Verification of real-time WRF-ARW forecast in IMD during monsoon 2010

Performance of the mesoscale model WRF-ARW has been evaluated for whole monsoon season of 2011. The real-time model forecasts are generated day to day in India meteorological Department for short-range weather prediction over the Indian region. Verification of rainfall forecasts has been carried out against observed rainfall analysis whereas for all other meteorological parameters verification analysis which was generated using WRFDA assimilation system. Traditional continuous scores and categorical skill scores are computed over seven different zones in India in the verification of rainfall. For other parameters (upper-air as well as surface), continuous scores are evaluated with temporal and spatial features during whole season. The forecast errors of meteorological parameters other than rainfall are analyzed to portray the model efficiency in maintaining monsoon features in large scale along with localized pattern. In the study, time series of errors throughout the season also has been maneuvered to evaluate model forecasts during diverse phases of monsoon. Categorical scores suggest the model forecasts are reliable up to moderate rainfall category for all seven zones. But, rainfall areas with rainfall above 35.5 mm per day associated with migrated weather system from Indian seas could not be predicted as the model displaces them in the forecast. The verification for a whole monsoon season has shown that the model has capability to predict orographic rainfall for the interactive areas with low level monsoon flow over Western Ghats. The model efficiency are in general brought out for a single monsoon season and errors characteristics are discussed for further improvement which could not perceived during real-time use of the model.

Observations and Modelling of Precipitation in the Southern Alps of New Zealand

<p>Precipitation in the central Southern Alps affects glaciation, river flows and key economic activities, yet there is still uncertainty about its spatial distribution and primary influences. Long-term and future patterns of New Zealand precipitation can be estimated by the HadRM3P regional climate model (RCM) - developed by the United Kingdom Met Office - but orographic rainfall in the steep and rugged topography of the Southern Alps is difficult to simulate accurately at the 30-km resolution of the RCM. To quantify empirical relationships, observations of surface rainfall were gathered from rain gauges covering a broad region of the South Island. In four transects of the Hokitika, Franz Josef and Haast regions, the mean annual precipitation maxima of objectively interpolated profiles are consistently located 7-11 km southeast of the New Zealand Alpine Fault. The magnitude and shape of the rainfall profile across the Southern Alps are strongly influenced by the 850-hPa wind direction to the north of the mountain range, as determined by comparing rain-gauge observations to wind vectors from NCEP/NCAR Reanalysis 1. The observed profile of orographically enhanced rainfall was incorporated into a trivariate spline in order to interpolate precipitation simulated by the RCM. This downscaling method significantly improved the RCM's estimates of mean annual rainfall at stations in the Southern Alps region from 1971 to 2000, and RCM projections of future rainfall in mountainous regions may be similarly refined via this technique. The improved understanding of the observed rainfall distribution in the Southern Alps, as gained from this analysis, has a range of other hydrological applications and is already being used in 'downstream' modelling of glaciers.</p>

Observations and Modelling of Precipitation in the Southern Alps of New Zealand

<p>Precipitation in the central Southern Alps affects glaciation, river flows and key economic activities, yet there is still uncertainty about its spatial distribution and primary influences. Long-term and future patterns of New Zealand precipitation can be estimated by the HadRM3P regional climate model (RCM) - developed by the United Kingdom Met Office - but orographic rainfall in the steep and rugged topography of the Southern Alps is difficult to simulate accurately at the 30-km resolution of the RCM. To quantify empirical relationships, observations of surface rainfall were gathered from rain gauges covering a broad region of the South Island. In four transects of the Hokitika, Franz Josef and Haast regions, the mean annual precipitation maxima of objectively interpolated profiles are consistently located 7-11 km southeast of the New Zealand Alpine Fault. The magnitude and shape of the rainfall profile across the Southern Alps are strongly influenced by the 850-hPa wind direction to the north of the mountain range, as determined by comparing rain-gauge observations to wind vectors from NCEP/NCAR Reanalysis 1. The observed profile of orographically enhanced rainfall was incorporated into a trivariate spline in order to interpolate precipitation simulated by the RCM. This downscaling method significantly improved the RCM's estimates of mean annual rainfall at stations in the Southern Alps region from 1971 to 2000, and RCM projections of future rainfall in mountainous regions may be similarly refined via this technique. The improved understanding of the observed rainfall distribution in the Southern Alps, as gained from this analysis, has a range of other hydrological applications and is already being used in 'downstream' modelling of glaciers.</p>

