Dense Observation Network Research Articles

Validating global horizontal irradiance retrievals from Meteosat SEVIRI at increased spatial resolution against a dense network of ground-based observations

Validating global horizontal irradiance retrievals from Meteosat SEVIRI at increased spatial resolution against a dense network of ground-based observations

Evaluation of Wintertime Precipitation Estimates and Forecasts in the Mountains of Colorado

Abstract Wintertime precipitation poses many observational and forecasting challenges, especially in the complex topography of the western United States where radar beam blockage and difficulty siting in situ observations yields more sparse observations than in the eastern United States. Uncertainty in western U.S. winter precipitation is known to be high, so much so that some studies have found model simulated precipitation to produce similar or better large-scale estimates of annual precipitation than gridded observational products during climatologically anomalous years. This study evaluates high-resolution gridded precipitation estimates from Multi-Radar Multi-Sensor (MRMS) and Stage IV as well as forecasts from NOAA’s High-Resolution Rapid Refresh (HRRR) model in the Colorado Rocky Mountains. Gridded precipitation estimates and forecasts are compared with in situ SNOTEL measurements for two seasons of wintertime precipitation. The influence of forecast length, lead time, and model elevation on seasonal precipitation predictions from the HRRR are investigated. Additional comparisons are made with the relatively dense network of observations deployed in Colorado’s East River Watershed during the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign. Gridded products and forecasts are found to underestimate cold-season precipitation by 25%–65% relative to in situ and aircraft measurements, with longer forecast periods and lead times (6–24 h) having smaller biases (25%–30%) than shorter forecast periods and lead times (55%–65%). The assessment of multiple years of observations indicates that these biases are related more to the data and methods used to create the gridded products and forecasts than to precipitation characteristics. Significance Statement In the mountainous western United States, it is very challenging to both observe and forecast wintertime precipitation, yet snowfall plays an important role in providing the region’s annual water supply. This study aims to increase our understanding of the biases in observations and forecasts of snowfall in the Colorado Rocky Mountains, which can in turn impact forecasts of water availability for the ensuing warm season. In this study we find high-resolution gridded precipitation estimates and forecasts to underestimate cold-season precipitation when compared with in situ observing stations, with longer-range forecasts (e.g., daily) being the least biased. These findings were consistent over two years of study and have broad implications for the hydrologic modeling and water management communities.

A Spatiotemporal Enhanced SMAP Freeze/Thaw Product (1980–2020) over China and Its Preliminary Analyses

The soil freeze/thaw (FT) state has emerged as a critical role in the ecosystem, hydrological, and biogeochemical processes, but obtaining representative soil FT state datasets with a long time sequence, fine spatial resolution, and high accuracy remains challenging. Therefore, we propose a decision-level spatiotemporal data fusion algorithm based on Convolutional Long Short-Term Memory networks (ConvLSTM) to expand the SMAP-enhanced L3 landscape freeze/thaw product (SMAP_E_FT) temporally. In the algorithm, the Freeze/Thaw Earth System Data Record product (ESDR_FT) is sucked in the ConvLSTM and fused with SMAP_E_FT at the decision level. Eight predictor datasets, i.e., soil temperature, snow depth, soil moisture, precipitation, terrain complexity index, area of open water data, latitude and longitude, are used to train the ConvLSTM. Direct validation using six dense observation networks located in the Genhe, Maqu, Naqu, Pali, Saihanba, and Shandian river shows that the fusion product (ConvLSTM_FT) effectively absorbs the high accuracy characteristics of ESDR_FT and expands SMAP_E_FT with an overall average improvement of 2.44% relative to SMAP_E_FT, especially in frozen seasons (averagely improved by 7.03%). The result from indirect validation based on categorical triple collocation also shows that ConvLSTM_FT performs stable regardless of land cover types, climate types, and terrain complexity. The findings, drawn from preliminary analyses on ConvLSTM_FT from 1980 to 2020 over China, suggest that with global warming, most parts of China suffer from different degrees of shortening of the frozen period. Moreover, in the Qinghai–Tibet region, the higher the permafrost thermal stability, the faster the degradation rate.

