Browse

You are looking at 1 - 10 of 2,046 items for :

  • Journal of Hydrometeorology x
  • Refine by Access: Content accessible to me x
Clear All
Shiori Sugimoto, Kenichi Ueno, Hatsuki Fujinami, Tomoe Nasuno, Tomonori Sato, and Hiroshi G. Takahashi

Abstract

A numerical experiment with a 2-km resolution was conducted using the Weather Research and Forecasting (WRF) Model to investigate physical processes driving nocturnal precipitation over the Himalayas during the mature monsoon seasons between 2003 and 2010. The WRF Model simulations of increases in precipitation twice a day, one in the afternoon and another around midnight, over the Himalayan slopes, and of the single nocturnal peak over the Himalayan foothills were reasonably accurate. To understand the synoptic-scale moisture transport and its local-scale convergence generating the nocturnal precipitation, composite analyses were conducted using the reanalysis dataset and model outputs. In the synoptic scale, moisture transport associated with the westward propagation of low pressure systems was found when nocturnal precipitation dominated over the Himalayan slopes. In contrast, moisture was directly provided from the synoptic-scale monsoon westerlies for nocturnal precipitation over the foothills. The model outputs suggested that precipitation occurred on the mountain ridges in the Himalayas during the afternoon and expanded horizontally toward lower-elevation areas through the night. During the nighttime, the downslope wind was caused by radiative cooling at the surface and was intensified by evaporative cooling by hydrometeors in the near-surface layer. As a result, convergence between the downslope wind and the synoptic-scale flow promoted nocturnal precipitation over the Himalayas and to the south, as well as the moisture convergence by orography and/or synoptic-scale circulation patterns. The nocturnal precipitation over the Himalayas was not simulated well when we used the coarse topographic resolution and the smaller number of vertical layers.

Open access
Daniel Regenass, Linda Schlemmer, Oliver Fuhrer, Jean-Marie Bettems, Marco Arpagaus, and Christoph Schär

Abstract

An adequate representation of the interaction between the land surface and the atmosphere is critical for both numerical weather prediction and climate models. The surface energy and mass balances are tightly coupled to the terrestrial water cycle, mainly through the state of soil moisture. An inadequate representation of the terrestrial water cycle will deteriorate the state of the land surface model and introduce biases to the atmospheric model. The validation of land surface models is challenging, as there are very few observations and the soil is highly heterogeneous. In this paper, a validation framework for land surface schemes based on catchment mass balances is presented. The main focus of our development lies in the application to kilometer-resolution numerical weather prediction and climate models, although the approach is scalable in both space and time. The methodology combines information from multiple observation-based datasets. Observational uncertainties are estimated by using independent sets of observations. It is shown that the combination of observation-based datasets and river discharge measurements close the water balance fairly well for the chosen catchments. As a showcase application, the framework is then applied to compare and validate four different versions of TERRA ML, the land surface scheme of the COSMO numerical weather prediction and climate model over five mesoscale catchments in Switzerland ranging from 105 to 1713 km2. Despite large observational uncertainties, validation results clearly suggest that errors in terrestrial storage changes are closely linked to errors in runoff generation and emphasize the crucial role of infiltration processes.

Open access
Xiaoyang Li, Ryuichi Kawamura, Atsuko Sugimoto, and Kei Yoshimura

Abstract

Moisture sources and their corresponding temperature and humidity are important for explosive extratropical cyclones’ development regarding latent heating. To clarify the water origins and moisture transport processes within an explosive cyclone, we simulated an explosive cyclone migrating poleward across the Sea of Japan on 30 November 2014, by using an isotopic regional spectral model. In the cyclone’s center area, a replacement of water origins occurred during the cyclone’s development. During the early stage, the warm conveyor belt (WCB) transported large amounts of moisture from the East China Sea and Kuroshio into the cyclone’s inner region. While in the deepening stage, the cold conveyor belt (CCB) and dry intrusion (DI) conveyed more moisture from the northwest Pacific Ocean and the Sea of Japan, respectively. Compared with the contribution of local moisture, that of remote moisture was dominant in the cyclone’s center area. Regarding the water origins of condensation within the frontal system in the deepening stage, the northwest Pacific Ocean vapor, principally transported by the CCB, contributed 35.5% of the condensation in the western warm front. The East China Sea and Kuroshio moisture, conveyed by the WCB, accounted for 32.4% of the condensation in the cold and eastern warm fronts. In addition, condensation from the Sea of Japan, which was mainly triggered by the DI and induced by the topography, occurred on the west coast of the mainland of Japan and near the cyclone center. The spatial distribution of the isotopic composition in condensation and water vapor also supports the water-origin results.

