Satellite and reanalysis precipitation products perform poorly over regions with low-density ground observation networks. In order to improve space-dependent parameterization of precipitation estimation models in data-scarce environments, the delineation boundaries of precipitation regimes should be accurately identified. Existing approaches to characterize precipitation regimes by seasonal or other climatological properties do not account for small scale spatial-temporal variability. Precipitation time series can be used to account for this small-scale variability in regime classification. Unfortunately, precipitation products with global coverage perform poorly at small time scales over data scarce regions. A methodology of using satellite-based cloud-top temperature (CTT) time series as a proxy of precipitation time series for precipitation regime classification was developed, and its potential and uncertainty were analyzed. A precipitation regime in this study was defined on the basis of characteristic small-scale temporal distribution and variability of precipitation at a given place. Dynamic time warping was used to calculate the distance between two time series. Criteria to select the optimal temporal scale of time series for clustering and the number of clusters were also developed. The method was validated over Germany and applied to Tanzania, characterized by complex climatology and low density ground observations. This approach was evaluated against precipitation regime classification based on a satellite precipitation product. Results show that CTT outcompetes satellite-based precipitation for classification of precipitation regime classification. The CTT-based classification can be used as precursor to spatially adapted precipitation estimation algorithms where parameters are calibrated by gauge data or other ground-based precipitation observations, and parameterization can be used for satellite-precipitation estimates, precipitation forecasts in numerical or stochastic weather models, etc.

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In this paper, a methodology is proposed to quantitatively evaluate precipitation products for multiple purposes. Evaluation mainly focuses on rainfall characteristics relevant to hydrological or agricultural ap-plications: spatial distribution pattern, effect of aggregation over time, the capture of small-scale variability and seasonality, detection of dry spells and wet spells, and timing and volume of heavy rainfall events. Verification statistics were modified and metrics were reported for extreme weather performance, such as flood and drought monitoring. The analysis was performed for different rainfall categories, over regions dominated by different weather systems or with different topographical structures. The latest versions of seven commonly available, high-resolution rainfall estimates have been evaluated by the method against daily data from 16 rain gauge stations over Tanzania, during 1998–2006. They were TRMM 3B42, CHIRPS, TAMSAT, CMORPH_RAW, CMORPH_BLD, WFDEI_CRU, and CPCU. All products, except for CMORPH_BLD and CPCU, were poorly correlated to gauge data at daily time scale with correlation coefficients < 0.5. Five-day aggregation was the minimum time scale that can be used for the products to reach an accuracy better than monthly-mean of gauge data. Their performance varied across different climatic or topographical regions and different rainfall seasons. Timing of precipitation was inaccurately estimated by all products, particularly for heavy rains, with less than 40% hits. The results of the evaluation procedure allow discrimination between available products and better selection of the product to be used for a specific application, such as crop insurance or flood early warning, under particular climatic conditions.

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Evaluation studies of global precipitation datasets invariably show poor performance over regions with limited gauge availability, including most of the African continent, where reporting rate is lowest of any region in the world. While technical advances lead to a vast increase in sensor information in many domains, functional weather stations have been deteriorating progressively since the 1980s. Recently we have seen an increase through the TAHMO network with more than 500 stations in Africa (www.tahmo.org), but their record is still short. At the same, development of reliable weather services has a growing urgency, in regions that depend on rain-fed agriculture and for cities with fast growing populations prone to extreme rainfall flooding. The question then becomes: given the urgency and poor information quality, what is the best pathway towards services that can support society to build extreme weather resilience in a changing climate? Here, we present a methodology that supports evaluation of precipitation products across a range of rainfall features relevant for hydrological or agricultural applications: spatial distribution patterns, representation of small-scale variability and seasonality, detection of dry and wet spells, and timing and intensity of extreme rainfall. We demonstrate application of the methodology for the case of Tanzania, based on six commonly available, (near)global precipitation datasets and data from 16 rain gauges across the country. The analysis distinguishes between performance across season and for regions dominated by different weather systems or with different topographical structure. One of the conclusions is that 5-day aggregation is the minimum time-scale that can be used for the products to reach a quality better than monthly-mean of gauge data. We also show that performance varies strongly over different regions and seasons. Timing of the precipitation was poorly estimated by all products, particularly for heavy rains. Based on this methodology, precipitation products can be selected based on their strengths with respect to particular applications. It also, and perhaps more importantly, points out the limitations of using products at fine-scale resolutions, where their predictive performance is hardly better than using a climatological average.@en

