Evaluation of high resolution rainfall forecasts over Karnataka for the 2011 southwest and northeast monsoon seasons

IEEE Trans. Geosci. Remote Sensing

2015

Accurate soil moisture data, critical for many applications such as agriculture and estimation of ground water, is limited worldwide, and particularly over India, by the absence of sustained multisite observations. A long-term sustained soil moisture observation at four vertical levels (5, 15, 50, and 100 cm) is now available at several locations over India under a multiinstitutional program Climate Observations and Modeling Network (COMoN) led by CSIR, India. At the same time, a high resolution (0.1 • × 0.1 • ) daily (moving 5-day mean) surface relative soil moisture data set has now become available from the Advanced Scatterometer (ASCAT). However, there is a need to compare remotely sensed data and in situ observations to ensure consistency and quantify uncertainties. This is particularly true for India characterized by diverse climatic zones. We present a comparative analysis of gridded ASCAT soil moisture data and in situ COMoN station data over six locations in India during the period 2010-2013. A multiscale analysis is carried out involving daily, weekly, monthly, and seasonal timescales at different geographical locations. Analyses show that the two data sets are generally consistent, although there are seasonalities in the agreement; the correlation coefficient is higher for the wet season (summer, autumn), and moderate for dry season (winter, spring). The correlation coefficients are ranged from 0.73 to 0.91 and above 99% significance level. The results quantify the reliability and robustness of ASCAT soil moisture over different climatic regions in India; the results also identify certain differences between the two data sets.Index Terms-Advanced Scatterometer (ASCAT), Climate Observations and Modeling Network (COMoN) data, climatology, data quality, multiscale analysis, soil moisture, soil moisture variability.

Section: A Ascat Soil Moisture Datamentioning

confidence: 99%

Section: B In Situ Observations: Csir Comonmentioning

confidence: 99%

A Comparison of ASCAT Soil Moisture Data With <italic>In Situ</italic> Observations Over the Indian Region: A Multiscale Analysis

Bhimala

IEEE Trans. Geosci. Remote Sensing

2015

“…Near-surface observed data have been adopted from Climate Observation and Modelling Network (COMoN) from a meso-scale network of four multi-level profilers in and around Delhi, established by CSIR, India (Goswami et al 2012(Goswami et al , 2014. The observations (Table 1) include high frequency (*30 min) data on temperature, relative humidity, wind speed and wind direction at 3 levels (2, 20, 30 m), soil moisture and soil temperature at four sub-surface levels (1, 15, 50, 100 cm).…”

Section: Meteorological Datamentioning

confidence: 99%

Analysis and quantification of contrasts in observed meteorological fields for foggy and non-foggy days

Sarkar

2015

Meteorol Atmos Phys

The genesis and dynamics of fog take place under certain meteorological conditions. Thus quantification of the differences in different meteorological variables between foggy and non-foggy days can provide insight into the dynamics of fog; such contrasts can also help to identify and quantify precision and the accuracy required in the meteorological forecasts for driving fog prediction models. However, systematic evidence and quantification of differences in meteorological variables for foggy and non-foggy days are missing. Based on analyses of composites of differences between meteorological variables (temperature, relative humidity, wind and dew-depression) for days with no fog, mild fog and dense fog (based on visibility) days over Delhi, a fog-prone metropolis, we show that consistent and appreciable distinctions exist between days with fog and no fog (characterized in terms of hourly visibility) for both December and January. An important finding is that the differences persist not only near the surface, but also at 850 hPa level. Expectedly but consistently, the contrasts were stronger for long-duration (duration C4 h) than those for short duration (duration\2 h) fog; the contrasts were also stronger for dense (visibility \500 m) fog. The average differences between non-foggy and dense fog (duration C4 h) in composite temperature, relative humidity and wind are of the order of 3°C, 10 % and 5 m/s, respectively; these findings can help to assess and validate meteorological forecasts for driving fog prediction. Fog is a high-impact event over many locations world-wide; while the present analysis is for a specific location, the methodology is generic to be applied over any fog-prone location.

“…The forecasts were verified for the 4 year period 2011–2014. Even though the forecast skill of mesoscale models has been evaluated in many studies globally (Coniglio et al , ; Schmidli et al , ; Marsh et al , ; Wen et al , ; Stratman et al , ; Berner et al , ), as well as over the Indian region (Rakesh et al , , , ; Goswami et al , ; Vishnu and Francis, ), very few studies have evaluated rainfall forecast skill for specific agricultural applications. Another important aspect of the present study is that the rainfall forecasts are verified against the observations at comparable resolution from the telemetric rain gauge (TRG) network installed and maintained by Karnataka State Natural Disaster Monitoring Centre (KSNDMC).…”

Section: Introductionmentioning

confidence: 99%

An evaluation strategy of skill of high‐resolution rainfall forecast for specific agricultural applications

Rakesh

Meteorological Applications

2016

Self Cite

ABSTRACT:A strategy for validation of rainfall forecasts for specific agricultural applications is presented. The focus is mainly on the design of specific forecast advisories that are risk-free and useful in spite of their inherent errors. The strategy works for these specific applications because the forecast advisories are based on when NOT to irrigate or apply fertilizer/pesticide because rain is predicted (risk-free because wrong forecast only delays irrigation/application of fertilizer/pesticide within tolerance). Thus, unlike in conventional forecast evaluation, a forecast is considered as valid if the forecasted rain (or no rain) is correct for the day of the forecast (D0C) or the next day or the day after (designated D1C and D2C, respectively), as the farmer can afford to postpone the field application for a couple of days beyond the scheduled date. The methodology has been evaluated for rainfall forecasts over Karnataka (a state in southwest India with nearly 56% of the workforce engaged in agriculture). Here, forecast validation against rain gauge observations is presented at comparable resolutions for the southwest (June to September) and the northeast (October to December) monsoon seasons during 2011-2014. Analyses demonstrate that forecasts over several areas which may appear to be less reliable based on conventional evaluation (D0C) are found to have useful skill for the specific agro-applications as evident from evaluation based on D1C and D2C criteria. Our analysis shows that the evaluation strategy presented is effective during the non-rainy (January-May) season also. It is pointed out that such an approach can help to meet the challenges in designing and implementing best practices in agriculture by combining immediate gains for the end users with long-term sustainability.