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Nobuyuki Utsumi, Hyungjun Kim, F. Joseph Turk, and Ziad. S. Haddad

Abstract

Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.

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Kylie J. Park, Kei Yoshimura, Hyungjun Kim, and Taikan Oki

Abstract

Over 150 years of investigations into global terrestrial precipitation are revisited to reveal how researchers estimated annual means from in situ observations before the age of digitization. After introducing early regional efforts to measure precipitation, the pioneering estimates of terrestrial mean precipitation from the late nineteenth and early twentieth centuries are compared to successive estimates, including those using the latest gridded precipitation datasets available. The investigation reveals that the range of the early estimates is comparable to the interannual variation in terrestrial mean precipitation derived from the latest Climatic Research Unit (CRU) dataset. In-depth revisions of the estimates were infrequent up to the 1970s, due in part to difficulty obtaining and maintaining up-to-date datasets with global coverage. This point is illustrated in a “family tree” that identifies the key publications that subsequent authors referenced, sometimes decades after the original publication. Significant efforts to collate global observations facilitated new investigations and improved data exchange, for example, in the International Hydrological Decade (1965–74) and following the establishment of the Global Telecommunication System under the World Weather Watch Programme of the World Meteorological Organization. Also in the 1970s were the first attempts to adjust in situ observations on a global scale to account for gauge undercatch, and this had a noticeable impact on mean annual estimates. There remains no single satisfactory approach to gauge bias adjustment. Echoing the repeated message of past researchers, today’s authors cite poor spatial coverage, temporal inhomogeneity, and inadequate sharing of in situ observations as the key obstacles to obtaining more accurate estimates of terrestrial mean precipitation.

Open access
Kinya Toride, Panduka Neluwala, Hyungjun Kim, and Kei Yoshimura

Abstract

There is a large amount of documented weather information all over the world, including Asia (e.g., old diaries, log books, etc.). The ultimate goal of this study is to reconstruct historical weather by deriving total cloud cover (TCC) from historically documented weather records and to assimilate them using a general circulation model and a data assimilation scheme. Two experiments are performed using the Global Spectral Model and an ensemble Kalman filter: 1) a reanalysis data experiment and 2) a ground observation data experiment, for 18 synthesized observation stations in Japan according to the Historical Weather Data Base. By assuming that weather records can be converted into three TCC categories, the synthetic observation data of daily TCC are created from reanalysis data, with a large observation error of 30%, and by classifying ground observation data into the three categories. Compared with the simulation without assimilation of any observation, the results of the reanalysis data experiment show improvements, not only in TCC but also in other meteorological variables (e.g., humidity, precipitation, precipitable water, wind, and pressure). For specific humidity at 2 m above the surface, the monthly averaged root-mean-square error is reduced by 18%–22% downstream of the assimilated region. The results of the ground observation data experiment are not as successful as a result of additional error sources, indicating the bias needs to be handled correctly. By showing improvements with the loosely classified cloud information, the feasibility of the developed model to be applied for historical weather reconstruction is confirmed.

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Nobuyuki Utsumi, F. Joseph Turk, Ziad S. Haddad, Pierre-Emmanuel Kirstetter, and Hyungjun Kim

Abstract

Precipitation estimation based on passive microwave (MW) observations from low-Earth-orbiting satellites is one of the essential variables for understanding the global climate. However, almost all validation studies for such precipitation estimation have focused only on the surface precipitation rate. This study investigates the vertical precipitation profiles estimated by two passive MW-based retrieval algorithms, i.e., the emissivity principal components (EPC) algorithm and the Goddard profiling algorithm (GPROF). The passive MW-based condensed water content profiles estimated from the Global Precipitation Measurement Microwave Imager (GMI) are validated using the GMI + Dual-Frequency Precipitation Radar combined algorithm as the reference product. It is shown that the EPC generally underestimates the magnitude of the condensed water content profiles, described by the mean condensed water content, by about 20%–50% in the middle-to-high latitudes, while GPROF overestimates it by about 20%–50% in the middle-to-high latitudes and more than 50% in the tropics. Part of the EPC magnitude biases is associated with the representation of the precipitation type (i.e., convective and stratiform) in the retrieval algorithm. This suggests that a separate technique for precipitation type identification would aid in mitigating these biases. In contrast to the magnitude of the profile, the profile shapes are relatively well represented by these two passive MW-based retrievals. The joint analysis between the estimation performances of the vertical profiles and surface precipitation rate shows that the physically reasonable connections between the surface precipitation rate and the associated vertical profiles are achieved to some extent by the passive MW-based algorithms.

