Precipitation Fields Research Articles

Spatiotemporal Limitations of Data‐Driven Modeling: An ISINGLASS Case Study

AbstractIonospheric outflow remains difficult to predict because of myriad processes acting over a large range of altitudes, physical regimes, and time scales. This study uses the ionospheric model Geospace Environment Model of Ion‐Neutral Interactions with Transverse Ion Acceleration (GEMINI‐TIA) and sounding rocket campaign data to assess the impact of this spatially and temporally variable energy inputs on ionospheric upflow. The Ionospheric Structuring: In situ and Groundbased Low Altitude Studies B sounding rocket was launched on 03/02/17, into a substorm expansion, and included nearby ground‐based measurements which, along with in situ data, are used to construct electron precipitation and electric field inputs to GEMINI‐TIA for purposes of modeling complicated upflow patterns in two important limits: (a) time variable but spatially constant inputs or (b) spatially variable inputs that are constant in time. Realistic ionospheric responses will likely fall between these two limiting cases and may also include additional energy sources not constrained by the rocket. To investigate mixed spatial and temporal forcing we conduct a couple of simulations driven with ground‐based data that resolves both space and time variations in the forcing but at a much lower spatial and temporal resolution. Results demonstrate that the time history of the ionosphere also plays a significant role when accurately describing the upflow response to auroral heating. It takes up to 20 min for simulations initialized with different conditions to converge, suggesting that at the local scales and altitude range studied here it is necessary to include at least that much hysteresis in data analysis and modeling efforts.

Ensemble Representation of Satellite Precipitation Uncertainty Using a Nonstationary, Anisotropic Autocorrelation Model

AbstractThe usefulness of satellite multi‐sensor precipitation (SMP) and other satellite‐informed precipitation products in water resources modeling can be hindered by substantial errors which vary considerably with spatiotemporal scale. One approach to cope with these errors is to combine SMPs with ensemble generation methods, such that each ensemble member reflects one plausible realization of the true—but unknown—precipitation. This requires replicating the spatiotemporal autocorrelation structure of SMP errors. The climatology of this structure is unknown for most locations due to a lack of ground‐reference observations, while the unique anisotropy and nonstationarity within any particular precipitation system limit the relevance of this climatology to the depiction of individual storm systems. Characterizing and simulating autocorrelation across spatiotemporal scales has thus been called a grand challenge within the precipitation community. We introduce the Space‐Time Rainfall Error and Autocorrelation Model (STREAM), which combines uncalibrated, anisotropic and nonstationary SMP spatiotemporal correlation modeling with a pixel‐scale precipitation error model to stochastically generate ensemble precipitation fields that resemble “ground truth” precipitation. We generate STREAM precipitation ensembles at high resolution (1‐hr, 0.1°) and evaluate these ensembles at multiple scales. STREAM ensembles consistently bracket ground‐truth observations and replicate the autocorrelation structure of ground‐truth precipitation fields. STREAM is compatible with pixel‐scale error/uncertainty formulations beyond those presented here, and could be applied to other precipitation sources such as numerical weather predictions or blended products. Although ground truth is used here to parameterize pixel‐scale uncertainty, if combined with other recent work in SMP uncertainty characterization, STREAM could be used without any ground data.

A GIS-Based Methodology to Combine Rain Gauge and Radar Rainfall Estimates of Precipitation Using the Conditional Merging Technique for High-Resolution Quantitative Precipitation Forecasts in Țibleș and Rodnei Mountains

