Dense Observation Network Research Articles

REAL-TIME ASSIMILATION USING A DENSE ARRAY OF DIRECTIONAL WAVE OBSERVATIONS

Wave conditions along our coastlines are monitored using networks of wave buoys. Augmented with regional wave now- and hind-casts from operational wave models, these data networks provide detailed regional information of wave conditions providing vital updates of wave conditions for maritime, engineering, recreational and scientific purposes. Currently, the observational networks are mostly used to initiate models and assess model performance, but are usually not directly integrated into the modeling system. Recent work by Crosby et al. (2017) explores the integration of buoy data into models and shows that data assimilation of buoy observations into models can improve predictions and wave hindcasts. The results suggest that assimilation of dense observational networks results in significant and important improvements in model performance. In the current work we leverage these modeling advances with the recent development of low-cost directional wave buoys (such as the Spoondrift Spotter, www.spoondrift.co). The use of low-cost and solar powered instruments allows for much denser long-term arrays of instruments than was previously possible. The availability of large numbers of independent observations, in turn, can provide excellent constrains on models and model boundary conditions.

Cyclic Properties of Seismic Noise and the Problem of Predictability of the Strongest Earthquakes in Japanese Islands

The results of an analysis of the properties of low-frequency seismic noise in the Japanese Islands from early 1997 to March 2018 are presented. The time interval under consideration includes one of the largest seismic disasters of recent times, the Tohoku earthquake of March 11, 2011. The presence of a dense network of seismic observations provides a unique opportunity to investigate how the preparation of a strong earthquake is reflected in changes in the properties of seismic noise in time and space. An analysis of the clustering of the daily multifractal and entropic properties of seismic noise in a 1-year moving time window averaged over all stations of the network made it possible to find a 2.5-year periodicity established since the beginning of 2003. This periodicity correlates with the occurrence of strong earthquakes in Japan. Studying features of the spatial distribution of seismic-noise properties allows us to put forward a hypothesis about the increased danger of the next megaearthquake in Japan in the area of the contact between the northern boundary of the Philippine Sea plate and Honshu Island, in the Nankai deepwater trough area, not far from Tokyo. In the Japanese Islands, in addition to the network of seismic observations, there is a dense network of fixed GPS points for which observations are available from the beginning of March 2015 with a time step of 5 min. The availability of such measurements makes it possible to complement the analysis of seismic-noise properties and to calculate the degree of correlation of GPS data at any point from measurements at neighboring stations. An analysis and processing of measurement results show that the most intense spot of increased correlation of the Earth’s surface tremor, measured using GPS, is located in the Nankai Trough, with the center at the point of 34° N and 138° E.

Practical Ensemble-Based Approaches to Estimate Atmospheric Background Error Covariances for Limited-Area Deterministic Data Assimilation

AbstractHigh-resolution flow-dependent background error covariances can allow for a better usage of dense observation networks in applications of data assimilation for numerical weather prediction. The generation of high-resolution ensembles, however, can be computationally cost prohibitive. In this study, practical and low-cost ensemble generation methods are presented and compared against both global and regional ensemble Kalman filters (G-EnKF and R-EnKF, respectively). The goal is to provide limited-area deterministic assimilation schemes with higher-resolution flow-dependent background error covariances that perform at least as well as those from the G-EnKF when assimilating the same observations. The low-cost methods are based on short-range regional ensemble forecasts initialized from 1) deterministic analysis plus balanced perturbations (filter free approach) and 2) a simplified ensemble square root filter (S-EnSRF), centered on deterministic analyses. The resulting ensembles from the different approaches are used within a 4D ensemble–variational (4D-EnVar) assimilation system covering most of Canada and the northern United States. Diagnostic results show that the mean is an important component of the ensembles. Results also show that the persistence of the homogeneous characteristics of the perturbations in the filter free approach makes this method unsuited for short assimilation time windows since some error structures take longer to develop. The S-EnSRF approach overcomes this limitation by recycling part of the prior perturbations. Results from 1-month assimilation experiments show that the S-EnSRF and R-EnKF experiments provide forecasts of similar quality to those from G-EnKF. Furthermore, results from precipitation verification indicate that the R-EnKF experiment provides the best precipitation accumulation predictions over 24-h periods.