In this study, we investigate a strategy to accelerate the data assimilation (DA) algorithm. Based on evaluations of the computational time, the analysis step of the assimilation turns out to be the most expensive part. After a study of the characteristics of the ensemble ash state, we propose a mask-state algorithm which records the sparsity information of the full ensemble state matrix and transforms the full matrix into a relatively small one. This will reduce the computational cost in the analysis step. Experimental results show the mask-state algorithm significantly speeds up the analysis step. Subsequently, the total amount of computing time for volcanic ash DA is reduced to an acceptable level. The mask-state algorithm is generic and thus can be embedded in any ensemble-based DA framework. Moreover, ensemble-based DA with the mask-state algorithm is promising and flexible, because it implements exactly the standard DA without any approximation and it realizes the satisfying performance without any change in the full model.@en

Remote sensing, as a powerful tool for monitoring atmospheric phenomena, has been playing an increasingly important role in inverse modeling. Remote sensing instruments measure quantities that often combine several state variables as one. This creates very strong correlations between the state variables that share the same observation variable. This may cause numerical problems resulting in a low convergence rate or inaccurate estimates in gradient-based variational assimilation if improper error statistics are used. In this paper, two criteria or scoring rules are proposed to quantify the numerical robustness of assimilating a specific set of remote sensing observations and to quantify the reliability of the estimates of the parameters. The criteria are derived by analyzing how the correlations are created via shared observation data and how they may influence the process of variational data assimilation. Experimental tests are conducted and show a good level of agreement with theory. The results illustrate the capability of the criteria to indicate the reliability of the assimilation process. Both criteria can be used with observing system simulation experiments (OSSEs) and in combination with other verification scores.@en

Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and the ash plume is usually vertically located in a narrow band, directly assimilating the 2-D ash mass loadings in a three-dimensional (3-D) volcanic ash model (with an integral observational operator) can usually introduce large artificial/spurious vertical correlations.

In this study, we look at an approach to avoid the artificial vertical correlations by not involving the integral operator. By integrating available data of ash mass loadings and cloud top heights, as well as data-based assumptions on thickness, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The 3-D SOO makes the analysis step of assimilation comparable in the 3-D model space.

Ensemble-based DA is used to assimilate the extracted measurements of ash concentrations. The results show that satellite DA with SOO can improve the estimate of volcanic ash state and the forecast. Comparison with both satellite-retrieved data and aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts for the distal part of the Eyjafjallajökull volcano is about 6 h.@en

Volcanic eruptions release a large amount of volcanic ash, which can pose hazard to human and animal health, land transportation, and aviation safety. Volcanic Ash Transport and Dispersion (VATD) models are critical tools to provide advisory information and timely volcanic ash forecasts. Due to the complexity and the uncertainty of many dynamic processes involved in the volcanic ash distribution, even the most advanced VATDs today are not capable to reproduce the reality accurately. It is necessary to integrate available observations in the models for more accurate predictions by employing data assimilation techniques.In addition to a valid VATD, ash emissions, usually used as input so the model, are crucial for the forecasts of the locations and shapes of the ash cloud. In general, the eruption source parameters for the construction of the emission are poorly known, which include Plume Height (PH), Mass Eruption Rate (MER) and vertical distribution of the emission rate. Even when PH can be obtained from ground-based observations in some cases, the emission source computed from this PH and a MER empirically related to this PH remains highly uncertain. Not to mention the volcanoes which are unmonitored or hardly accessible, the PH can merely be retrieved from satellite data with a large uncertainty and temporal insufficiency. Fortunately, satellite instruments are able to observe the movement of an ash cloud with a global coverage. Therefore, this thesis focuses on the estimation of the volcanic ash emissions by assimilating Ash Mass Loadings (AMLs) retrieved from satellite data to improve the accuracy of forecasts. Among all available data assimilation approaches, Four Dimensional Variational assimilation (4D-Var) approach was chosen as a suitable one. 4D-Var seeks an optimal set of parameters, including model states, initial conditions and systematic parameters, by minimizing a cost function which combines the model simulations and observations over a period according to their statistic properties. 4D-Var with a standard form of the cost function is tested in a twin experiment framework, where synthetic observations of ash columns computed from model simulated 3D ash concentrations are used. The results show that Standard 4DVar (Std4DVar) is unable to reconstruct the vertical profile of the emission. The injection layer containing the maximal amount of emission rate cannot be accurately determined. This failure is attributed to the fact that AML data lacks vertical resolution. Using the AMLs, it is difficult to reconstruct the volcanic ash emission presented in forms of an eruption column.To deal with this problem, a Trajectory-based 4D-Var (Trj4DVar) approach is proposed. Trj4DVar reformulates the cost function in a regression type which computes the total difference between observed ash columns and a linear combination of simulated trajectories coupled with a priori emission knowledge. The results of twin experiments show that, for most cases, Trj4DVar is capable of estimating the input emission column when a large assimilation window (> 6 hours) is used. The twin experiments is repeated where different values of noise are given in the synthetic observations or perturbations are used in the meteorologic data. The outcomes show that there is still a small possibility that Trj4DVar fails to determine the injection height accurately. Being disturbed by the weather condition (light and cloud, etc) at that moment, satellite instrument can be hampered to observe the ash cloud, which may increase the possibility of failure for the use of Trj4DVar. To remedy this, Trj4DVar is modified to incorporate observations of PH and MER in addition to satellite AMLs. The modified Trj4DVar is shown to be able accurately estimate the injection height based on the results of twin experiments.When it comes to using real-life field data, the situation is more complicated. The detection of volcanic ash can be disturbed by the weather condition such as water vapor. This will result in observations of undetected or wrongly-detected ash. Besides, many sensors ,such as UV and visible sensors, have limited temporal coverage which can only observe during daylight. In order to find effective method in dealing with the temporal and sometimes spatial insufficiency of the data, investigations are carried out on how to use the data properly to benefit more and produce a reasonable estimate. A prepossessing procedure and guidance on the proper use of satellite data are presented.Finally, a deeper analysis is given on the failure of using Std4DVar in this application. It is found that using Std4DVar to assimilate remote sensing data can be tricky. Remote sensing measures quantities that combine several state variables. This creates Sensor-Induced Correlations between the state variables which share the same observation variable and may be physically unrelated. This may cause numerical problems resulting in a low convergence rate or inaccurate estimates of parameters using gradient-based variational assimilation if an erroneous or improper specification of error statistics is adopted. These problems are usually ignored when a reasonable result is obtained, or are avoided by reducing the 3D model to a 2D model. However, it results in significantly unreliable and misguiding estimates for the application in this thesis. Two criteria are proposed to quantify the negative effects of the SICs, which give indications of the effectiveness of the assimilation process and the forecast quality. They are simple to implement and very practical for the use of remote sensing data. They are tested in the twin experiments. The results show that they are able to give evaluation on the design and configuration of the assimilation system with remote sensing data.@en

