A case study for cyclone ‘Aila’ for forecasting rainfall using satellite derived rain rate data

caxky dh [kkM+h esa cuus ,oa tehu ls Vdjkus okys pØokrh; rwQkuksa ds ifj.kkeLo:i Hkkjh o"kkZ dh otg ls if’pe caxky ds rV lesr Hkkjr ds iwohZ rV ds yksxksa dh tku eky dks dkQh [krjk jgrk gSA tehu ls Vdjkus okys m".kdfVca/kh; pØokrh rwQkuksa dh otg ls gksus okyh o"kkZ dh ek=k dk iwokZuqeku djuk cgqr dfBu gSA m".kdfVca/kh; pØokrh; rwQkuksa ds nk;js esa vkus okys o"kkZ okys {ks=ksa esa laHkkfor pØokrh; rwQku ls gksus okys o"kkZ lap;u dk iwokZuqeku djus ds fy, mixzg ls izkIr o"kkZ njksa dk mi;ksx fd;k tk ldrk gSA bl 'kks/k i= esa ‘vkbyk’ ds m".kdfVca/kh; o"kkZ ekiu fe’ku ¼Vh- vkj- ,e- ,e-½] mixzg o"kkZ nj vk¡dM+ksa rFkk rwQku ds ns[ks x, ekxZ dk mi;ksx djrs gq, m".kdfVca/kh; pØokr ‘vkbyk’ ds tehu ls Vdjkus ls 24 ?kVsa igys rVh; LVs’kuksa ij o"kkZ dk vkdyu djus dk iz;kl fd;k x;k gSA la;qDr jkT; vesfjdk esa fodflr lqifjfpr rduhd ds vk/kkj ij m".kdfVca/kh; pØokr ‘vkbyk’ ds tehu ls Vdjkus ds 24 ?kaVs igys m".kdfVca/kh; o"kkZ foHko ¼Vh- vkj- ,- ih-½ iwokZuqeku fo’ks"k :i ls rwQku dh fn’kk ds lkeus vkus okys rVh; {ks=ksa ds fy, vPNh o"kkZ dk iwokZuqeku miyC/k djkrk gSA Major threat to the life and property of people on the east coast of India, including West Bengal Coast, is due to very heavy rainfall from landfalling tropical cyclones originated over Bay of Bengal. Forecasting magnitude of rainfall from landfalling tropical cyclones is very difficult. Satellite derived rain rates over the raining areas of tropical cyclones can be used to forecast potential tropical cyclone rainfall accumulations. In the present study, an attempt has been made to estimate 24 hours rainfall over coastal stations before landfall of tropical Cyclone ‘Aila’ using Tropical Rainfall Measuring Mission (TRMM) satellite rain rates data and observed storm track of Aila. Forecast Tropical Rainfall Potential (TRaP), 24 hours prior to landfall for the tropical cyclone ‘Aila’ based on well known technique developed in USA, provides a good rainfall forecast especially for the coastal areas lying at the head of direction of the storm.

EFECTO DE LA CORRIENTE EN CHORRO DE BAJOS NIVELES EN LA OCURRENCIA DE PRECIPITACIÓN EN LA SELVA DEL PERÚ

Se analizó la corriente en chorro de América del Sur (SALLJ, siglas en inglés) y la ocurrencia de precipitación sobre la selva del Perú, tomando en cuenta los datos del modelo atmosférico Global Forecast System (GFS) y datos de precipitación acumulada estimado por el satélite Tropical Rainfall Measuring Mission (TRMM) en los veranos australes comprendidos entre los años 2005 y 2014. Se utilizó la distribución de Weibull para el análisis estadístico del viento meridional del norte y el test estadístico no paramétrico de correlación de Kendall para asociar los eventos SALLJ definidos por los criterios de Whiteman et al. (1997) y Bonner (1968). Los resultados revelan que el comportamiento promedio de la componente meridional del viento fluctúa entre 1.2 y 11.7 m/s con variaciones de +/- 3.2 m/s, registrando un viento máximo de 21.4 m/s. De un total de 39 casos, el 53.8% se identificó con las condiciones propuestas por Whiteman y un 46.2% con las condiciones de Bonner. Se registró una precipitación máxima de 64.00 mm/día y mayor número de días con precipitaciones asociadas a eventos SALLJ para las 00 UTC.