Mesoscale structures in the Orinoco basin during an extreme precipitation event in the tropical Andes

During the night of March 31st, 2017, a severe precipitation event affected the city of Mocoa, in the tropical Andes. Total 24-h accumulated precipitation during that day was the fourth largest on record. Satellite data shows that the event was associated with a Mesoscale Convective System (MCS) that formed over the Amazon and moved westward, reaching the tropical Andes. Reanalysis data suggests that a rapid intensification of the Orinoco Low-Level Jet (OLLJ) traveling southwestward parallel to the Andes was a precursor that favored the zones of convergence for MCS formation. Upstream intensification of the OLLJ was evident 8 h prior to the Mocoa precipitation event. Given the lack of a dense network of observations in this understudied region, we use the Weather Research and Forecasting model (WRF) to explore the plausible mesoscale structures in the OLLJ region associated with the initiation and development of the MCS. We study an ensemble of simulations with different grid spacings (12, 4 and 1.3 km) and Planetary Boundary Layer (PBL) schemes (YSU, MYNN and QNSE). The more realistic MCSs were obtained with the QNSE and YSU schemes, given that the corresponding simulations included a density current in the lowest levels moving parallel to the Andes, with a sharp line of convergence and large vertical velocities over the leading edge of the mesoscale disturbance. In contrast, the MYNN scheme produced a weaker OLLJ and no density current. It is suggested that the stronger vertical mixing in the MYNN scheme was associated with the vertical dilution of the OLLJ, and with a much weaker low-level traveling perturbation via the upward radiation of energy by gravity waves. Our results help to better understand flood-producing extreme events over the poorly studied Andes-Amazon region and provide the groundwork for improved predictability of such storms.

Field evaluation of low-cost electrochemical air quality gas sensors under extreme temperature and relative humidity conditions

Abstract. Modern electrochemical gas sensors hold great potential for improving practices in air quality (AQ) monitoring as their low cost, ease of operation and compact design can enable dense observational networks and mobile measurements. Despite that, however, numerous studies have shown that the performance of these sensors depends on a number of factors (e.g. environmental conditions, sensor quality, maintenance and calibration), thereby adding significant uncertainties in the reported measurements and large discrepancies from those recorded by reference-grade instruments. In this work we investigate the performance of electrochemical sensors, provided by two manufacturers (namely Alphasense and Winsen), for measuring the concentrations of CO, NO2, O3 and SO2. To achieve that we carried out collocated yearlong measurements with reference-grade instruments at a traffic AQ monitoring station in Nicosia, Cyprus, where temperatures ranged from ca. 0 ∘C in the winter to almost 45 ∘C in the summer. The CO sensors exhibit the best performance among all the ones we tested, having minimal mean relative error (MRE) compared to reference instruments (ca. −5 %), although a significant difference in their response was observed before and after the summer period. At the other end of the spectrum, the SO2 sensors reported concentration values that were at least 1 order of magnitude higher than the respective reference measurements (with MREs being more than 1000 % for Alphasense and almost 400 % for Winsen throughout the entire measurement period), which can be justified by the fact that the concentrations of SO2 at our measuring site were below their limit of detection. In general, variabilities in the environmental conditions (i.e. temperature and relative humidity) appear to significantly affect the performance of the sensors. When compared with reference instruments, the CO and NO2 electrochemical sensors provide measurements that exhibit increasing errors and decreasing correlations as temperature increases (from below 10 to above 30 ∘C) and RH decreases (from >75 % to below 30 %). Interestingly, the performance of the sensors was affected irreversibly during the hot summer period, exhibiting different responses before and after that, resulting in a signal deterioration that was more than twice that reported by the manufacturers. With the exception of the Alphasense NO2 sensor, all low-cost sensors (LCSs) exhibited measurement uncertainties that were much higher, even at the beginning of our measurement period, compared to those required for qualifying the sensors for indicative air quality measurements according to the respective European Commission (EC) Directive. Overall, our results show that the response of all LCSs is strongly affected by the environmental conditions, warranting further investigations on how they are manufactured, calibrated and employed in the field.