Open access
Clement Guilloteau, Efi Foufoula-Georgiou, Pierre Kirstetter, Jackson Tan, and George J. Huffman

Abstract

As more global satellite-derived precipitation products become available, it is imperative to evaluate them more carefully for providing guidance as to how well precipitation space–time features are captured for use in hydrologic modeling, climate studies, and other applications. Here we propose a space–time Fourier spectral analysis and define a suite of metrics that evaluate the spatial organization of storm systems, the propagation speed and direction of precipitation features, and the space–time scales at which a satellite product reproduces the variability of a reference “ground-truth” product (“effective resolution”). We demonstrate how the methodology relates to our physical intuition using the case study of a storm system with rich space–time structure. We then evaluate five high-resolution multisatellite products (CMORPH, GSMaP, IMERG-Early, IMERG-Final, and PERSIANN-CCS) over a period of 2 years over the southeastern United States. All five satellite products show generally consistent space–time power spectral density when compared to a reference ground gauge–radar dataset (GV-MRMS), revealing agreement in terms of average morphology and dynamics of precipitation systems. However, a deficit of spectral power at wavelengths shorter than 200 km and periods shorter than 4 h reveals that all satellite products are excessively “smooth.” The products also show low levels of spectral coherence with the gauge–radar reference at these fine scales, revealing discrepancies in capturing the location and timing of precipitation features. From the space–time spectral coherence, the IMERG-Final product shows superior ability in resolving the space–time dynamics of precipitation down to 200-km and 4-h scales compared to the other products.

Open access
Matthew Sturm and Glen E. Liston

Abstract

Twenty-five years ago, we published a global seasonal snow classification now widely used in snow research, physical geography, and as a mission planning tool for remote sensing snow studies. Performing the classification requires global datasets of air temperature, precipitation, and land cover. When introduced in 1995, the finest-resolution global datasets of these variables were on a 0.5° × 0.5° latitude–longitude grid (approximately 50 km). Here we revisit the snow classification system and, using new datasets and methods, present a revised classification on a 10-arc-s × 10-arc-s latitude–longitude grid (approximately 300 m). We downscaled 0.1° × 0.1° latitude–longitude (approximately 10 km) gridded meteorological climatologies [1981–2019, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis, 5th Generation Land (ERA5-Land)] using MicroMet, a spatially distributed, high-resolution, micrometeorological model. The resulting air temperature and precipitation datasets were combined with European Space Agency (ESA) Climate Change Initiative (CCI) GlobCover land-cover data (as a surrogate for wind speed) to produce the updated classification, which we have applied to all of Earth’s terrestrial areas. We describe this new, high-resolution snow classification dataset, highlight the improvements added to the classification system since its inception, and discuss the utility of the climatological snow classes at this much higher resolution. The snow class dataset (Global Seasonal-Snow Classification, Version 1) and the tools used to develop the data are publicly available online at the National Snow and Ice Data Center (NSIDC).

Open access
Ju-Yu Chen, Silke Trömel, Alexander Ryzhkov, and Clemens Simmer

Abstract

Recent advances demonstrate the benefits of radar-derived specific attenuation at horizontal polarization (A H) for quantitative precipitation estimation (QPE) at S and X band. To date the methodology has, however, not been adapted for the widespread European C-band radars such as those installed in the network of the German Meteorological Service (DWD, Deutscher Wetterdienst). Simulations based on a large dataset of drop size distributions (DSDs) measured over Germany are performed to investigate the DSD dependencies of the attenuation parameter α H for the A H estimates. The normalized raindrop concentration (Nw) and the change of differential reflectivity (Z DR) with reflectivity at horizontal polarization (Z H) are used to categorize radar observations into regimes for which scan-wise optimized α H values are derived. For heavier continental rain with Z H > 40 dBZ, the A H-based rainfall retrieval R(A H) is combined with a rainfall estimator using a substitute of specific differential phase (KDP*). We also assess the performance of retrievals based on specific attenuation at vertical polarization (A V). Finally, the regime-adapted hybrid QPE algorithms are applied to four convective cases and one stratiform case from 2017 to 2019, and compared to DWD’s operational Radar-Online-Aneichung (RADOLAN) RW rainfall product, which is based on Z h only but adjusted to rain gauge measurements. For the convective cases, our hybrid retrievals outperform the traditional R(Z h) and pure R(A H/V) retrievals with fixed α H/V values when evaluated with gauge measurements and outperform RW when evaluated by disdrometer measurements. Potential improvements using ray-wise α H/V and segment-wise applications of the ZPHI method along the radials are discussed.