Rain gauges provide accurate rainfall amount data; however, the interpolation of their data is difficult, especially because of the high spatial and temporal variability. On the other hand, a high-resolution type of information is highly required in hydrological modeling for discharge calculations in small catchments. This problem is partially solved by meteorological radars, which provide precipitation data with high spatial and temporal distributions over large areas. The purpose of this study is to validate a conditional merging technique (CMT) for 15 rainfall events that occurred on the southern slope of the Tibleș and Rodnei Mountains (Northern Romania). A Geographic Information System (GIS) methodology, based on three interpolation techniques—simple kriging, ordinary kriging, and cokriging—were utilized to derive continuous precipitation fields based on discrete rain gauge precipitation data and to derive interpolated radar data at rain gauge locations, and spatial analysis tools were developed to extract and analyze the optimal information content from both radar data and measurements. The dataset contains rainfall events that occurred in the period of 2015–2018, having 24 h temporal resolution. The model performance accuracy was carried out by using three validation metrics: mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE). The validation stage showed that our model, based on conditional merging technique, performed very well in 11 out of 15 rainfall events (approximate 78%), with an MAE under 0.4 mm and RMSE under 0.7 mm.

Theory of In-Cloud Activation of Aerosols and Microphysical Quasi-Equilibrium in a Deep Updraft

Abstract The microphysical quasi-equilibrium in an ascending adiabatic parcel is elucidated by an analytical theory in 0D with drastic simplifications. The theory predicts how in-cloud activation is most likely to be triggered by the onset of precipitation during sufficient ascent, with the ascent only needing to approach almost twice the cloud-base updraft speed aloft. The precipitation and cloud condensate mass fields are coupled in a closed system in a simplified version of the model. The initial state of no precipitation is unstable with respect to a perturbation. In the 2D phase space of both mass fields, there is a neutral line. Unstable growth of precipitation mass drives the system to cross the line into a regime of stability. An attractor is then approached consisting of precipitation mass balanced by accretion of cloud mass and fallout. The cloud-particle number concentration also approaches a stable equilibrium governed by the balance between in-cloud activation aloft from the inexorably increasing supersaturation during vertical acceleration and accretion of cloud droplets by precipitation. Dimensionless numbers characterizing the microphysical equilibria and their stability are derived mathematically, including a condensation–precipitation efficiency and an in-cloud activation efficiency. The theory explains common observations of the orders of magnitude of liquid water content in convective and stratiform clouds. Finally, sensitivity tests of the numerically integrated theoretical equations are documented with respect to variations in cloud condensation nucleus (CCN) aerosols and updraft speed. It is shown that this theory of in-cloud activation, with an increasing supersaturation during ascent, applies to both ice-only and liquid-only cloud. Significance Statement The purpose of the study is to create a theory for how equilibria among cloud-microphysical properties are attained during ascent. Continual initiation of fresh cloud droplets far above cloud base in the free troposphere is explained. The theory proposes a balance between creation and depletion of such droplets, in relation to a balance between creation and fallout of precipitation. The transition from positive feedbacks of instability toward negative feedbacks of stability when the equilibria are approached is elucidated. The theory explains why the mass content of cloud liquid in regions of ascent is typically about an order of magnitude lower in layer clouds with weak ascent than in convective clouds with faster ascent. Any future work could extend the theory to treat dilute parcels or coexistence of supercooled liquid and ice.

Effects of Saharan Dust Aerosols and West African Precipitation on the Energetics of African Easterly Waves

Abstract The effects of Saharan dust aerosols and West African precipitation on the seasonally averaged energetics of African easterly waves (AEWs) are examined using the Weather Research and Forecasting Model coupled to an interactive dust model. Four experiments are conducted: a control for the period July–September 2008, and three other experiments in which the dust emissions and precipitation are reduced separately and in combination. An analysis of the total energy shows the relative importance of the dust and precipitation to the seasonally averaged AEW strength and AEW tracks, which straddle the African easterly jet (AEJ). Changes in the dust amount have a larger effect on the strength of the AEWs than changes in the precipitation amount. The north AEW track is more strongly affected by changes in dust, while the south AEW track is more strongly affected by changes in precipitation. An analysis of the energy conversions aids in identifying the relative importance of the wave–mean flow interaction pathways that connect the dust and precipitation fields to the AEJ–AEW system. The analysis shows that the variability of the AEWs is primarily coupled to the dust- and precipitation-modified variability of the AEJ through wave–mean flow interaction. These results are discussed in light of tropical cyclone development over the eastern Atlantic Ocean.

Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation

Ice sheet models are used to study the deglaciation of North America at the end of the last ice age (past 21,000 years), so that we might understand whether and how existing ice sheets may reduce or disappear under climate change. Though ice sheet models have a few parameters controlling physical behaviour of the ice mass, they also require boundary conditions for climate (spatio-temporal fields of temperature and precipitation, typically on regular grids and at monthly intervals). The behaviour of the ice sheet is highly sensitive to these fields, and there is relatively little data from geological records to constrain them as the land was covered with ice. We develop a methodology for generating a range of plausible boundary conditions, using a low-dimensional basis representation of the spatio-temporal input. We derive this basis by combining key patterns, extracted from a small ensemble of climate model simulations of the deglaciation, with sparse spatio-temporal observations. By jointly varying the ice sheet parameters and basis vector coefficients, we run ensembles of the Glimmer ice sheet model that simultaneously explore both climate and ice sheet model uncertainties. We use these to calibrate the ice sheet physics and boundary conditions for Glimmer, by ruling out regions of the joint coefficient and parameter space via history matching. We use binary ice/no ice observations from reconstructions of past ice sheet margin position to constrain this space by introducing a novel metric for history matching to binary data.

Acquisition of rainfall in ungauged basins: a study of rainfall distribution heterogeneity based on a new method

High-density rainfall data is always desired to capture the heterogeneity of precipitation to accurately describe the components of the hydrological cycle. However, equipping and maintaining a high-density rain gauge network involve high costs, and the existing rain gauges are often unable to meet the density requirements. The objective of this study is to provide a new method to analyze the spatiotemporal variability of the precipitation field and to solve the problem of insufficient site density. To this end, the proper orthogonal decomposition (POD) method is proposed, which can analyze the spatial distribution characteristics of rainfall fields to solve data shortages. To demonstrate the feasibility and advantages of the proposed methodology, four districts and counties (Hongshan District, Jianli County, Sui County, and Xuanen County) in Hubei province in China were selected as case studies. The principal results are as follows. (1) The proposed method is effective in analyzing the spatiotemporal variability of the rainfall field to reconstruct rainfall processes in ungauged basins. (2) Compared with the commonly used Thiessen polygon method, the inverse distance weighting method, and the Kriging method, POD is more accurate and convenient, and the root mean squared error is reduced from 3.22, 1.83, 2.19 to 2.09; the correlation coefficients are improved from 0.60, 0.85, 0.79 to 0.89, respectively. (3) The POD method performs particularly well in simulating the peak value and the peak time and can offer a meaningful reference for analyzing the spatial distribution of rainfall.

Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada

The interpretation of deep learning (DL) hydrological models is a key challenge in data-driven modeling of streamflow, as the DL models are often seen as “black box” models despite often outperforming process-based models in streamflow prediction. Here we explore the interpretability of a convolutional long short-term memory network (CNN-LSTM) previously trained to successfully predict streamflow at 226 stream gauge stations across southwestern Canada. To this end, we develop a set of sensitivity experiments to characterize how the CNN-LSTM model learns to map spatiotemporal fields of temperature and precipitation to streamflow across three streamflow regimes (glacial, nival, and pluvial) in the region, and we uncover key spatiotemporal patterns of model learning. The results reveal that the model has learned basic physically-consistent principles behind runoff generation for each streamflow regime, without being given any information other than temperature, precipitation, and streamflow data. In particular, during periods of dynamic streamflow, the model is more sensitive to perturbations within/nearby the basin where streamflow is being modeled, than to perturbations far away from the basins. The sensitivity of modeled streamflow to the magnitude and timing of the perturbations, as well as the sensitivity of day-to-day increases in streamflow to daily weather anomalies, are found to be specific for each streamflow regime. For example, during summer months in the glacial regime, modeled daily streamflow is increasingly generated by warm daily temperature anomalies in basins with a larger fraction of glacier coverage. This model's learning of “glacier runoff” contributions to streamflow, without any explicit information given about glacier coverage, is enabled by a set of cell states that learned to strongly map temperature to streamflow only in glacierized basins in summer. Our results demonstrate that the model's decision making, when mapping temperature and precipitation to streamflow, is consistent with a basic physical understanding of the system.