Regionalisation of rainfall depth–duration–frequency curves with different data types in Germany

Abstract. Rainfall depth–duration–frequency (DDF) curves are required for the design of several water systems and protection works. For the reliable estimation of such curves, long and dense observation networks are necessary, which in practice is seldom the case. Usually observations with different accuracy, temporal resolution and density are present. In this study, we investigate the integration of different observation datasets under different methods for the local and regional estimation of DDF curves in Germany. For this purpose, two competitive DDF procedures for local estimation (Koutsoyiannis et al., 1998; Fischer and Schumann, 2018) and two for regional estimation (kriging theory vs. index based) are implemented and compared. Available station data from the German Weather Service (DWD) for Germany are employed, which includes 5000 daily stations with more than 10 years available, 1261 high-resolution (1 min) recordings with an observation period between 10 and 20 years, and finally 133 high-resolution (1 min) recordings with 60–70 years of observations. The performance of the selected approaches is evaluated by cross-validation, where the local DDFs from the long sub-hourly time series are considered the true reference. The results reveal that the best approach for the estimation of the DDF curves in Germany is by first deriving the local extreme value statistics based on Koutsoyiannis et al.'s (1998) framework and later using the kriging regionalisation of long sub-hourly time series with the short sub-hourly time series acting as an external drift. The integration of the daily stations proved to be useful only for DDF values of a low return period (T[a] < 10 years) but does not introduce any improvement for higher return periods (T[a] ≥ 10 years).

Contemporary Velocity Field for Turkey Inferred from Combination of a Dense Network of Long Term GNSS Observations

The Anatolia?Aegean domain represents a broad plate boundary zone, with the deformation accommodated by major faults bounding quasi-low deforming units. First-order features of this deformation were obtained in the form of a GNSS-derived velocity field. During the last decade, the accuracy of velocity solutions was improved, and the expansion of continuous networks increased spatial resolution. Nonetheless, an accurate representation of the deformation field requires interstation distances much lower than the locking depth of nearby faults, which has not yet been satisfied. The basis for creating a precise and accurate velocity field is uniform processing of the time series recorded both in the campaign and permanent GNSS stations, at once and for a single reference system. Although for Anatolia the data density has increased 6-fold since the landmark work of Reilinger et al. (2006), this crucial integration has not been made. We aim to fill this gap by analyzing the data and providing a uniform velocity solution. In this study, we processed the time series of 836 stations, of which 178 are published for the first time. With a period of up to 28 years, we present the most accurate velocity field with increased spatial and temporal resolution and homogeneity. We used the improved coverage of the velocity field to calculate strain accumulation on the North and East Anatolian Faults. Modelled slip rates vary between 20 and 26 mm/yr and 9.7 and 11 mm/yr for the North and East Anatolian faults, respectively. The data can better constrain the kinematics of continental deformation, provide accurate boundary conditions for dynamic models, and help test outstanding hypotheses about the kinematics of the Anatolia-Aegean domain.

Geodynamic monitoring at the GS RAS: Current state and development prospects

Modern trends in geosciences includes numerical modeling based on vast amounts of direct earth’s surface measurements. A significant increase in the accuracy, detail and scale of the aforementioned measurements in recent decades is based on the active development of space and satellite geodetic methods, as well as the creation of dense observation networks. GS RAS is one of the pioneers in the development of geodynamic monitoring systems based on the usage of satellite geodetic data on the territory of the Russian Federation. The goal of maintaining the leadership position of the GS RAS in the field of geodynamic monitoring emphasizes the development of modern satellite geodetic methods in conditions of limited resources and the need to develop import-substituting technologies. The article considers the existing scientific and methodological groundwork created in the geodynamic monitoring sector of the GS RAS, and formulates promising development areas of the geodynamic monitoring subsystem.

