289 000 0. 7346 000 0. 6233 000 0. 1070 000 0. 1721 000 0. 3744 000 0. 1224 000 0. 2164 000 0. 2207 000 31. 00000

Format Conversions - Met data n First-guess ¡ ¡ n GRIB format – fdgrib

Format Conversions - Met data n First-guess ¡ ¡ n GRIB format – fdgrib (from our web site) Other formats – custom converter required Surface/upper-air observations ¡ ¡ NCAR ADP format – dprep-ncar (from our web site - soon!) Other formats - dprep-generic (from our web site)

Format Conversions – Surface data n n RAMS requires topography, SST, etc. in “blocked”

Format Conversions – Surface data n n RAMS requires topography, SST, etc. in “blocked” form. Example file name: EL 40 N 100 W

Format Conversions – Surface data n Converters ¡ ¡ ¡ Topography, SST – mktopo.

Format Conversions – Surface data n Converters ¡ ¡ ¡ Topography, SST – mktopo. f (from our web site) Land use – special version of mktopo. f (contact me) Soil, NDVI for v 6. 0 – new version of mktopo. f (make_blocks), will be on our web site soon.

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