A NASA–Air Force Precipitation Analysis for Near-Real-Time Operations

Abstract This article describes a new precipitation analysis algorithm developed by NASA for time-sensitive operations at the United States Air Force. Implemented as part of the Land Information System—a land modeling and data assimilation software framework—this NASA–Air Force Precipitation Analysis (NAFPA) combines numerical weather prediction model outputs with rain gauge measurements and satellite estimates to produce global, gridded 3-h accumulated precipitation fields at approximately 10-km resolution. Input observations are subjected to quality control checks before being used by the Bratseth analysis algorithm that converges to optimal interpolation. NAFPA assimilates up to 3.5 million observations without artificial data thinning or selection. To evaluate this new approach, a multiyear reanalysis is generated and intercompared with eight alternative precipitation products across the contiguous United States, Africa, and the monsoon region of eastern Asia. NAFPA yields superior accuracy and correlation over low-latency (up to 14 h) alternatives (numerical weather prediction and satellite retrievals), and often outperforms high-latency (up to 3.5 months) products, although the details for the latter vary by region and product. The development of NAFPA offers a high-quality, near-real-time product for use in meteorological, land surface, and hydrological research and applications. Significance Statement Precipitation is a key input to land modeling systems due to effects on soil moisture and other parts of the hydrologic cycle. It is also of interest to government decision-makers due to impacts on human activities. Here we present a new precipitation analysis based on available near-real-time data. By running the program for prior years and comparing with alternative products, we demonstrate that our analysis provides better accuracy and usually less bias than near-real-time satellite data alone, and better accuracy and correlation than data provided by numerical weather models. Our analysis is also competitive with other products created months after the fact, justifying confidence in using our analysis in near-real-time operations.

Verification of tropical cyclones (TC) wind structure forecasts from global NWP models and ensemble prediction systems (EPSs)

Forecasting wind structure of tropical cyclone (TC) is vital in assessment of impact due to high winds using Numerical Weather Prediction (NWP) model. The usual verification technique on TC wind structure forecasts are based on grid-to-grid comparisons between forecast field and the actual field. However, precision of traditional verification measures is easily affected by small scale errors and thus cannot well discriminate the accuracy or effectiveness of NWP model forecast. In this study, the Method for Object-Based Diagnostic Evaluation (MODE), which has been widely adopted in verifying precipitation fields, is utilized in TC's wind field verification for the first time. The TC wind field forecast of deterministic NWP model and Ensemble Prediction System (EPS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) over the western North Pacific and the South China Sea in 2020 were evaluated. A MODE score of 0.5 is used as a threshold value to represent a skillful (or good) forecast. It is found that the R34 (radius of 34 knots) wind field structure forecasts within 72 h are good regardless of DET or EPS. The performance of R50 and R64 is slightly worse but the R50 forecasts within 48 h remain good, with MODE exceeded 0.5. The R64 forecast within 48 h are worth for reference as well with MODE of around 0.5. This study states that the TC wind field structure forecast by ECMWF is skillful for TCs over the western North Pacific and the South China Sea.

Study of the convergence of low cloudiness by the effect of ocean breeze using the regional climate model

This work sought to analyze in high resolution the downscaling of the global model Climate Forecast System Version 2 (CFSv2) by using the regional climate model RegCM-4.9. The precipitation fields were generated in two different domains and spatial resolution with 10 km and 3 km, for August to October 2020. The equatorial region of Northeast Brazil can be characterized by the complex dynamics that encompass atmospheric movements such as the existence of convergence at low levels of convective cloudiness and its diurnal effect on low cloud cover and its influence on evident precipitation. However, for meteorological scales with a daytime circle, the land-ocean breeze effect in the Gulf of Maranhão in this theoretical model of night convergence shows a peak between 6 to 9 am local time. This work seeks to recognize the existence of land-ocean breeze in Alcântara and its influence on the daytime precipitation cycle, its dynamics can influence aerospace activities by increasing convergence and, depending on its intensity, generating more intense rainfall in the coastal region even in the dry period, which corresponds to the most suitable period for the operational activities of the Alcântara Launch Center.