Climate of the Pokuttia and Bukovyna Carpathians and foreland in the second half of the 19th – at the beginning of 20th centuries

Climate reconstruction of the Pokuttia and Bukovyna Carpathians, its foreland and adjacent mountain and foothill areas in the 19th century is performed due to a dense network of meteorological observations, starting from the 60s of the 19th century in the Bukovyna and Galychyna provinces within the Austrian Monarchy and several paleoclimatic reconstructions, mainly dendroclimatic, carried out on the basis of data collected in the Eastern Carpathians. Spatial interpolations using universal kriging has been performed based on in-situ homogenized data of weather stations of the second half of the 19th century – the first half of the 20th century. The longest series of observations in the city of Chernivtsi since 1852 has enabled to track multidecadal changes in average annual and monthly air temperatures and atmospheric precipitation. The centennial course of these climatic indicators corresponds to the general patterns characteristic of all the Eastern Carpathians with the most pronounced continentality among the rest of the Carpathian regions. Cold decades in the second half of the 19th century in Chernivtsi are considered the continuation of the coldest period of 1720–1850 in the Eastern Carpathians in the last 600 years. The spatial distribution peculiarities of average monthly temperatures in the second half of the 19th – at the beginning of 20th centuries in the region are manifested in a pronounced orographic steplike isotherm pattern. In the second half of the 19th century – the first half of the 20th century, the average long-term temperatures ranged from +3.5 to +4 °C in the southwest to +8 °C in the north and northeast in accordance to the orientation of the mountains. Inversion features of the valleys and internal lowlands are revealed in the spatial distribution of average temperatures, especially in the summer months. These features are also distinguished by a lower amount of atmospheric precipitation in the dry months of spring and autumn. Spatial distribution of temperature and precipitation in the second half of the 19th century – at the beginning of 20th century is also distinctive in the the inner mountain ridges of the adjacent Hryniava and Chornyi Dil mountains. These mountains are founded to be the coldest, often with a lack of meteorological summer and a maximum of precipitation in the spring and autumn months. A comparative analysis of the spatio-temporal distribution of average multi-year air temperatures and precipitation at the turn of the 19th and 20th centuries was performed. In general, similar distribution of air temperatures and precipitation is revealed. The differences are more noticeable in the monthly context, especially in summer and in the mountainous parts of the region. Comparing the turns of 19th and 20th centuries, the average July monthly temperature in the southernmost mountain massifs of the region has increased by 1 °C, while in the foothills the increase does not exceed 0.5 °C. In the annual context, changes are less noticeable. The range of air temperature changes from north to south is the same at the turn of both the 19th and 20th centuries. The city of Chernivtsi is founded to become a bit warmer (by 0.5 °C), but interannual fluctuations of annual air temperatures within 2 °C are typical throughout all the centuries. Alongside, high annual temperatures (+9 °C and higher) have been observed more often. While in the last century such temperatures were recorded once per 5–10 years, in the first decade of this century, annual temperature above +9 °C is recorded every second year. Short-term fluctuations (5–10 years) of annual precipitation amounts have been characteristic of the whole period since the 19th century. The interpretation of local climatic differences involves further devolopment of accurate interpolation techniques and downscaling, especially for the construction of precipitation field given lack of high-mountain weather stations in the earlier centuries. The latter corresponds to the future studies of the author.

A multi-temporal and multi-angular approach for systematically retrieving soil moisture and vegetation optical depth from SMOS data