Performance of daily satellite-based rainfall in groundwater basin of Merapi Aquifer System, Yogyakarta

Evaluation of the performance of daily satellite-based rainfall (CMORPH, CHIRPS, GPM IMERG, and TRMM) was done to obtain applicable satellite rainfall estimates in the groundwater basin of the Merapi Aquifer System (MAS). Performance of satellite data was assessed by applying descriptive statistics, categorical statistics, and bias decomposition on the basis of daily rainfall intensity classification. This classification is possible to measure the performance of daily satellite-based rainfall in much detail. CM (CMORPH) has larger underestimation compared to other satellite-based rainfall assessments. This satellite-based rainfall also mostly has the largest RMSE, while CHR (CHIRPS) has the lowest. CM has a good performance to detect no rain, while IMR (GPM IMERG) has the worst performance. IMR and CHR have a good performance to detect light and moderate rain. Both of them have larger H frequencies and lower MB values compared to other satellite products. CHR mostly has a good performance compared to TR (TRMM), especially on wet periods. CM, IMR, and TR mostly have a good performance on dry periods, while CHR on wet periods. CM mostly has the largest MB and lowest AHB values. CM and CHR have better accuracy to estimate rain amount compared to IMR and TR. All in all, all 4 satellite-based rainfall assessments have large discrepancy compared with rain gauge data along mountain range where orographic rainfall usually occurs in wet periods. Hence, it is recommended to evaluate satellite-based rainfall with time series of streamflow simulation in hydrological modeling framework by merging rain gauge data with more than one satellite-based rainfall than to merge both IMR and TR together.

Post landslide Investigation of Shallow Landslide: A case study from the Southern Western Ghats, India

Many mountainous regions in the tropics witnessed extreme orographic rainfall episodes in the recent past. The portions of the Western Ghats that fall on the Kerala state also experienced extreme climatic conditions in floods and landslides in 2018 and 2019. More than a thousand small and large landslides occurred during that period in the State's Western Ghats regions. The landslide at Kavalapara in the Malappuram district in 2019 is at the top in the state regarding the causalities, financial loss, and spatial spread. This study is based on a comprehensive field investigation at the Kavalappara landslide site and we developed a detailed landslide susceptibility map with the local community's involvement. The massive landslide covers 0.34 Sq.km (34 hectares) triggered by the unprecedented monsoon rainfall coupled with unsustainable agricultural practices. The area's risk zones have been identified and spatially mapped with the help of a detailed field investigation using Geographic Information System (GIS) and remote sensing technology. The output of the study can be used for the policymakers and planners working in landslide-prone areas.

Introduction of a simple formula for estimating approximate intensity-duration-frequency curves from daily rain gauge data

&lt;p&gt;A simple formula for estimating approximate values of return levels for sub-daily rainfall is presented. It was derived from a combination of simple mathematical principles, approximations and fitted to 10-year return levels taken from intensity-duration-frequency (IDF) curves representing 14 sites in Oslo. The formula has subsequently been evaluated against IDF curves from independent sites elsewhere in Norway. Since it only needs 24 h rain gauge data as input, it can provide approximate estimates for the IDF curves used to describe sub-daily rainfall return levels. In this respect, it can be considered as a means of downscaling regarding the timescale, given an approximate power-law dependency between temporal scales. One clear benefit of this framework is that observational data is far more abundant for 24 hr rain gauge records than for sub-daily measurements. Furthermore, it does not assume stationarity and is well-suited for projecting IDF curves for a future climate. This method also provides a framework that strengthens the connection between climatology and meteorology to hydrology, and can be applied to risk management in terms of flash flooding. The proposed formula can also serve as a 'yardstick' to study how different meteorological phenomena with different timescales influence the local precipitation, such as convection, weather fronts, cyclones, atmospheric rivers, or orographic rainfall. An interesting question is whether the slopes of the IDF curves change as a consequence of climate change and if it is possible to predict how they change. One way to address this question is to apply the framework to simulations by convective-permitting regional climate models that offer a complete representation of both sub-daily and daily precipitation over time and space.&amp;#160;&lt;/p&gt;