Evaluation of convection-permitting WRF CONUS simulation on the relationship between soil moisture and heatwaves

Soil moisture plays an important role in modulating regional climate from sub-seasonal to seasonal timescales. Particularly important, soil moisture deficits can amplify summer heatwaves (HWs) through soil moisture-temperature feedback which has critical impacts on society, economy and human health. In this study, we evaluate decade-long convection-permitting Weather Research and Forecast (WRF) model simulations over the contiguous US on simulating heatwaves and their relationship with antecedent soil moisture using a dense observational network. We showed that the WRF model is capable of capturing the spatial patten of temperature threshold to define HWs, though the simulation shows a warm bias in the Midwest and cold bias in western mountainous regions. Two HW indices, based on frequency (HWF) and magnitude (HWM), are evaluated. Significant anti-correlations between antecedent soil moisture and both HW indices have been found in most parts of the domain except the South Pacific Coast. A detailed study has been conducted for the Midwest and South Great Plains regions, where two heatwaves had occurred in the last decade. In both regions, the high quantile of the HWF distribution shows a strong dependence on antecedent soil moisture: drier soil leads to much larger increase on the upper quantile of HWF than it does on the lower quantile. Soil moisture effects on the higher end of HWM are not as strong as on the lower end: wetter antecedent soil corresponds to a larger decrease on the lower quantile of HWM. WRF captures the heterogeneous responses to dry soil on HWF distribution in both regions, but overestimates these HWM responses in the Midwest and underestimates them in the South Great Plains. Our results show confidence in WRF’s ability to simulate HW characteristics and the impacts of antecedent soil moisture on HWs. These are also important implications for using high-resolution convection-permitting mode to study the coupling between land and atmosphere.

Measurement-based assessment of health burdens from long-term ozone exposure in the United States, Europe, and China

Long-term ozone (O3) exposure estimates from chemical transport models are frequently paired with exposure-response relationships from epidemiological studies to estimate associated health burdens. Impact estimates using such methods can include biases from model-derived exposure estimates. We use data solely from dense ground-based monitoring networks in the United States, Europe, and China for 2015 to estimate long-term O3 exposure and calculate premature respiratory mortality using exposure-response relationships derived from two separate analyses of the American Cancer Society Cancer Prevention Study-II (ACS CPS-II) cohort. Using results from the larger, extended ACS CPS-II study, 34 000 (95% CI: 24, 44 thousand), 32 000 (95% CI: 22, 41 thousand), and 200 000 (95% CI: 140, 253 thousand) premature respiratory mortalities are attributable to long-term O3 exposure in the USA, Europe and China, respectively, in 2015. Results are approximately 32%–50% lower when using an older analysis of the ACS CPS-II cohort. Both sets of results are lower (∼20%–60%) on a region-by-region basis than analogous prior studies based solely on modeled O3, due in large part to the fact that the latter tends to be high biased in estimating exposure. This study highlights the utility of dense observation networks in estimating exposure to long-term O3 exposure and provides an observational constraint on subsequent health burdens for three regions of the world. In addition, these results demonstrate how small biases in modeled results of long-term O3 exposure can amplify estimated health impacts due to nonlinear exposure-response curves.

A comparison of posterior atmospheric CO<sub>2</sub> adjustments obtained from in situ and GOSAT constrained flux inversions