Passive microwave remote sensing of soil moisture is an underdetermined problem, as observed microwave emission from the landscape is affected by a variety of unknown surface parameters. Increasing observation information is an effective means to make retrievals more robust. In this study, a multi-temporal and multi-angular (MTMA) approach is proposed using SMOS (Soil Moisture and Ocean Salinity) satellite L-band data for retrieving vegetation optical depth (VODp, p indicates the polarization with H for horizontal and V for vertical)), effective scattering albedo (ωpeff), soil surface roughness (Zps), and soil moisture (SMp). The advantage of the MTMA approach is that it does not need auxiliary data as inputs or constraints. SMOS polarization-dependent VOD are produced and compared at a global scale for the first time, and it is found that the polarization difference of vegetation effects should not be ignored in the SM retrieval algorithm. The MTMA VOD retrievals are found to have a reasonable global spatial distribution, which is generally consistent with the VOD retrievals obtained from the SMOS Level 3 (SMOS-L3) and SMOS-IC Version 2 (V2) (referred to as SMOS-IC), except for showing relatively lower values over densely vegetated areas compared with the other two SMOS products. The spatial distribution of retrieved ωpeff generally shows a dependence on both VOD and land cover types. In addition, the values of MTMA-ωVeff are higher than that of MTMA-ωHeff, indicating stronger microwave scattering of V-pol in the vegetation layer than that of H-pol. The retrieved surface roughness parameter (Zps) ranges from 0.04 to 0.22 cm, and its spatial distribution is partially different from the existing roughness products/auxiliary data from SMOS and SMAP. The retrieved MTMA SM shows generally high correlations with in-situ measurements (11 dense observation networks) with overall correlation coefficients of > 0.75. The overall ubRMSE of MTMA-SMH and MTMA-SMV are < 0.055 m3/m3 and lower than that of SMOS-IC and SMOS-L3 products. SMOS-IC generally presents higher correlation coefficients compared to MTMA in most sites outside China; in China, RFI filtering is crucial and makes it very difficult when comparing algorithms based on different brightness temperature products. The number of effective retrievals of MTMA-SMH and MTMA-SMV ranges from 1409 to 1640 and 1104 to 1603 respectively, which is more than that from SMOS-IC (from 236 to 1358) over the selected 11 networks. Therefore, it is concluded that by incorporating multi-temporal SMOS data, the proposed method of MTMA can be used to systematically retrieve SM, VOD and additional surface parameters (effective scattering albedo and surface roughness) with comparable or better performance of SM than that of SMOS-IC and SMOS-L3. Moreover, this paper for the first time produced a polarization-dependent SMOS VOD product at a global scale.

Improving NOx emission estimates in Beijing using network observations and a perturbed emissions ensemble

Abstract. Emissions inventories are crucial inputs to air quality simulations and represent a major source of uncertainty. Various methods have been adopted to optimise emissions inventories, yet in most cases the methods were only applied to total anthropogenic emissions. We have developed a new approach that updates a priori emission estimates by source sector, which are particularly relevant for policy interventions. At its core is a perturbed emissions ensemble (PEE), constructed by perturbing parameters in an a priori emissions inventory within their respective uncertainty ranges. This PEE is then input to an air quality model to generate an ensemble of forward simulations. By comparing the simulation outputs with observations from a dense network, the initial uncertainty ranges are constrained, and a posteriori emission estimates are derived. Using this approach, we were able to derive the transport sector NOx emissions for a study area centred around Beijing in 2016 based on a priori emission estimates for 2013. The absolute emissions were found to be 1.5–9 × 104 Mg, corresponding to a 57 %–93 % reduction from the 2013 levels, yet the night-time fraction of the emissions was 67 %–178 % higher. These results provide robust and independent evidence of the trends of traffic emission in the study area between 2013 and 2016 reported by previous studies. We also highlighted the impacts of the chemical mechanisms in the underlying model on the emission estimates derived, which is often neglected in emission optimisation studies. This work paves forward the route for rapid analysis and update of emissions inventories using air quality models and routine in situ observations, underscoring the utility of dense observational networks. It also highlights some gaps in the current distribution of monitoring sites in Beijing which result in an underrepresentation of large point sources of NOx.

