prediction error in wildfire forecasting

Halmar Halide @ physics unhasCICES 2014, Kendari

Halmar Halide@physics unhas, CICES 2014, Kendari

PREDICTION ERRORS IN FORECASTING WILDLAND FIRES USING LAND AND OCEANIC TEMPERATURE INDICES:

HOW LOW CAN WE GO?*

Halmar Halide

Hydrometeorology Laboratory, Physics Dept, FMIPA Universitas Hasanuddin, Makassar, INDONESIA

*) Presented for the Celebes International Conference on Earth Sciences, Swiss-Bell Hotel, Kendari, 11th November 2014.

Background: BNPB, Wildland Fire events (Indonesia, USA) and suppression cost (USA)

Modeling Interannual Fire events (USA cases): simple MR and Fire Persistence and rolling-validation, skill assessment (RMSE, CORR)

Implications: use of a more complex model for the USA fires, model adaptation for the Indonesia fires



http://geospasial.bnpb.go.id/pantauanbencana/data/datakbhutanall.php


http://www.nifc.gov/fireInfo/fireInfo_stats_totalFires.html


http://www.nifc.gov/fireInfo/fireInfo_documents/SuppCosts.pdf

Suppression Costs (1985-2013)

http://www.phrases.org.uk/meanings/385400.html

Halmar Halide @ physics unhas CICES 2014, Kendari

DISASTER IN THE AL QURAN

Mushbah: segala sesuatu yang tak menyenangkan QS 42 (30); 64 (11)

iqab, adzb, dharra, bas, sayyit

Bal: nyata/tampak QS 86 (9);ujian QS 26 (7), 27 (40), 89 (15-17) , 3 (154)

Fitnah: ujian QS 8 (25,28), 21 (35)

http://gayonusantara.blogspot.com/2013/07/bencana-alam-menurut-perspektif-al-quran_8280.html

Thfn/sail : banjir QS 7 (133), 29 (14), 11 (42),34 (16)

rajfah : gempa QS 7 (77-79, 91, 131-136, 155)29 (37), 7

al-rh: angin QS 69 (6-7), 41 (16), 46 (24), 51 (41-42), 33 (9), 17 (69), 69 (6-7)

shiqah: petir QS 41 (13,17), 51 (43-44) , 7 (79)

Sijjil : hujan batu QS 11 (82-83)


TRMM=Tropical Rain Measuring MissionHalmar Halide@physics unhas, CICES 2014,

Kendari


http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml


http://gradworks.umi.com/36/28/3628955.html


RECIPE:

Model: MR (Multiple Regression), PersistenceModel Input/Predictors: Past annual ENSO, NTI, Fire eventsModul Output/Predictant: next year fire events

Validation: Rolling Validation/ One-step-ahead forecast

training testing

training testing , etc

Prediction skill: Pearson correlation and RMSE

Model inputs Pearson correlation RMSE

Past NTI 0.54 52201

Past Fire, ENSO & NTI 0.92 40348

Past ENSO 0.95 39558

Past Fire 0.96 38574

Persistence* 0.99 38397

* : no modeling was involved



Model inputs

Pearson correlation

RMSE

Past NTI 0.54 52201

Past Fire, ENSO & NTI

0.92 40348

Past ENSO 0.95 39558

Past Fire 0.96 38574

Persistence 0.99 38397

Bersama..Kita bisa!

dan tentu sajalebih cepat!Halmar Halide@physics unhas, CICES 2014, Kendari

Documents

prediction error in wildfire forecasting