Abstract. Posterior fluxes obtained from inverse modelling are difficult to verify because there is no dense network of flux measurements available to evaluate estimates against. Here we present a new diagnostic to evaluate structures in posterior fluxes. First, we simulate the change in atmospheric CO2 fields between posterior and prior fluxes, referred to as the posterior atmospheric adjustments due to updated fluxes (PAAFs). Second, we calculate the uncertainty in atmospheric CO2 fields due solely to uncertainty in the meteorological fields, referred to as the posterior atmospheric adjustments due to imperfect meteorology (PAAMs). We argue that PAAF can only be considered robust if it exceeds PAAM, that is, the changes in atmospheric CO2 between the posterior and prior fluxes should at least exceed atmospheric CO2 changes arising from imperfect meteorology. This diagnostic is applied to two CO2 flux inversions: one which assimilates observations from the in situ CO2 network and the other which assimilates observations from the Greenhouse Gases Observing SATellite (GOSAT). On the global scale, PAAF in the troposphere reflects northern extratropical fluxes, whereas stratospheric adjustments primarily reflect tropical fluxes. In general, larger spatiotemporal variations in PAAF are obtained for the GOSAT inversion than for the in situ inversion. Zonal standard deviations of the PAAF exceed the PAAM through most of the year when GOSAT observations are used, but the minimum value is exceeded only in boreal summer when in situ observations are used. Zonal spatial structures in GOSAT-based PAAF exceed PAAM throughout the year in the tropics and through most of the year in the northern extratropics, suggesting GOSAT flux inversions can constrain zonal asymmetries in fluxes. However, we cannot discount the possibility that these structures are influenced by biases in GOSAT retrievals. Verification of such spatial structures will require a dense network of independent observations. Because PAAF depends on the choice of prior fluxes, the comparison with PAAM is system dependent and thus can be used to monitor a given assimilation system's behaviour.

Transport of Canadian forest fire smoke over the UK as observed by lidar

Abstract. Layers of aerosol at heights between 2 and 11 km were observed with Raman lidars in the UK between 23 and 31 May 2016. A network of these lidars, supported by ceilometer observations, is used to map the extent of the aerosol and its optical properties. Space-borne lidar profiles show that the aerosol originated from forest fires over western Canada around 17 May, and indeed the aerosol properties – weak volume depolarisation (<5 %) and a lidar ratio at 355 nm in the range 35–65 sr – were consistent with long-range transport of forest fire smoke. The event was unusual in its persistence – the smoke plume was drawn into an atmospheric block that kept it above north-western Europe for 9 days. Lidar observations show how the smoke layers became optically thinner during this period, but the lidar ratio and aerosol depolarisation showed little change. The results demonstrate the value of a dense network of observations for tracking forest fire smoke, and show how the dispersion of smoke in the free troposphere leads to the emergence of discrete thin layers in the far field. They also show how atmospheric blocking can keep a smoke plume in the same geographic area for over a week.

Forests dominate the interannual variability of the North American carbon sink

Understanding what drives the interannual variability (IAV) of the land carbon sink is crucial for improving future predictions of this important, yet uncertain, component of the climate system. While drivers of global and hemispheric-scale net ecosystem exchange (NEE) IAV have been investigated, our understanding of the drivers of NEE IAV at regional scales (e.g. sub-continental, biome-level) is quite poor. Here we explore the biome-level attribution and drivers of North American NEE using inverse estimates derived from a dense network of atmospheric CO2 observations. We find that deciduous broadleaf and mixed forests are the primary regions responsible for North American NEE IAV, which differs from the ecoregions identified for the globe and Northern Hemisphere. We also find that a suite of terrestrial biosphere models (TBMs) do not agree on the dominant biome contributing to NEE IAV, with TBMs falling along an apparent spectrum ranging between those with IAV dominated primarily by forested ecosystems to those with IAV dominated by non-forested ecosystems. Furthermore, this regional trade-off in TBM NEE IAV is found to be linked to differing regional responses to environmental drivers among TBMs. This work displays the importance of extra-tropical forests in driving continental NEE IAV and also highlights the challenges and limitations of using TBMs to inform regional-scale carbon flux dynamics.

Zoning of Faults and Secondary Mineralization of Host Rocks of Kimberlites of the Maiskoe Diamond Deposit, Nakyn Field, Yakutia

Study of faults and secondary mineralization of host rocks of diamond-bearing kimberlites yields important data for local prediction of kimberlite bodies. Of special methodological interest are exploration data on deposits where the study of host rocks is based on a dense observation network. Factual material for this paper was collected from cores of all inclined exploration boreholes of the Maiskoe diamond deposit found in the Nakyn field in Yakutia in 2006. The paper shows a nonuniform distribution of tectonic deformations, stringer mineralization, O and C isotopes of calcite, and CO2 content of Lower Paleozoic host carbonate rocks of kimberlites. Our data agree with different diamond potentials of two areas of the Maiskoe kimberlite body, which can be used to search for and explore deposits.