Optimizing Spatial Quality Control for a Dense Network of Meteorological Stations

Abstract Crowdsourced meteorological observations are becoming more prevalent and in some countries their spatial resolution already far exceeds that of traditional networks. However, due to the larger uncertainty associated with these observations, quality control (QC) is an essential step. Spatial QC methods are especially well suited for such dense networks since they utilize information from nearby stations. The performance of such methods usually depends on the choice of their parameters. There is, however, currently no specific procedure on how to choose the optimal settings of such spatial QC methods. In this study we present a framework for tuning a spatial QC method for a dense network of meteorological observations. The method uses artificial errors in order to perturb the observations to simulate the effect of having errors. A cost function, based on the hit and false alarm rate, for optimizing the spatial QC method is introduced. The parameters of the spatial QC method are then tuned such that the cost function is optimized. The application of the framework to the tuning of a spatial QC method for a dense network of crowdsourced observations in Denmark is presented. Our findings show that the optimal settings vary with the error magnitude, time of day, and station density. Furthermore, we show that when the station network is sparse, a better performance of the spatial QC method can be obtained by including crowdsourced observations from another denser network.

Summertime Assessment of an Urban-Scale Numerical Weather Prediction System for Toronto

Urban-scale Numerical Weather Prediction (NWP) systems will be important tools for decision-making in and around large cities in a changing climate exposed to more extreme weather events. Such a state-of-the-art real-time system down to 250-m grid spacing was implemented in the context of the Toronto 2015 Panamerican games, Canada (PanAm). Combined with the Global Environmental Multiscale (GEM) model, attention was brought to the representation of the detailed urban landscape, and to the inclusion of sub-daily variation of the Great Lakes surface temperature. Results show a refined representation of the urban coastal environment micro-meteorology with a strong anisotropy of the urban heat island reaching about 2 °C on average for the summer season, coastal upwelling, and mesoscale features such as cumulus clouds and lake-breeze flow. Objective evaluation at the surface with a dense observational network reveals an overall good performance of the system and a clear improvement in comparison to reference forecasts at 2.5-km grid spacing in particular for standard deviation errors in urban areas up to 0.3 °C for temperature and dew point temperature, and up to 0.5 m s−1 for the wind speed, as well as for precipitation with an increased Equitable Threat Score (ETS) by up to 0.3 for the evening accumulation. The study provides confidence in the capacity of the new system to improve weather forecasts to be delivered to urban dwellers although further investigation of the initialization methods in urban areas is needed.

Rapid Estimation of Seismic Intensities Using a New Algorithm That Incorporates Array Technologies and Ground-Motion Prediction Equations (GMPEs)

ABSTRACTRapid seismic intensity maps for damaging earthquakes enable the swift implementation of earthquake disaster mitigation action, issuance of accurate tsunami warnings, and prevention of associated secondary disasters. However, many countries lack dense local seismic observation networks, making it infeasible to obtain accurate seismic intensity maps of earthquakes within a few hours, particularly for earthquakes that have considerable source extents. In this study, we developed a new algorithm for rapidly obtaining seismic intensity maps of damaging earthquakes. With our model, source energy radiation is acquired using backprojection, and then the locations and relative amplitudes of the fault geometry and subevents are determined. Peak ground accelerations and peak ground velocities (PGVs) are subsequently calculated based on ground-motion prediction equations and the distribution of the estimated subevents. PGVs are then further site-corrected using the VS30 database (Wald and Allen, 2007; Heath et al., 2020). The algorithm was applied to the 2008 Mw 7.9 Wenchuan and 2010 Mw 6.9 Yushu earthquakes, and the resulting seismic intensity maps were highly similar to those generated by field surveys. The algorithm is simple and straightforward to use, and local real-time instrument observations are not required. Calculations can be performed automatically, and reliable seismic intensity maps can be issued within 30 min following damaging earthquakes. The model’s application may assist greatly with rescue and recovery efforts, and enable tsunami hazards to be evaluated immediately following earthquakes, particularly in regions lacking dense observation networks.