Value addition in coupled model intercomparison project phase 6 over phase 5: global perspectives of precipitation, temperature and soil moisture fields

Value addition in coupled model intercomparison project phase 6 over phase 5: global perspectives of precipitation, temperature and soil moisture fields

Cold Pools Observed during EUREC4A: Detection and Characterization from Atmospheric Soundings

AbstractA new method is developed to detect cold pools from atmospheric soundings over tropical oceans and applied to sounding data from the Elucidating the Role of Cloud–Circulation Coupling in Climate (EUREC4A) field campaign, which took place south and east of Barbados in January–February 2020. The proposed method uses soundings to discriminate cold pools from their surroundings: cold pools are defined as regions where the mixed-layer height is smaller than 400 m. The method is first tested against 2D surface temperature and precipitation fields in a realistic high-resolution simulation over the western tropical Atlantic Ocean. Then, the method is applied to a dataset of 1068 atmospheric profiles from dropsondes (launched from two aircraft) and 1105 from radiosondes (launched from an array of four ships and the Barbados Cloud Observatory). We show that 7% of the EUREC4A soundings fell into cold pools. Cold-pool soundings coincide with (i) mesoscale cloud arcs and (ii) temperature drops of ∼1 K relative to the environment, along with moisture increases of ∼1 g kg−1. Furthermore, cold-pool moisture profiles exhibit a “moist layer” close to the surface, topped by a “dry layer” until the cloud base level, and followed by another moist layer in the cloud layer. In the presence of wind shear, the spreading of cold pools is favored downshear, suggesting downward momentum transport by unsaturated downdrafts. The results support the robustness of our detection method in diverse environmental conditions and its simplicity makes the method a promising tool for the characterization of cold pools, including their vertical structure. The applicability of the method to other regions and convective regimes is discussed.

The relationship between precipitation and its spatial pattern in the trades observed during EUREC4A

AbstractTrade wind convection organises into a rich spectrum of spatial patterns, often in conjunction with precipitation development. Which role spatial organisation plays for precipitation and vice versa is not well understood. We analyse scenes of trade‐wind convection scanned by the C‐band radar Poldirad during the EURECA field campaign to investigate how trade‐wind precipitation fields are spatially organised, quantified by the cells' number, mean size, and spatial arrangement, and how this matters for precipitation characteristics. We find that the mean rain rate (i.e., the amount of precipitation in a scene) and the intensity of precipitation (mean conditional rain rate) relate differently to the spatial pattern of precipitation. Whereas the amount of precipitation increases with mean cell size or number, as it scales well with the precipitation fraction, the intensity increases predominantly with mean cell size. In dry scenes, the increase of precipitation intensity with mean cell size is stronger than in moist scenes. Dry scenes usually contain fewer cells with a higher degree of clustering than moist scenes do. High precipitation intensities hence typically occur in dry scenes with rather large, few, and strongly clustered cells, whereas high precipitation amounts typically occur in moist scenes with rather large, numerous, and weakly clustered cells. As cell size influences both the intensity and amount of precipitation, its importance is highlighted. Our analyses suggest that the cells' spatial arrangement, correlating mainly weakly with precipitation characteristics, is of second‐order importance for precipitation across all regimes, but it could be important for high precipitation intensities and to maintain precipitation amounts in dry environments.