Open access
Guofeng Zhu, Zhuanxia Zhang, Huiwen Guo, Yu Zhang, Leilei Yong, Qiaozhuo Wan, Zhigang Sun, and Huiying Ma

Abstract

As raindrops fall from the cloud base to the ground, evaporation below those clouds affects the rain’s isotope ratio, reduces precipitation in arid areas, and impacts the local climate. Therefore, in arid areas with scarce water resources and fragile ecological environments, the below-cloud evaporation is an issue of great concern. Based on 406 event-based precipitation samples collected from nine stations in the Shiyang River basin (SRB) in the northwest arid area, global meteorological water line (GMWL) and local meteorological water line (LMWL) are compared, and the Stewart model is used to study the effect of spatial and temporal variation of below-cloud evaporation on isotope values in different geomorphic units at the SRB. Furthermore, factors influencing below-cloud evaporation are analyzed. The results show that 1) the change of d-excess (Δd) in precipitation at the SRB and the residual ratio of raindrop evaporation (f) vary in time and space. With regard to temporal variation, the intensity of below-cloud evaporation is described by summer < autumn < winter < spring. Regarding spatial variation, the below-cloud evaporation in mountain areas is weaker than in oases and deserts. The intensity of below-cloud evaporation in mountain areas increases with decreasing altitude, and the below-cloud evaporation in oasis and desert areas is affected by local climatic conditions. 2) Below-cloud evaporation is also affected by local transpiration evaporation, especially around reservoirs. Reservoirs increase the relative humidity of the air nearby, weakening below-cloud evaporation. This study deepens our understanding of the water cycle process in arid areas.

Open access
Jon Olav Skøien, Konrad Bogner, Peter Salamon, and Fredrik Wetterhall

Abstract

Different postprocessing techniques are frequently employed to improve the outcome of ensemble forecasting models. The main reason is to compensate for biases caused by errors in model structure or initial conditions, and as a correction for under- or overdispersed ensembles. Here we use the ensemble model output statistics method to postprocess the ensemble output from a continental-scale hydrological model, LISFLOOD, as used in the European Flood Awareness System (EFAS). We develop a method for local calibration and interpolation of the postprocessing parameters and compare it with a more traditional global calibration approach for 678 stations in Europe based on long-term observations of runoff and meteorological variables. For the global calibration we also test a reduced model with only a variance inflation factor. Whereas the postprocessing improved the results for the first 1–2 days lead time, the improvement was less for increasing lead times of the verification period. This was the case both for the local and global calibration methods. As the postprocessing is based on assumptions about the distribution of forecast errors, we also present an analysis of the ensemble output that provides some indications of what to expect from the postprocessing.

Open access
N. Zíková, P. Pokorná, O. Makeš, J. Rotrekl, P. Sedlák, P. Pešice, and V. Ždímal

Abstract

In situ campaigns focused on aerosol–cloud interactions were performed to describe the size-dependent activation of aerosols of various origins during variable meteorological conditions. Low cloud episodes, coded as fog, freezing fog, or rain with fog, were compared with nonphenomenon episodes. From the difference in aerosols measured behind the whole air inlet and PM2.5 inlet, the activated fraction (AF; a share of activated particles from all those available) was calculated. For fog, the AF was stable, resulting in a small variability in the activated size. During freezing fog, a higher variability in supersaturation was deduced from larger variability in the AF and smaller effective radii of cloud droplets. The AF during rain with fog showed a connection to the air mass origin, less effective activation, and smaller cloud droplets. The analysis of the relationship between meteorological conditions and activations suggested that the different hydrometeors were connected with different air masses. No effect of photochemistry was found; in contrast, some dependence on relative humidity, temperature, wind speed, and liquid water content (LWC) was described. With increasing humidity, smaller particles were able to activate. For lower RH, the importance of supersaturation fluctuations increased, moving to a fluctuation-influenced regime. The strongest connection was found between activation and LWC; for the LWC below 0.10 g m−3, a strong decrease in activated particle size was found with increasing LWC, due to turbulence, number of particles, and availability of condensable water. From 0.10 g m−3 LWC and higher, the LWC and the connected supersaturation could be the main factors influencing the activation.

Open access
Zeyu Xue and Paul Ullrich

Abstract

Climate models are frequently used tools for adaptation planning in light of future uncertainty. However, not all climate models are equally trustworthy, and so model biases must be assessed to select models suitable for producing credible projections. Drought is a well-known and high-impact form of extreme weather, and knowledge of its frequency, intensity, and duration are key for regional water management plans. Droughts are also difficult to assess in climate datasets, due to the long duration per event, relative to the length of a typical simulation. Therefore, there is a growing need for a standardized suite of metrics addressing how well models capture this phenomenon. In this study, we present a widely applicable set of metrics for evaluating agreement between climate datasets and observations in the context of drought. Two notable advances are made in our evaluation system: first, statistical hypothesis testing is employed for normalization of individual scores against the threshold for statistical significance. And second, within each evaluation region and dataset, principal feature analysis is used to select the most descriptive metrics among 11 metrics that capture essential features of drought. Our metrics package is applied to three characteristically distinct regions in the conterminous United States and across several commonly employed climate datasets (CMIP5/6, LOCA, and CORDEX). As a result, insights emerge into the underlying drivers of model bias in global climate models, regional climate models, and statistically downscaled models.

Open access