Modelling high‐latitude summer temperature patterns using physiographic variables

Recent investigations of high‐resolution temperature patterns employing dense observational networks have improved our understanding of fine‐scale climatic influences on hydrological and ecological processes. Few of these investigations, however, have focused on high‐latitude environments. In this study—one of the first to evaluate drivers of fine‐scale temperature patterns in a non‐glaciated high‐latitude environment—we evaluated physiographic controls on summer temperature while accounting for free‐air temperature conditions along a north–south transect of temperature sensors on Mt. Healy, at the eastern edge of Denali National Park and Preserve in Alaska, United States. Although the study was limited to summer, when near‐surface temperature inversions are rare, we identified average daily minimum temperature (Tmin) lapse rates shallower than −2 °C/km over five summers. Lapse rates of average temperature (Tavg) were steeper (−5.3 °C/km), but still did not approach the global average lapse rate of −6.5 °C/km. We also identified positive effects of solar radiation on both Tmin and Tavg, variation in Tmin and Tavg lapse rates across a range of synoptic temperature conditions, and variation in Tmin lapse rates across terrain convergence values. Finally, we compared relationships between elevation and temperature from transect sensors and a gridded product. Over 15 months of comparison, sensor Tavg was 0.6 °C cooler than gridded data at similar elevations. Sensor lapse rates were on average 0.5 °C steeper than those from gridded data but only significantly so in the cool month of June 2014. These differences highlight the need for additional instrumentation across this remote and topographically variable region to improve accuracy and reduce biases in gridded climate products.

The BErkeley Atmospheric CO<sub>2</sub> Observation Network: field calibration and evaluation of low-cost air quality sensors

Abstract. The newest generation of air quality sensors is small, low cost, and easy to deploy. These sensors are an attractive option for developing dense observation networks in support of regulatory activities and scientific research. They are also of interest for use by individuals to characterize their home environment and for citizen science. However, these sensors are difficult to interpret. Although some have an approximately linear response to the target analyte, that response may vary with time, temperature, and/or humidity, and the cross-sensitivity to non-target analytes can be large enough to be confounding. Standard approaches to calibration that are sufficient to account for these variations require a quantity of equipment and labor that negates the attractiveness of the sensors' low cost. Here we describe a novel calibration strategy for a set of sensors, including CO, NO, NO2, and O3, that makes use of (1) multiple co-located sensors, (2) a priori knowledge about the chemistry of NO, NO2, and O3, (3) an estimate of mean emission factors for CO, and (4) the global background of CO. The strategy requires one or more well calibrated anchor points within the network domain, but it does not require direct calibration of any of the individual low-cost sensors. The procedure nonetheless accounts for temperature and drift, in both the sensitivity and zero offset. We demonstrate this calibration on a subset of the sensors comprising BEACO2N, a distributed network of approximately 50 sensor “nodes”, each measuring CO2, CO, NO, NO2, O3 and particulate matter at 10 s time resolution and approximately 2 km spacing within the San Francisco Bay Area.

A Robust Non-Gaussian Data Assimilation Method for Highly Non-Linear Models

In this paper, we propose an efficient EnKF implementation for non-Gaussian data assimilation based on Gaussian Mixture Models and Markov-Chain-Monte-Carlo (MCMC) methods. The proposed method works as follows: based on an ensemble of model realizations, prior errors are estimated via a Gaussian Mixture density whose parameters are approximated by means of an Expectation Maximization method. Then, by using an iterative method, observation operators are linearized about current solutions and posterior modes are estimated via a MCMC implementation. The acceptance/rejection criterion is similar to that of the Metropolis-Hastings rule. Experimental tests are performed on the Lorenz 96 model. The results show that the proposed method can decrease prior errors by several order of magnitudes in a root-mean-square-error sense for nearly sparse or dense observational networks.