Assimilation of Cosmogenic Neutron Counts for Improved Soil Moisture Prediction in a Distributed Land Surface Model

Cosmic-Ray Neutron Sensing (CRNS) offers a non-invasive method for estimating soil moisture at the field scale, in our case a few tens of hectares. The current study uses the Ensemble Adjustment Kalman Filter (EAKF) to assimilate neutron counts observed at four locations within a 655 km2 pre-alpine river catchment into the Noah-MP land surface model (LSM) to improve soil moisture simulations and to optimize model parameters. The model runs with 100 m spatial resolution and uses the EU-SoilHydroGrids soil map along with the Mualem–van Genuchten soil water retention functions. Using the state estimation (ST) and joint state–parameter estimation (STP) technique, soil moisture states and model parameters controlling infiltration and evaporation rates were optimized, respectively. The added value of assimilation was evaluated for local and regional impacts using independent root zone soil moisture observations. The results show that during the assimilation period both ST and STP significantly improved the simulated soil moisture around the neutron sensors locations with improvements of the root mean square errors between 60 and 62% for ST and 55–66% for STP. STP could further enhance the model performance for the validation period at assimilation locations, mainly by reducing the Bias. Nevertheless, due to a lack of convergence of calculated parameters and a shorter evaluation period, performance during the validation phase degraded at a site further away from the assimilation locations. The comparison of modeled soil moisture with field-scale spatial patterns of a dense network of CRNS observations showed that STP helped to improve the average wetness conditions (reduction of spatial Bias from –0.038 cm3 cm−3 to –0.012 cm3 cm−3) for the validation period. However, the assimilation of neutron counts from only four stations showed limited success in enhancing the field-scale soil moisture patterns.

Application of the Modified PLUM Method to a Dense Seismic Intensity Network of a Local Government in Japan: A Case Study on Tottori Prefecture

An application of the PLUM (Propagation of Local Undamped Motion) method to real-time seismic intensity distribution from a dense seismic intensity network maintained by a local government in Japan is demonstrated. The JMA (Japan Meteorological Agency) has employed the PLUM method from March 2018 as a supplement to the traditional EEW (Earthquake Early Warning) which requires hypocenter determination. A dense observation network is desirable for upgrading accuracy and immediateness of EEW including the PLUM method. Seismic intensity meters in Tottori Prefecture, Japan, are suitable for the purpose because they have been improved to broadcast packets with peak ground acceleration and JMA seismic intensity at every 1 s. Also, 34 seismic intensity meters are installed in the target area while only six observation sites are used for the EEW by the JMA. The packet data are received at Tottori University, and the modified PLUM method considers wave propagation with damping from not only the observation points but also all evaluating grid points at a 1 km mesh. Additionally, P-wave amplitudes in preliminary tremors are also introduced to estimate seismic intensities from an empirical relationship. Applying the methodology to past earthquake data, more detailed and rapid evaluations of seismic intensity distribution are achieved. We expect the system will be utilized for earthquake disaster mitigation measures of local governments.

Numerical discretization causing error variance loss and the need for inflation

AbstractThe effects of model discretization errors on the propagation of error covariance have a more complex nature than the effect on the state variable. The analysis is carried out for the advection transport equation, where the continuous (in space and time) propagation of the related error covariance function can be written, solved and compared with the discrete model applied to the covariance matrix. The numerical analysis of the problem is carried out with a 1D‐problem, but is also illustrated with a 3D chemical transport model (CTM) used for chemical data assimilation. It is shown that variance loss (compared to the continuous propagation solution) depends on the covariance function itself as well as the numerical discretization scheme. The variance loss is particularly sensitive to the correlation length. In a simple first‐order discretization, an analytical expression is obtained and is used to derive an analytical expression for inflation. Experiments show, for example, that following an analysis over a dense network of observations (with spatially uncorrelated errors) a significant variance loss occurs in the propagation step. With the (variance) inflation scheme, we are able to restore the variance lost at each grid point, and at each timestep, during the entire integration. The variance inflation scheme applied to an EnKF can be formulated to change the variance spread of the ensemble or to act directly on the state.