HIRHAM5 Kullanılarak Akdeniz Havzası’nın Gelecek Klimatolojisi ve Ekstrem Olaylarındaki Değişikliklerin Projeksiyonları

In this work, change of temperature and precipitation climatology over Mediterranean Basin including Turkey were investigated using HIRHAM5 driven by global climate models such as EC-EARTH, HadGEM2-ES and NorESM1-M for 2011-2100 compared to 1971-2000. Daily mean temperature and precipitation fields are used to compute extreme indices. According to the results, severity of temperature- and precipitation-based indices will be expected to increase throughout the century with increasing radiative forcing. Minimum of minimum temperatures will increase more pronounced over northern Mediterranean, which is referred to climate change hot spots, whereas increase in maximum of maximum temperatures are moderate over land areas. Decrease in total wet-day precipitation is expected while number dry days is expected to increase. Therefore, Mediterranean Basin and Turkey will have warmer and drier conditions compared to present climate conditions.

Stacking machine learning models versus a locally weighted linear model to generate high-resolution monthly precipitation over a topographically complex area

This study applied a stacked generalization ensemble approach to generate high-resolution precipitation estimates and compared its performance with an optimized local weighted linear regression (LWLR) algorithm, a well-known local precipitation-elevation interpolation method incorporating physiographical factors. The stacked generalization ensemble consists of multilayer perceptron neural network (MLP), support vector machine (SVM), and random forest (RF) combined through a meta-learning algorithm with/without rescanning input covariates. Sixteen input covariates, including 2 topographic features, 5 cloud properties, 5 environmental variables, 3 precipitation products (PPs), and inverse distance weighted (IDW) estimates as the precipitation background field were fed into the machine learning models. Hold out approach was adopted for performance evaluation in which 50% of the 174 gauges were used for training, and the rest was used for validation. The results indicated that the overall monthly MAE, RMSE, and rBias of the proposed stacking model for the validation dataset were 3.3%, 6.8%, and 50%, respectively, less than that of the RF model, which is the best individual model. Also, the stacking model outperformed LWLR by decreasing monthly MAE, RMSE, and rBias 10.7%, 19.1%, and 28.6%, respectively. Further analysis implied that (1) the stacked model is more robust than LWLR and less dependent on the density of gauges, thus suitable for areas with scarce gauge coverage; (2) comparing the spatial distribution of mean monthly precipitation maps, generated by stacking and LWLR models, stacking algorithm can successfully screen out the bulls' eyes of background IDW precipitation field, and both patterns are consistent with the topography of the area; and (3) the stacking scheme is found to have a better extrapolation ability than LWLR over high elevations.

Development of cluster analysis methodology for identification of model rainfall hyetographs and its application at an urban precipitation field scale

Despite growing access to precipitation time series records at a high temporal scale, in hydrology, and particularly urban hydrology, engineers still design and model drainage systems using scenarios of rainfall temporal distributions predefined by means of model hyetographs. This creates the need for the availability of credible statistical methods for the development and verification of already locally applied model hyetographs. The methodology development for identification of similar rainfall models is also important from the point of view of systems controlling stormwater runoff structure in real time, particularly those based on artificial intelligence. This paper presents a complete methodology of division of storm rainfalls sets into rainfalls clusters with similar temporal distributions, allowing for the final identification of local model hyetographs clusters. The methodology is based on cluster analysis, including the hierarchical agglomeration method and k-means clustering. The innovativeness of the postulated methodology involves: the objectivization of clusters determination number based on the analysis of total within sum of squares (wss) and the Caliński and Harabasz Index (CHIndex), verification of the internal coherence and external isolation of clusters based on the bootmean parameter, and the designated clusters profiling. The methodology is demonstrated at a scale of a large urban precipitation field of Kraków city on a total set of 1806 storm rainfalls from 25 rain gauges. The obtained results confirm the usefulness and repeatability of the developed methodology regarding storm rainfall clusters division, and identification of model hyetographs in particular clusters, at a scale of an entire city. The applied methodology can be successfully transferred on a global scale and applied in large urban agglomerations around the world.