Improved input to distributed hydrologic model in areas with sparse subdaily rainfall data using multivariate daily rainfall disaggregation

Abstract Flood forecasting relies on good quality of observed and forecasted rainfall. In Serbia, the recording rain gauge network is sparse and rainfall data mainly come from dense non-recording rain gauges. This is not beneficial for flood forecasting in smaller catchments and short-duration events, when hydrologic models operating on subdaily scale are applied. Moreover, differences in rainfall amounts from two types of gauges can be considerable, which is common in operational hydrological practice. This paper examines the possibility of including daily rainfall data from dense observation networks in flood forecasting based on subdaily data, using the extreme flood event in the Kolubara catchment in May 2014 as a case study. Daily rainfall from a dense observation network is disaggregated to hourly scale using the MuDRain multivariate disaggregation software. The disaggregation procedure results in well-reproduced rainfall dynamics and adjusts rainfall volume to the values from the non-recording gauges. The fully distributed wflow_hbv model, which is under development as a forecasting tool for the Kolubara catchment, is used for flood simulations with two alternative hourly rainfall data. The results show an improvement when the disaggregated rainfall from denser network is used, thus indicating the significance of better representation of rainfall temporal and spatial variability for flood forecasting.

Precipitation and temperature trends over central Italy (Abruzzo Region): 1951–2012

This study analyses spatial and temporal trends of precipitation and temperatures over Abruzzo Region (central Italy), using historical climatic data from a dense observation network. The results show a general, although not significant, negative trend in the regionally averaged annual precipitation (− 1.8% of the yearly mean rainfall per decade). This reduction is particularly evident in winter, especially at mountain stations (average − 3% change/decade). Despite this general decreasing trend, a partial rainfall recovery is observed after the 1980s. Furthermore, the majority of meteorological stations register a significant warming over the last 60 years, (mean annual temperature increase of + 0.15 °C/decade), which reflects a rise in both minimum and maximum temperatures, with the latter generally increasing at a faster rate. Spring and summer are the seasons which contribute most to the general temperature increase, in particular at high elevation sites, which exhibit a more pronounced warming (+ 0.24 °C/decade). However, this tendency has not been uniform over 1951–2012, but it has been characterised by a cooling phenomenon in the first 30 years (1951–1981), followed by an even stronger warming during the last three decades (1982–2012). Finally, correlations between the climatic variables and the dominant teleconnection patterns in the Mediterranean basin are analysed to identify the potential influence of large-scale atmospheric dynamics on observed trends in Abruzzo. The results highlight the dominant role of the East-Atlantic pattern on seasonal temperatures, while more spatially heterogeneous associations, depending on the complex topography of the region, are identified between winter precipitation and the North Atlantic Oscillation, East-Atlantic and East-Atlantic/Western Russian patterns.

Statistical analysis of dispersal and deposition patterns of volcanic emissions from Mt. Sakurajima, Japan

With the eruption of Eyjafjallajökull (Iceland) in 2010, interest in the transport of volcanic ash after moderate to major eruptions has increased with regards to both the physical and the emergency hazard management aspects. However, there remain significant gaps in the understanding of the long-term behaviour of emissions from volcanoes with long periods of activity. Mt. Sakurajima (Japan) provides us with a rare opportunity to study such activity, due to its eruptive behaviour and dense observation network. In the 6-year period from 2009 to 2015, the volcano was erupting at an almost constant rate introducing approximately 500 kt of ash per month to the atmosphere. The long-term characteristics of the transport and deposition of ash and SO2 in the area surrounding the volcano are studied here using daily surface observations of suspended particulate matter (SPM) and SO2 and monthly ashfall values.Results reveal different dispersal patterns for SO2 and volcanic ash, suggesting volcanic emissions’ separation in the long-term. Peak SO2 concentrations at different locations on the volcano vary up to 2 orders of magnitude and decrease steeply with distance. Airborne volcanic ash increases SPM concentrations uniformly across the area surrounding the volcano, with distance from the vent having a secondary effect. During the period studied here, the influence of volcanic emissions was identifiable both in SO2 and SPM concentrations which were, at times, over the recommended exposure limits defined by the Japanese government, European Union and the World Health Organisation.Depositional patterns of volcanic ash exhibit elements of seasonality, consistent with previous studies. Climatological and topographic effects are suspected to impact the deposition of volcanic ash away from the vent: for sampling stations located close to complex topographical elements, sharp changes in the deposition patterns were observed, with ash deposits for neighbouring stations as close as 5 km differing as much as an order of magnitude. Despite these effects, deposition was sufficiently approximated by an inverse power law relationship, the fidelity of which depended on the distance from the vent: for proximal to intermediate areas (<20 km), errors decrease with longer accumulation periods (tested here for 1–72 months), while the opposite was seen for deposition in distal areas (>20 km).