Urban nocturnal cooling mediated by bluespace

The spatiotemporal characteristics of air temperature and humidity mediated by urban bluespace are investigated using a combination of dense network of climatological observations in a medium-sized US city, computational fluid dynamics, and analytical modeling approaches. Both numerical simulation and observational results show that the rate of change of hourly averaged air temperature and humidity at 3.5 m over urban areas peaks 2 h after sunset, while it decreases with time monotonically over greenspace, indicating different impacts due to presence of urban lakes. The apparent temperature decreases with distance to lakes in urban area due to higher near-shore humidity. This highlights that urban lakes located near city center can deteriorate the nighttime cooling effects due to elevated humidity. Finally, two analytical models are presented to explain the connection between the surface and air temperature as well as the spatial variation of air temperature and humidity adjacent to the urban lakes. These simplified models with parameters being inferred from the network of measurements have reasonably good performance compared to the observations. Compared to other sophisticated numerical simulations, these analytical models offer an alternative means that is easily accessible for evaluating the efficacy of bluespace on urban nocturnal cooling.

Generation of a long-term daily gridded precipitation dataset for the Upper Indus Basin (UIB) through temporal Reconstruction, Correction & Informed Regionalization-“ReCIR”

This study attempted to generate a long-term (1961–2010) daily gridded precipitation dataset for the Upper Indus Basin (UIB) with orographic adjustments so as to generate realistic precipitation estimates, enabling hydrological and water resource investigations that can close the water balance, that is difficult, if not impossible to achieve with the currently available precipitation data products for the basin. The procedure includes temporal reconstruction of precipitation series at points where data were not recorded prior to the mid-nineties, followed by a regionalization of the precipitation series to a smaller scale across the basin (0.125 ° × 0.125 °), while introducing adjustments for the orographic effect and changes in glacier storage. The reconstruction process involves interpolation of the precipitation at virtual locations of the current (1995-) dense observational network, followed by corrections for frequency and intensity and adjustments for temporal trends at these virtual locations. The data generated in this way were further validated for temporal and spatial representativeness through evaluation of SWAT-modelled streamflow responses against observed flows across the UIB. The results show that the calibrated SWAT-simulated daily discharge at the basin outlet as well as at different sub-basin outlets, when forcing the model with the reconstructed precipitation of years 1973–1996, is almost identical to that when forcing it with the reference precipitation data (1997–2008). Finally, the spatial distribution pattern of the reconstructed (1961–1996) and reference (1997–2008) precipitation were also found consistent across the UIB, reflecting well the large-scale atmospheric-circulation pattern in the region.

Evaluation of fine resolution gridded rainfall datasets over a dense network of rain gauges in Niger

This study evaluates the reliability of gridded rainfall products with fine spatial resolution using 29 gauges of a dense observation network in Niger. Four gridded datasets were considered, viz. the African Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Climate Prediction Center morphing technique (CMORPH) and Tropical Applications of Meteorology using Satellite data and ground-based observations (TAMSAT). The datasets were quantitatively compared to the gauge rainfall at different timescales and their rainfall detection skills were assessed using four validation metrics and three contingency indices, respectively. Furthermore, their potentials for trend analysis was assessed using a modified Mann-Kendall test; and finally, their capability of capturing damaging rainfalls was evaluated using data of five historical damaging rainfall amounts on a daily basis. The CMORPH product showed a good performance in detecting rainfall events and exhibited the best linear relationship with the gauge rainfall; however, it displayed high errors and relative bias (RB > 100%)—showing its inaccuracy in rainfall quantification. At daily, dekadal and monthly timescale, CHIRPS showed the lowest errors, followed by the ARC2 and TAMSAT, whereas ARC2 exhibited the lowest RB—between −12% and 22%. All of the four datasets were unable to unveil the significant trends found in ground observations and were inaccurate in sensing the depths of damaging rainfall amounts. The overall results revealed that the 3-hourly CMORPH dataset is a good sub-daily rainfall product—whereas daily, dekadal and monthly CHIRPS and ARC2 rainfalls have good potentials for hydro-climatological applications in Niger.