In this paper, we reconstruct the vertical profile of volcanic ash emissions by assimilating satellite data and ground-based observations using a modified trajectory-based 4D-Var (Trj4DVar) approach. In our previous work, we found that the lack of vertical resolution in satellite ash column data can result in a poor estimation of the injection layer where the ash is emitted into the atmosphere. The injection layer is crucial for the forecast of volcanic ash clouds. To improve estimation, Trj4DVar was implemented, and it has shown increased performance in twin experiments using synthetic observations. However, there are some cases with real satellite data where Trj4DVar has difficulty in obtaining an accurate estimation of the injection layer. To remedy this, we propose a modification of Trj4DVar, test it with synthetic twin experiments, and evaluate real data performance. The results show that the modified Trj4DVar is able to accurately estimate the injection height (location of the maximal emission rate) by incorporating the plume height (top of the ash plume) and mass eruption rate data obtained from ground-based observations near the source into the assimilation system. This will produce more accurate emission estimations and more reliable forecasts of volcanic ash clouds. Also provided are two strategies on the preprocessing and proper use of satellite data.@en

The forecast accuracy of distal volcanic ash clouds is important for providing valid aviation advice during volcanic ash eruption. However, because the distal part of volcanic ash plume is far from the volcano, the influence of eruption information on this part becomes rather indirect and uncertain, resulting in inaccurate volcanic ash forecasts in these distal areas. In our approach, we use real-life aircraft in situ observations, measured in the northwestern part of Germany during the 2010 Eyjafjallajokull eruption, in an ensemble-based data assimilation system combined with a volcanic ash transport model to investigate the potential improvement on the forecast accuracy with regard to the distal volcanic ash plume. We show that the error of the analyzed volcanic ash state can be significantly reduced through assimilating real-life in situ measurements. After a continuous assimilation, it is shown that the aviation advice for Germany, the Netherlands and Luxembourg can be significantly improved. We suggest that with suitable aircrafts measuring once per day across the distal volcanic ash plume, the description and prediction of volcanic ash clouds in these areas can be greatly improved.@en

Since the 2010 Eyjafjallajökull volcano eruption caused a big problem to aviation and economy, improvement on volcanic ash forecast has been put onto the research agenda. Satellite-based measurements are considered as the most common and cheapest type of volcanic ash observations. However, due to its intrinsic functionality, satelliteretrived two-dimensional data can not be easily and directly combined with a three-dimensional volcanic ash model to improve volcanic ash forecasts continuously. Here we propose a satellite observational operator to transfer 2D volcanic ash mass loadings to 3D concentrations. The uncertainties of reconstructed 3D ash concentrations are also quantified. Sequential data assimilation is used to continuously assimilate the reconstructed volcanic ash concentrations. The results are evaluated in a multiobservational network including satellite-based measurements and aircraft in-situ measurements. Here we show for long-time assimilating satellite-based measurements, Ensemble Squre Root Filter (EnSR), as a common sequential data assimilation technique, is more efficient than Ensemble Kalman Filter (EnKF) because the ensemble size required for EnSR is considerable less than the ensemble size of EnKF for a comparable assimilation performance. Moreover, the forecast after assimilation is validated to be accurate and valid within 15 hours. @en