A wind field downscaling strategy based on domain segmentation and transfer functions

AbstractThis paper presents a novel methodology for mesoscale‐to‐microscale downscaling of near‐surface wind fields. The model chain consists on the Weather Research and Forecast mesoscale model and the Alya‐CFDWind microscale model (assuming neutral stability). The downscaling methodology combines precomputed microscale simulations with a mesoscale forecast using a domain segmentation technique and transfer functions. As a result, the downscaled wind field preserves the mesoscale pattern but, at the same time, incorporates local mesoscale subgrid terrain effects, particularly at valleys and channelling zones. The methodology has been validated over a 9‐month period on a very complex terrain site instrumented with a dense observational network of meteorological masts. With respect to mesoscale results, the global skills of the downscaled wind at masts improve for wind direction and remain similar for wind velocity. However, a substantial improvement occurs under stable and neutral conditions and for high wind velocity regimes.

Air quality modelling in the summer over the eastern Mediterranean using WRF-Chem: chemistry and aerosol mechanism intercomparison

Abstract. We employ the WRF-Chem model to study summertime air pollution, the intense photochemical activity and their impact on air quality over the eastern Mediterranean. We utilize three nested domains with horizontal resolutions of 80, 16 and 4 km, with the finest grid focusing on the island of Cyprus, where the CYPHEX campaign took place in July 2014. Anthropogenic emissions are based on the EDGAR HTAP global emission inventory, while dust and biogenic emissions are calculated online. Three simulations utilizing the CBMZ-MOSAIC, MOZART-MOSAIC, and RADM2-MADE/SORGAM gas-phase and aerosol mechanisms are performed. The results are compared with measurements from a dense observational network of 14 ground stations in Cyprus. The model simulates T2 m, Psurf, and WD10 m accurately, with minor differences in WS10 m between model and observations at coastal and mountainous stations attributed to limitations in the representation of the complex topography in the model. It is shown that the south-eastern part of Cyprus is mostly affected by emissions from within the island, under the dominant (60 %) westerly flow during summertime. Clean maritime air from the Mediterranean can reduce concentrations of local air pollutants over the region during westerlies. Ozone concentrations are overestimated by all three mechanisms (9 % ≤ NMB ≤ 23 %) with the smaller mean bias (4.25 ppbV) obtained by the RADM2-MADE/SORGAM mechanism. Differences in ozone concentrations can be attributed to the VOC treatment by the three mechanisms. The diurnal variability of pollution and ozone precursors is not captured (hourly correlation coefficients for O3 ≤ 0.29). This might be attributed to the underestimation of NOx concentrations by local emissions by up to 50 %. For the fine particulate matter (PM2.5), the lowest mean bias (9 µg m−3) is obtained with the RADM2-MADE/SORGAM mechanism, with overestimates in sulfate and ammonium aerosols. Overestimation of sulfate aerosols by this mechanism may be linked to the SO2 oxidation in clouds. The MOSAIC aerosol mechanism overestimates PM2.5 concentrations by up to 22 µg m−3 due to a more pronounced dust component compared to the other two mechanisms, mostly influenced by the dust inflow from the global model. We conclude that all three mechanisms are very sensitive to boundary conditions from the global model for both gas-phase and aerosol pollutants, in particular dust and ozone.

