A variational approach to retrieve rain rate by combining information from rain gauges, radars, and microwave links

Accurate and reliable rain rate estimates are important for various hydrometeorological applications. Consequently, rain sensors of different types have been deployed in many regions. In this work, measurements from different instruments, namely, rain gauge, weather radar, and microwave link, are combined for the first time to estimate with greater accuracy the spatial distribution and intensity of rainfall. The objective is to retrieve the rain rate that is consistent with all these measurements while incorporating the uncertainty associated with the different sources of information. Assuming the problem is not strongly nonlinear, a variational approach is implemented and the Gauss–Newton method is used to minimize the cost function containing proper error estimates from all sensors. Furthermore, the method can be flexibly adapted to additional data sources. The proposed approach is tested using data from 14 rain gauges and 14 operational microwave links located in the Zürich area (Switzerland) to correct the prior rain rate provided by the operational radar rain product from the Swiss meteorological service (MeteoSwiss). A cross-validation approach demonstrates the improvement of rain rate estimates when assimilating rain gauge and microwave link information.

Development of a Technique for Estimating Rain Parameters from Rain Noise Measurements

The measurement of global precipitation is of great importance in climate modeling since the release of latent heat associated with tropical convection is one of the pricipal driving mechanisms of atmospheric circulation.Knowledge of the larger-scale precipitation field also has important potential applications in the generation of initial conditions for numerical weather prediction models Knowledge of the relationship between rainfall intensity and kinetic energy, and its variations in time and space is important for erosion prediction. Vegetation on earth also greatly depends on the total amount of rainfall as well as the drop size distribution (DSD) in rainfall.While methods using visible,infrared, and microwave radiometer data have been shown to yield useful estimates of precipitation, validation of these products for the open ocean has been hampered by the limited amount of surface rainfall measurements available for accurate assessement, especially for the tropical oceans.Surface rain fall measurements(often called the ground truth)are carried out by rain gauges working on various principles like weighing type,tipping bucket,capacitive type and so on.The acoustic technique is yet another promising method of rain parameter measurement that has many advantages. The basic principle of acoustic method is that the droplets falling in water produce underwater sound with distinct features, using which the rainfall parameters can be computed. The acoustic technique can also be used for developing a low cost and accurate device for automatic measurement of rainfall rate and kinetic energy of rain.especially suitable for telemetry applications. This technique can also be utilized to develop a low cost Disdrometer that finds application in rainfall analysis as well as in calibration of nozzles and sprinklers. This thesis is divided into the following 7 chapters, which describes the methodology adopted, the results obtained and the conclusions arrived at.

Reduced space optimal interpolation of daily rain gauge precipitation in Switzerland

The coarse spacing of automatic rain gauges complicates near-real- time spatial analyses of precipitation. We test the possibility of improving such analyses by considering, in addition to the in situ measurements, the spatial covariance structure inferred from past observations with a denser network. To this end, a statistical reconstruction technique, reduced space optimal interpolation (RSOI), is applied over Switzerland, a region of complex topography. RSOI consists of two main parts. First, principal component analysis (PCA) is applied to obtain a reduced space representation of gridded high- resolution precipitation fields available for a multiyear calibration period in the past. Second, sparse real-time rain gauge observations are used to estimate the principal component scores and to reconstruct the precipitation field. In this way, climatological information at higher resolution than the near-real-time measurements is incorporated into the spatial analysis. PCA is found to efficiently reduce the dimensionality of the calibration fields, and RSOI is successful despite the difficulties associated with the statistical distribution of daily precipitation (skewness, dry days). Examples and a systematic evaluation show substantial added value over a simple interpolation technique that uses near-real-time observations only. The benefit is particularly strong for larger- scale precipitation and prominent topographic effects. Small-scale precipitation features are reconstructed at a skill comparable to that of the simple technique. Stratifying the reconstruction method by the types of weather type classifications yields little added skill. Apart from application in near real time, RSOI may also be valuable for enhancing instrumental precipitation analyses for the historic past when direct observations were sparse.

Rainfall estimation by rain gauge-radar combination: a concurrent multiplicative-additive approach

A procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) is proposed for operational rainfall estimation using rain gauges and radar data. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on Barnes' objective analysis scheme (OAS), whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km2. Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, a spatially variable adjustment with multiplicative factors, and ordinary cokriging.