Wildfires are one of the major environmental issue in the Mediterranean area. Prescribed burning (PB) is increasingly used in Europe as a practice to reduce fire risk, through dead fine fuel reduction. Several studies have focused on fire effects on vegetation and soil microbial community, but very few on ecosystem processes involved in water cycle. This study aims to estimate interception by the litter and fermentation layer and the successive evaporation flux in laboratory conditions, using a water balance and 2H and 18O isotopes mass balance calculation, in order to assess PB effects on the hydrology and ecosystem in pine plantations. PB was carried out in spring 2014 in three pine plantations of Southern Italy, dominated by Pinus halepensis (Cilento, Vallo di Diano e Alburni National Park, CVDANP), P. pinaster (Vesuvio National Park, VNP) and P. pinea (Castel Volturno Nature Reserve, CVNR). A dataset concerning the effects of PB on vegetation structure, floristic composition, microbial biomass and activity in the fermentation layer and 5-cm of soil beneath is available for the same stands. In each plantation, two cores of litter and fermentation layer were sampled in a burned area and in a near unburned area (control), respectively, with a collector to extract an “undisturbed” core. Then, each core was transferred in a lysimeter installed in the Water Lab of Delft University of Technology. In total, three lysimeters were set up and each experiment was carried out in duplicate. The laboratory had constant temperature, and both temperature and relative humidity were recorded every 15 minutes. To simulate rainfall, 1 litre of tap water was sprinkled uniformly on the lysimeter with a plant spray (equivalent to 32 mm of rain). The precipitation was sprinkled every 3 days for a period of two months. Soil moisture and temperature were measured during the experiment every 15 minutes in the top and bottom of the litter and fermentation layer. Interception water was collected for isotope analysis from every layer with Rhizon soil moisture samplers by applying a vacuum with 5 ml syringes. Samples were collected two times per day (in the morning and in the evening) and at two different depths for each layer ( 4 cm and 7 cm in litter layer and 10 and 15 cm in fermentation layer) until 2 days after rain simulation. Water samples were analysed with laser spectrometry using the liquid water isotope analyser (LGR-LWIA). The influence of different litter layers and PB on interception and litter layer evaporation was assessed. Then, the evaporation flux measured using the lysimeter was compared with the calculated evaporation flux using the isotopes mass balance. Generally, the preliminary results indicate a slight increase in evaporation flux in burned areas compared to the controls, in P. pinea and P. pinaster stands. By contrast, in P. halepensis stand, a significant decrease in evaporation flux was detected in prescribed burned plot. The isotope mass balance method to measure litter evaporation is promising and could be used in future, in-situ, measurements of evaporation from the litter layer. @en

The 2010 Eyjafjallajökull volcano eruption had serious consequences to civil aviation. This has initiated a lot of research on volcanic ash forecast in recent years. Ensemble-based data assimilation uses the observation data to improve the parameter and state estimation and subsequently the volcanic ash forecast accuracy. Due to the computational complexity of ensemble-based algorithms and the large scale of real-life applications, application of these methods usually introduces a large computational cost, particularly in the analysis step of assimilation processes. Because the other time-consuming part in the single CPU case, the forecast step, can be efficiently and easily parallelized. In this study, we focus on speeding up of the analysis step. For volcanic ash assimilation of aircraft-based measurements, the most time-consuming part in the analysis step has been shown to be the computation of the Kalman gain matrix. After a careful study on the characteristics of ensemble ash states, we propose a model-reduced Kalman gain (MR-Gain) approach which transforms the ensemble state matrix into a low-rank matrix by a multiplication with an index matrix which recorded the sparsity information of the ensemble state matrix, and thus the computational cost of all the ensemble-related matrix multiplications are reduced. After the computation of Kalman gain, using the previously recorded state index, the full analyzed ensemble states are reconstructed. The result shows the MR-Gain approach is exact, which can be used to replace the original full matrix with a much low computation cost. Computer experiments show that the computing time for the analysis step with the new approach is a factor of ten times faster than the conventional analysis step. The result also shows that with the accelerated analysis step in volcanic ash assimilation system, the total amount of computing time for volcanic ash forecast can be significantly reduced by up to a factor of 5. @en