Urban Finescale Forecasting Reveals Weather Conditions with Unprecedented Detail

Abstract Urban landscapes impact the lives of urban dwellers by influencing local weather conditions. However, weather forecasting down to the street and neighborhood scale has been beyond the capabilities of numerical weather prediction (NWP) despite the fact that observational systems are now able to monitor urban climate at these scales. In this study, weather forecasts at intra-urban scales were achieved by exploiting recent advances in topographic element mapping and aerial photography as well as looking at detailed mappings of soil characteristics and urban morphological properties, which were subsequently incorporated into a specifically adapted Weather Research and Forecasting (WRF) Model. The urban weather forecasting system (UFS) was applied to the Amsterdam, Netherlands, metropolitan area during the summer of 2015, where it produced forecasts for the city down to the neighborhood level (a few hundred meters). Comparing these forecasts to the dense network of urban weather station observations within the Amsterdam metropolitan region showed that the forecasting system successfully determined the impact of urban morphological characteristics and urban spatial structure on local temperatures, including the cooling effect of large water bodies on local urban temperatures. The forecasting system has important practical applications for end users such as public health agencies, local governments, and energy companies. It appears that the forecasting system enables forecasts of events on a neighborhood level where human thermal comfort indices exceeded risk thresholds during warm weather episodes. These results prove that worldwide urban weather forecasting is within reach of NWP, provided that appropriate data and computing resources become available to ensure timely and efficient forecasts.

Tsunami Simulation Method Assimilating Ocean Bottom Pressure Data Near a Tsunami Source Region

A new method was developed to reproduce the tsunami height distribution in and around the source area, at a certain time, from a large number of ocean bottom pressure sensors, without information on an earthquake source. A dense cabled observation network called S-NET, which consists of 150 ocean bottom pressure sensors, was installed recently along a wide portion of the seafloor off Kanto, Tohoku, and Hokkaido in Japan. However, in the source area, the ocean bottom pressure sensors cannot observe directly an initial ocean surface displacement. Therefore, we developed the new method. The method was tested and functioned well for a synthetic tsunami from a simple rectangular fault with an ocean bottom pressure sensor network using 10 arc-min, or 20 km, intervals. For a test case that is more realistic, ocean bottom pressure sensors with 15 arc-min intervals along the north–south direction and sensors with 30 arc-min intervals along the east–west direction were used. In the test case, the method also functioned well enough to reproduce the tsunami height field in general. These results indicated that the method could be used for tsunami early warning by estimating the tsunami height field just after a great earthquake without the need for earthquake source information.

Very Short Time Range Forecasting Using CReSS-3DVAR for a Meso-γ-Scale, Localized, Extremely Heavy Rainfall Event: Comparison with an Extrapolation-Based Nowcast

The forecast accuracy of a numerical weather prediction (NWP) model for a very short time range (≤1 h) for a meso-γ-scale (2–20 km) extremely heavy rainfall (MγExHR) event that caused flooding at the Shibuya railway station in Tokyo, Japan on 24 July 2015 was compared with that of an extrapolation-based nowcast (EXT). The NWP model used CReSS with 0.7 km horizontal grid spacing, and storm-scale data from dense observation networks (radars, lidars, and microwave radiometers) were assimilated using CReSS-3DVAR. The forecast accuracy of the heavy rainfall area (≥20 mm h-1), as a function of forecast time (FT), was investigated for the NWP model and EXT predictions using the fractions skill score (FSS) for various spatial scales of displacement error (L). These predictions were started 30 minutes before the onset of extremely heavy rainfall at Shibuya station. The FSS for L=1 km, i.e., grid-scale verification, showed NWP accuracy was lower than that of EXT before FT=40 min; however, NWP accuracy surpassed that of EXT from FT=45 to 60 min. This suggests the possibility of seamless, high-accuracy forecasts of heavy rainfall (≥20 mm h-1) associated with MγExHR events within a very short time range (≤1 h) by blending EXT and NWP outputs. The factors behind the fact that the NWP model predicted heavy rainfall area within the very short time range of ≤1 h more correctly than did EXT are also discussed. To enable this discussion of the factors, additional sensitivity experiments with a different assimilation method of radar reflectivity were performed. It was found that a moisture adjustment above the lifting condensation level using radar reflectivity was critical to the forecasting of heavy rainfall near Shibuya station after 25 min.