Open access
Yadu Pokhrel, Naota Hanasaki, Sujan Koirala, Jaeil Cho, Pat J.-F. Yeh, Hyungjun Kim, Shinjiro Kanae, and Taikan Oki

Abstract

Anthropogenic activities have been significantly perturbing global freshwater flows and groundwater reserves. Despite numerous advances in the development of land surface models (LSMs) and global terrestrial hydrological models (GHMs), relatively few studies have attempted to simulate the impacts of anthropogenic activities on the terrestrial water cycle using the framework of LSMs. From the comparison of simulated terrestrial water storage with the Gravity Recovery and Climate Experiment (GRACE) satellite observations it is found that a process-based LSM, the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO), outperforms the bucket-model-based GHM called H08 in simulating hydrologic variables, particularly in water-limited regions. Therefore, the water regulation modules of H08 are incorporated into MATSIRO. Further, a new irrigation scheme based on the soil moisture deficit is developed. Incorporation of anthropogenic water regulation modules significantly improves river discharge simulation in the heavily regulated global river basins. Simulated irrigation water withdrawal for the year 2000 (2462 km3 yr−1) agrees well with the estimates provided by the Food and Agriculture Organization (FAO). Results indicate that irrigation changes surface energy balance, causing a maximum increase of ~50 W m−2 in latent heat flux averaged over June–August. Moreover, unsustainable anthropogenic water use in 2000 is estimated to be ~450 km3 yr−1, which corresponds well with documented records of groundwater overdraft, representing an encouraging improvement over the previous modeling studies. Globally, unsustainable water use accounts for ~40% of blue water used for irrigation. The representation of anthropogenic activities in MATSIRO makes the model a suitable tool for assessing potential anthropogenic impacts on global water resources and hydrology.

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Xiaogang He, Hyungjun Kim, Pierre-Emmanuel Kirstetter, Kei Yoshimura, Eun-Chul Chang, Craig R. Ferguson, Jessica M. Erlingis, Yang Hong, and Taikan Oki

Abstract

As a basic form of climate patterns, the diurnal cycle of precipitation (DCP) can provide a key test bed for model reliability and development. In this study, the DCP over West Africa was simulated by the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) during the monsoon season (April–September) of 2005. Three convective parameterization schemes (CPSs), single-layer simplified Arakawa–Schubert (SAS), multilayer relaxed Arakawa–Schubert (RAS), and new Kain–Fritsch (KF2), were evaluated at two horizontal resolutions (20 and 10 km). The Benin mesoscale site was singled out for additional investigation of resolution effects. Harmonic analysis was used to characterize the phase and amplitude of the DCP. Compared to satellite observations, the overall spatial distributions of amplitude were well captured at regional scales. The RSM properly reproduced the observed late afternoon peak over land and the early morning peak over ocean. Nevertheless, the peak time was early. Sensitivity experiments of CPSs showed similar spatial patterns of rainfall totals among the schemes; CPSs mainly affected the amplitude of the diurnal cycle, while the phase was not significantly shifted. There is no clear optimal pairing of resolution and CPS. However, it is found that the sensitivity of DCP to CPSs and resolution varies with the partitioning between convective and stratiform, which implies that appropriate partitioning needs to be considered for future development of CPSs in global or regional climate models.

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Mehnaz Rashid, Rong-You Chien, Agnès Ducharne, Hyungjun Kim, Pat J.-F. Yeh, Christophe Peugeot, Aaron Boone, Xiaogang He, Luc Séguis, Yutaro Yabu, Moussa Boukari, and Min-Hui Lo

Abstract

A comprehensive estimation of water budget components, particularly groundwater storage (GWS) and fluxes, is crucial. In this study, we evaluate the terrestrial water budget of the Donga basin (Benin, West Africa), as simulated by three land surface models (LSMs) used in the African Monsoon Multidisciplinary Analysis Land Surface Model Intercomparison Project, phase 2 (ALMIP2): CLM4, Catchment LSM (CLSM), and Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO). All three models include an unconfined groundwater component and are driven by the same ALMIP2 atmospheric forcing from 2005 to 2008. Results show that all three models simulate substantially shallower water table depth (WTD) with smaller seasonal variations, approximately 1–1.5 m compared to the observed values that range between 4 and 9.6 m, while the seasonal variations of GWS are overestimated by all the models. These seemingly contradictory simulation results can be explained by the overly high specific yield prescribed in all models. All models achieve similar GWS simulations but with different fractions of precipitation partitioning into surface runoff, base flow, and evapotranspiration (ET), suggesting high uncertainty and errors in the terrestrial and groundwater budgets among models. The poor performances of models can be attributed to bias in the hydrological partitioning (base flow vs surface runoff) and sparse subsurface data. This analysis confirms the importance of subsurface hydrological processes in the current generation of LSMs and calls for substantial improvement in both surface water budget (which controls groundwater recharge) and the groundwater system (hydrodynamic parameters, vertical geometry).

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Cecile B. Menard, Richard Essery, Gerhard Krinner, Gabriele Arduini, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Emanuel Dutra, Xing Fang, Charles Fierz, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Hyungjun Kim, Matthieu Lafaysse, Thomas Marke, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Gerd Schädler, Vladimir A. Semenov, Tatiana Smirnova, Ulrich Strasser, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan

Abstract

Twenty-seven models participated in the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modeling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modeling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parameterizations are problematic, and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behavior and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the community.

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