Simulating the daily discharge of the Mandovi River, west coast of India

A hydrological modelling framework was assembled to simulate the daily discharge of the Mandovi River on the Indian west coast. Approximately 90% of the west-coast rainfall, and therefore discharge, occurs during the summer monsoon (June-September), with a peak during July-August. The modelling framework consisted of a digital elevation model (DEM) called GLOBE, a hydrological routing algorithm, the Terrestrial Hydrological Model with Biogeochemistry (THMB), an algorithm to map the rainfall recorded by sparse rain-gauges to the model grid, and a modified Soil Conservation Service Curve Number (SCS-CN) method. A series of discharge simulations (with and without the SCS method) was carried out. The best simulation was obtained after incorporating spatio-temporal variability in the SCS parameters, which was achieved by an objective division of the season into five regimes: the lean season, monsoon onset, peak monsoon, end-monsoon, and post-monsoon. A novel attempt was made to incorporate objectively the different regimes encountered before, during and after the Indian monsoon, into a hydrological modelling framework. The strength of our method lies in the low demand it makes on hydrological data. Apart from information on the average soil type in a region, the entire parameterization is built on the basis of the rainfall that is used to force the model. That the model does not need to be calibrated separately for each river is important, because most of the Indian west-coast basins are ungauged. Hence, even though the model has been validated only for the Mandovi basin, its potential region of application is considerable. In the context of the Prediction in Ungauged Basins (PUB) framework, the potential of the proposed approach is significant, because the discharge of these (ungauged) rivers into the eastern Arabian Sea is not small, making them an important element of the local climate system.

Physically Based, Hydrologic Model Results Based on Three Precipitation Products

The main objective of the study is to examine the accuracy of and differences among simulated streamflows driven by rainfall estimates from a network of 22 rain gauges spread over a 2,170 km2 watershed, NEXRAD Stage III radar data, and Tropical Rainfall Measuring Mission (TRMM) 3B42 satellite data. The Gridded Surface Subsurface Hydrologic Analysis (GSSHA), a physically based, distributed parameter, grid-structured, hydrologic model, was used to simulate the June-2002 flooding event in the Upper Guadalupe River watershed in south central Texas. There were significant differences between the rainfall fields estimated by the three types of measurement technologies. These differences resulted in even larger differences in the simulated hydrologic response of the watershed. In general, simulations driven by radar rainfall yielded better results than those driven by satellite or rain-gauge estimates. This study also presents an overview of effects of land cover changes on runoff and stream discharge. The results demonstrate that, for major rainfall events similar to the 2002 event, the effect of urbanization on the watershed in the past two decades would not have made any significant effect on the hydrologic response. The effect of urbanization on the hydrologic response increases as the size of the rainfall event decreases.

Regionalization of precipitation in India-a review

Regionalization of precipitation refers to delineation of rain gauges in an area into homogeneous groups (clusters or regions). Various regionalization procedures are employed by researchers in hydrometeorology for addressing a wide spectrum of problems. This paper provides an overview of underlying concepts as well as advantages and limitations of procedures that have been developed over the past six decades. Emphasis is given to studies that have been carried out in India. Following this, gaps where more research needs to be focussed are highlighted, and challenges for regionalization in a climate change scenario are discussed.

The Freshwater Biological Association at Wray Castle: Recollections of its first director

In 1937 the Development Commission provided an annual grant to the Freshwater Biological Association to pay for a director and secretary. The author moved to the Lake District in the same year, and at that time T.T. Macan was working on invertebrates; K.R. Allen on fish; C.H. Mortimer on chemistry and physics of the aquatic environment, and Marie Rosenberg on phytoplankton. They were backed by George Thompson as laboratory assistant and Rosa Bullen as secretary. The work of the Association continued and expanded throughout the Second World War with some far-reached discoveries made. For example, the recovery of lake sediment cores and the examination of diatom remains, so starting the discipline of archaeo-limnology. Also, a hydrological survey of the Windermere catchment area found significant traces of sulphuric acid in rain gauges. This was more than 30 years before "acid rain" became fashionable.