Performance of Daily Satellite-Based Rainfall in Groundwater Basin of Merapi Aquifer System, Yogyakarta

Evaluation of the performance of daily satellite-based rainfall (CMORPH, CHIRPS, GPM IMERG, and TRMM) was done to obtain applicable satellite rainfall estimates in groundwater basin of Merapi Aquifer System (MAS). Performance of satellite data was assessed by applying descriptive statistics, categorical statistics, and bias decomposition on the basis of daily rainfall intensity classification. This classification is possible to measure the performance of daily satellite-based rainfall in much detail.CM (CMORPH) has larger underestimation compared to other satellite-based rainfall. This satellite-based rainfall also mostly has the largest RMSE, while CHR (CHIRPS) is the lowest. CM has a good performance to detect no rain, while IMR (GPM-IMERG) has the worst performance. IMR and CHR have a good performance to detect light and moderate rain. Both of them have larger H frequencies and lower MB values compared to other satellite products. CHR mostly has a good performance compared to TR (TRMM) especially on wet periods. CM, IMR, and TR mostly have a good performance on dry periods, while CHR on wet periods. CM mostly has the largest MB and lowest AHB values. CM and CHR have better accuracy to estimate rain amount compared to IMR and TR. All in all, all 4 satellite-based rainfall has large discrepancy compared with rain gauge data along mountain range where orographic rainfall usually occurs in wet periods. Hence, it is recommended to evaluate satellite-based rainfall with time series of streamflow simulation in hydrological modeling framework by merging rain gauge data with more than one satellite-based rainfall except to merge both IMR and TR together.

Mechanical forcing of the North American monsoon by orography

The core of the North American monsoon consists of a band of intense rainfall along the west coast of Mexico[1, 2] and is commonly thought to be caused by thermal forcing from both land and the elevated terrain of that region[3-5]. Here we use observations, a global climate model, and stationary wave solutions to show that this rainfall maximum is instead generated when Mexico's Sierra Madre mountains mechanically force an adiabatic stationary wave by diverting extratropical eastward winds toward the equator; eastward, upslope flow in that wave lifts warm and moist air to produce convective rainfall. Land surface heat fluxes do precondition the atmosphere for convection, particularly in summer afternoons, but even if amplified are insufficient for producing the observed rainfall maximum. These results, together with dynamical structures in observations and models, indicate that the core monsoon should be understood as convectively enhanced orographic rainfall in a mechanically forced stationary wave, not as a classic, thermally forced tropical monsoon. This has implications for the response of the North American monsoon to past and future global climate change, making trends in jet stream interactions with orography of central importance.

A 20-year analysis of disturbance-driven rainfall on O’ahu, Hawai’i

Undisturbed trade-wind conditions comprise the most prevalent synoptic weather pattern in Hawai’i and produces a distinct pattern of orographic rainfall. Significant total rainfall contributions and extreme events are linked to four types of atmospheric disturbances: cold fronts, Kona lows, upper-tropospheric disturbances, and tropical cyclones. In this study, a 20- year (1990-2010) categorical disturbance time series is compiled and analyzed in relation to daily rainfall over the same period. The primary objective of this research is to determine how disturbances contribute to total wet season rainfall on the Island of O’ahu, Hawai’i. On average, 41% of wet seasonal rainfall occurs on disturbance days. Seventeen percent of seasonal rainfall can be directly attributed to disturbances (after a background signal is removed) and as much as 48% in a single season. The intensity of disturbance rainfall (mm/day) is a stronger predictor (r2 = 0.49; p < 0.001) of the total seasonal rainfall than the frequency of occurrence (r2 = 0.11; p = 0.153). Cold fronts are the most common disturbance type; however, the rainfall associated with fronts that cross the island is significantly higher than rainfall produced from non-crossing fronts. In fact, non-crossing fronts produce significantly less rainfall than under mean non-disturbance conditions 76% of the time. While the combined influence of atmospheric disturbances can account for almost half of the rainfall received during the wet season, the primary factor in determining a relatively wet or dry season/year on O’ahu are the frequency and rainfall intensity of Kona Low events.