Comparison of ITU models performance in predicting Malaysia′s tropical Rainfall Rate and Rain Attenuation at 26GHz mm-wave propagation

Comparative study of the performance of different ITU model in predicting Malaysia tropical climate properties.

Comparison of ITU models performance in predicting Malaysia′s tropical Rainfall Rate and Rain Attenuation at 26GHz mm-wave propagation

Comparative study of the performance of different ITU model in predicting Malaysia tropical climate properties.

Comparison of forecasting accuracy for Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) using Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim precipitation forecast data for Indonesia

Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim forecast data analyzed using second-order autoregressive AR(2) and space-time-spectra analysis methods (respectively) revealed contrasting results for predicting Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) phenomena over Indonesia. This research used the same 13-year series of daily TRMM 3B42 V7 derived datasets and ERA-Interim reanalysis model datasets from the European Center for Medium-Range Weather Forecasts (ECMWF) for precipitation forecasts. Three years (2016 to 2018) of the filtered 3B42 and ERA-Interim forecast data was then used to evaluate forecast accuracy by looking at correlation coefficients for forecast leads from day +1 through day +7. The results revealed that rainfall estimation data from 3B42 provides better results for the shorter forecast leads, particularly for MJO, equatorial Rossby (ER), mixed Rossby-gravity (MRG), and inertia-gravity phenomena in zonal wavenumber 1 (IG1), but gives poor correlation for Kelvin waves for all forecast leads. A consistent correlation for all waves was achieved from the filtered ERA-Interim precipitation forecast model, and although this was quite weak for the first forecast leads it did not reach a negative correlation in the later forecast leads except for IG1. Furthermore, Root Mean Square Error (RMSE) was also calculated to complement forecasting skills for both data sources, with the result that residual RMSE for the filtered ERA-Interim precipitation forecast was quite small during all forecast leads and for all wave types. These findings prove that the ERA-Interim precipitation forecast model remains an adequate precipitation model in the tropics for MJO and CCEW forecasting, specifically for Indonesia.

Perbandingan spektrum curah hujan harian antara BMKG dan TRMM di DKI Jakarta

Tujuan penelitian ini adalah untuk melihat hubungan korelasi tingkat curah hujan harian dan kumulatif 10 harian antara pengukuran dari stasiun Badan Meteorologi, Klimatologi dan Geofisika (BMKG) dan tropical rainfall measurement mission (TRMM) di Propinsi DKI Jakarta. Dari data curah hujan harian observasi BMKG dan data curah hujan satelit TRMM tersebut dilakukan perhitungan dan analisis menggunakan metode spektral untuk mendapatkan nilai spektrum dari kedua data curah hujan tersebut dengan metode fast fourier transform (FFT) dan dengan bantuan perangkat lunak FTRANS. Setelah nilai amplitudo diperoleh pada kedua data dari MBKG dan TRRM, kedua data dibandingkan untuk mendapatkan nilai hubungan korelasi. Hasil perhitungan spektrum curah hujan harian, nilai korelasi yang didapat di tiga stasiun curah hujan di Provinsi DKI Jakarta sangat lemah. Sedangkan untuk spektrum curah hujan kumulatif 10 harian, nilai korelasi yang didapat cukup kuat untuk stasiun Kemayoran dan Tanjung Priok.

Exploring Temporal Rainfall Variability and Trends Over a Tropical Region Using Tropical Rainfall Measurement Mission (TRMM) and Observatory Data

Kerala is the gateway of the Indian southwest monsoon. The Tropical Rainfall Measurement Mission (TRMM) rainfall data is an efficient approach to rainfall measurement. This study explores the temporal variability in rainfall and trends over Kerala from 1998-2019 using TRMM data and observatory data procured from India Meteorological Department (IMD). Direct comparison with observatory data at various time scales proved the reliability of the TRMM data (monthly, seasonal and annual). The temporal rainfall converted by averaging the data on an annual, monthly and seasonal time scale, and the results have confirmed that the rainfall estimated based on satellite data is dependable. The station wise comparison of rainfall in monsoon season provides satisfactory results. However, estimation of rainfall in mountainous areas is challenging task using the TRMM. In the basins of humid tropical regions, TRMM data can be a valuable source of rainfall data for water resource management and monitoring with some vigilance. In Kerala, the study found an insignificant increase in the southwest monsoon and winter season rainfall during last two decades. The rainfall over Kerala showed uncertainty in the distribution of monthly, seasonal and yearly time scales. This study provides a preview of recent weather patterns that would enable us to make better decisions and improve public policy against climate change.