Relação entre pluviosidade efetiva e estoque de matéria orgânica de superfície na ciclagem de nutrientes em floresta ombrófila urbana: Parque Nacional da Tijuca - RJ

O estoque de matéria orgânica de superfície refere-se a todo material orgânico decomposto ou em decomposição sobreposto ao solo mineral, bem como ao material orgânico misturado às partículas minerais do horizonte A. Este serve como indicador da funcionalidade e integridade de ecossistemas florestais quando o analisamos sob a perspectiva das "Formas de Húmus". Nesta perspectiva o material orgânico se presenta como disposto em camadas internas que variam no peso e espessura de acordo com a atuação dos agentes decompositores locais, e expressa a ciclagem de nutrientes que se dá a partir dos dois principais processos que sintetizam e/ou modulam o funcionamento dos ecossistemas, a produção e a decomposição da matéria orgânica. O modelo de formas de húmus segue uma classificação desenvolvida na Europa que não se aplica perfeitamente às áreas tropicais pela alta variabilidade de ambientes. Estima-se que fatores locais e particulares como características edáficas, estrutura e composição da vegetação, relevo e o microclima podem distorcer esta classificação e diferir significativamente do modelo global. Esta pesquisa buscou o refinamento deste modelo, buscando entender de que modo a pluviosidade efetiva (chuva que atravessa a copa das árvores e chega ao solo), como fenômeno de natureza microclimática, se relaciona com o estoque de matéria orgânica de superfície (sob a perspectiva das formas de húmus) em ambiente de floresta ombrófila montanhosa urbana.Para tal foram executadas análises físicas sobre o estoque de matéria orgânica de superfície, monitoramento da pluviosidade efetiva com pluviômetro experimentais adaptados para esta demanda e análises químicas sobre a água da chuva após lixiviação da matéria orgânica. Foi montada uma estrutura composta de quinze pluviômetros experimentais em uma encosta florestada no Parque Nacional da Tijuca (Rio de Janeiro - RJ) e instalação de pluviômetros eletrônicos em ambientes de céu aberto em áreas adjacentes a esta encosta pertencentes à área do parque. Os resultados indicam que da chuva que cai na floresta, cerca de 15% é interceptada pelas copas e o restante, parte é interceptada pela matéria orgânica do solo. A camada MFB (ou H) é a que retém mais água, indicando que, florestas que a decomposição é rápida no processo de fragmentação e lenta na mineralização, terão mais água retida no estoque de matéria orgânica. As diferentes camadas vão nutrir o solo de maneira diferenciada quando este processo deriva da lixiviação, como o cálcio que é liberado em a maior quantidade pelas camadas mais fragmentadas como a F e a MFB, o potássio e magnésio têm maior disponibilização quando deriva da camada L, indicando que durante este processo estes nutrientes são mais consumidos do que liberados. Foi perceptível que o sódio é bastante abundante na chuva antes mesmo de atingir a matéria orgânica, confirmando estudos anteriores. A utilização deste sistema proposto de pluviômetros experimentais possibilitou entender como esta estratificação interna do estoque de matéria orgânica de superfície acumula água, pois os métodos tradicionais não se baseiam no modelo de formas de húmus, e possibilitou ainda, o a análise do processo de lixiviação e liberação de nutrientes para o solo.

Influence of climatography on rainfall thresholds for initiation of debris-flows on the California coast [abstract]

EXTRACT (SEE PDF FOR FULL ABSTRACT): Since 1986, the U.S. Geological Survey and National Weather Service have operated a warning system for debris flows triggered by severe rainstorms in the San Francisco Bay region. The NWS tracks storm systems as they approach the region, forecasts precipitation, and observes rainfall with a network of radiotelemetered rain gauges (ALERT). The USGS also monitors ALERT data and compares the observed and forecast rainfall to thresholds for debris-flow initiation. Both groups jointly assess debris-flow hazards and issue public advisories when rainfall conditions reach or approach critical levels.