Spatial Downscaling Model Combined with the Geographically Weighted Regression and Multifractal Models for Monthly GPM/IMERG Precipitation in Hubei Province, China

High spatial resolution (1 km or finer) precipitation data fields are crucial for understanding the Earth’s water and energy cycles at the regional scale for applications. The spatial resolution of the Global Precipitation Measurement (GPM) mission (IMERG) satellite precipitation products is 0.1° (latitude) × 0.1° (longitude), which is too coarse for regional-scale analysis. This study combined the Geographically Weighted Regression (GWR) and the Multifractal Random Cascade (MFRC) model to downscale monthly GPM/IMERG precipitation products from 0.1° × 0.1° (approximately 11 km × 11 km) to 1 km in Hubei Province, China. This work’s results indicate the following: (1) The original GPM product can accurately express the precipitation in the study area, which highly correlates with the site data from 2015 to 2017 (R2 = 0.79) and overall presents the phenomenon of overestimation. (2) The GWR model maintains the precipitation field’s overall accuracy and smoothness, with even improvements in accuracy for specific months. In contrast, the MFRC model causes a slight decrease in the overall accuracy of the precipitation field but performs better in reducing the bias. (3) The GWR-MF combined with the GWR and MFRC model improves the observation accuracy of the downscaling results and reduces the bias value by introducing the MFRC to correct the deviation of GWR. The conclusion and analysis of this paper can provide a meaningful experience for 1 km high-resolution data to support related applications.

Impacts of the ocean-atmosphere coupling into the very short range prediction system, during the impact of Hurricane Matthew on Cuba

The main goal of this investigation is analyzing the impact of insert the ocean-atmosphere coupling into the very short range prediction system of Cuba. The ocean-atmosphere coupled components of the Coupled Ocean-Atmosphere-Wave-Sediment Transport Modeling System are used for this purpose and the hurricane Matthew is selected as study case. Two experiments are performed: first, using a dynamic sea surface temperature, updated daily in the atmospheric model WRF; and second using a dynamic coupling between the atmospheric and an oceanic models. For the simulated track, the best results are obtained with the coupled system. The impact of coupling on the maximum wind velocities and minimum central pressure is studied. In the coupled system the sea surface temperature has more influence in the surface latent heat fluxes. Also, with this methodology the dry footprint and the behavior of the precipitation field in the presence of a hurricane are studied. This analysis shows that the hurricane acts like an open and self-sustained system in the numerical experiments. The highest differences in the precipitation simulations are in the significant convective area inside the hurricane.

Is Precipitation Responsible for the Most Hydrological Model Uncertainty?

Rainfall-runoff modeling is highly uncertain for a number of different reasons. Hydrological processes are quite complex, and their simplifications in the models lead to inaccuracies. Model parameters themselves are uncertain—physical parameters because of their observations and conceptual parameters due to their limited identifiability. Furthermore, the main model input—precipitation is uncertain due to the limited number of available observations and the high spatio-temporal variability. The quantification of model output uncertainty is essential for their use. Most approaches used for the quantification of uncertainty in rainfall-runoff modeling assign the uncertainty to the model parameters. In this contribution, the role of precipitation uncertainty is investigated. Instead of a standard sensitivity analysis of the model output with respect to the input variations, it is investigated to what extent realistic precipitation fields could improve model performance. Realistic precipitation fields are defined as gridded realizations of precipitation which reproduce the observed values at the observation locations, with values which reproduce the distribution of the observed values and with spatial variability the same as the spatial variability of the observations. The above conditions apply to each observation time step. Through an inverse modeling approach based on Random Mixing precipitation fields fulfilling the above conditions and reproducing the discharge output better than using traditional interpolated observations can be obtained. These realizations show how much rainfall runoff models may profit from better precipitation input and how much remains for the parameter and model concept uncertainty. The methodology is applied using two hydrological models with a contrasting basis, SHETRAN and HBV, for three different mesoscale sub-catchments of the Neckar basin in Germany. Results show that up to 50% of the model error can be attributed to precipitation uncertainty. Further, inverting precipitation using hydrological models can improve model performance even in neighboring catchments which are not considered explicitly.