Distal Deposition of Tephra from the Eyjafjallajökull 2010 Summit Eruption

The 2010 Eyjafjallajökull lasted 39 days and had 4 different phases, of which the first and third (14–18 April and 5–6 May) were most intense. Most of this period was dominated by winds with a northerly component that carried tephra toward Europe, where it was deposited in a number of locations and was sampled by rain gauges or buckets, surface swabs, sticky-tape samples and air filtering. In the UK, tephra was collected from each of the Phases 1–3 with a combined range of latitudes spanning the length of the country. The modal grain size of tephra in the rain gauge samples was 25 um, but the largest grains were 100 um in diameter and highly vesicular. The mass loading was equivalent to 8–218 shards cm2, which is comparable to tephra layers from much larger past eruptions. Falling tephra was collected on sticky tape in the English Midlands on 19, 20 and 21st April (Phase 2), and was dominated by aggregate clasts (mean diameter 85 um, component grains <10 um). SEM-EDS spectra for aggregate grains contained an extra peak for sulphur, when compared to control samples from the volcano, indicating that they were cemented by sulphur-rich minerals e.g. gypsum (CaSO4⋅H2O). Air quality monitoring stations did not record fluctuations in hourly PM10 concentrations outside the normal range of variability during the eruption, but there was a small increase in 24-hour running mean concentration from 21–24 April (Phase 2). Deposition of tephra from Phase 2 in the UK indicates that transport of tephra from Iceland is possible even for small eruption plumes given suitable wind conditions. The presence of relatively coarse grains adds uncertainty to concentration estimates from air quality sensors, which are most sensitive to grain sizes <10 um. Elsewhere, tephra was collected from roofs and vehicles in the Faroe Islands (mean grain size 40 um, but 100 um common), from rainwater in Bergen in Norway (23–91 um) and in air filters in Budapest, Hungary (2–6 um). A map is presented summarizing these and other recently published examples of distal tephra deposition from the Eyjafjallajökull eruption. It demonstrates that most tephra deposited on mainland Europe was produced in the highly explosive Phase 1 and was carried there in 2–3 days.

Orographic precipitation in the tropics: experiments in Dominica

The “natural laboratory” of mountainous Dominica (15°N) in the trade wind belt is used to study the physics of tropical orographic precipitation in its purest form, unforced by weather disturbances or by the diurnal cycle of solar heating. A cross-island line of rain gauges and 5-min radar scans from Guadeloupe reveal a large annual precipitation at high elevation (7 m yr^{−1}) and a large orographic enhancement factor (2 to 8) caused primarily by repetitive convective triggering over the windward slope. The triggering is caused by terrain-forced lifting of the conditionally unstable trade wind cloud layer. Ambient humidity fluctuations associated with open-ocean convection may play a key role. The convection transports moisture upward and causes frequent brief showers on the hilltops. The drying ratio of the full air column from precipitation is less than 1% whereas the surface air dries by about 17% from the east coast to the mountain top. On the lee side, a plunging trade wind inversion and reduced instability destroys convective clouds and creates an oceanic rain shadow.

Orographic Enhancement of Precipitation inside Hurricane Dean

On 17 August 2007, the center of Hurricane Dean passed within 92 km of the mountainous island of Dominica in the West Indies. Despite its distance from the island and its category 1–2 state, Dean brought significant total precipitation exceeding 500 mm and caused numerous landslides. Four rain gauges, a Moderate Resolution Imaging Spectroradiometer (MODIS) image, and 5-min radar scans from Guadeloupe and Martinique are used to determine the storm’s structure and the mountains’ effect on precipitation. The encounter is best described in three phases: (i) an east-northeast dry flow with three isolated drifting cells; (ii) a brief passage of the narrow outer rainband; and (iii) an extended period with south-southeast airflow in a nearly stationary spiral rainband. In this final phase, from 1100 to 2400 UTC, heavy rainfall from the stationary rainband was doubled by orographic enhancement. This enhancement pushed the sloping soils past the landslide threshold. The enhancement was caused by a modified seeder–feeder accretion mechanism that created a “dipole” pattern of precipitation, including a dry zone over the ocean in the lee. In contrast to normal trade-wind conditions, no terrain triggering of convection was identified in the hurricane environment.

Representativity of in situ precipitation measurements - a case study for the LITFASS area in North-Eastern Germany

In situ precipitation measurements can extremely differ in space and time. Taking into account the limited spatial–temporal representativity and the uncertainty of a single station is important for validating mesoscale numerical model results as well as for interpreting remote sensing data. In situ precipitation data from a high resolution network in North-Eastern Germany are analysed to determine their temporal and spatial representativity. For the dry year 2003 precipitation amounts were available with 10 min resolution from 14 rain gauges distributed in an area of 25 km 25 km around the Meteorological Observatory Lindenberg (Richard-Aßmann Observatory). Our analysis reveals that short-term (up to 6 h) precipitation events dominate (94% of all events) and that the distribution is skewed with a high frequency of very low precipitation amounts. Long-lasting precipitation events are rare (6% of all precipitation events), but account for nearly 50% of the annual precipitation. The spatial representativity of a single-site measurement increases slightly for longer measurement intervals and the variability decreases. Hourly precipitation amounts are representative for an area of 11 km 11 km. Daily precipitation amounts appear to be reliable with an uncertainty factor of 3.3 for an area of 25 km 25 km, and weekly and monthly precipitation amounts have uncertainties of a factor of 2 and 1.4 when compared to 25 km 25 km mean values.