A Study of Convection Initiation in a Mesoscale Model Using High-Resolution Land Surface Initial Conditions

The present study is carried out to investigate the performance of different cumulus convection, planetary boundary layer, land surface processes, and microphysics parameterization schemes in the simulation of a very severe cyclonic storm (VSCS) Nargis (2008), developed in the central Bay of Bengal on 27 April 2008. For this purpose, the nonhydrostatic mesoscale model (NMM) dynamic core of weather research and forecasting (WRF) system is used. Model-simulated track positions and intensity in terms of minimum central mean sea level pressure (MSLP), maximum surface wind (10 m), and precipitation are verified with observations as provided by the India Meteorological Department (IMD) and Tropical Rainfall Measurement Mission (TRMM). The estimated optimum combination is reinvestigated with six different initial conditions of the same case to have better conclusion on the performance of WRF-NMM. A few more diagnostic fields like vertical velocity, vorticity, and heat fluxes are also evaluated. The results indicate that cumulus convection play an important role in the movement of the cyclone, and PBL has a crucial role in the intensification of the storm. The combination of Simplified Arakawa Schubert (SAS) convection, Yonsei University (YSU) PBL, NMM land surface, and Ferrier microphysics parameterization schemes in WRF-NMM give better track and intensity forecast with minimum vector displacement error.

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Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation

10.20944/preprints201901.0093.v1 ◽

2019 ◽

Author(s):

Clément Albergel ◽

Emanuel Dutra ◽

Bertrand Bonan ◽

Yongjun Zheng ◽

Simon Munier ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

Western Europe ◽

Initial Conditions ◽

Added Value ◽

Area Index ◽

Long Time ◽

Land Data Assimilation System ◽

The Impact

This study aims to assess the potential of the LDAS-Monde a land data assimilation system developed by Météo-France to monitor the impact of the 2018 summer heatwave over western Europe vegetation state. The LDAS-Monde is forced by the ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). Analysis of long time series of satellite derived CGLS LAI (2000-2018) and SSM (2008-2018) highlights marked negative anomalies for July 2018 affecting large areas of North Western Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the considered domain have never been observed in the LAI product over this 18-yr period. The LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25°x0.25° (January 2008 to October 2018) and 0.10°x0.10° (April 2016 to December 2018). Both configuration of the LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5 and IFS HRES driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g. associated to a better representation of the land cover, topography) and using HRES forcing still enhance the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedbacks in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model only initial conditions.

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Understanding the Impacts of Soil Moisture Initial Conditions on NWP in the Context of Land–Atmosphere Coupling

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0186.1 ◽

2019 ◽

Vol 20 (5) ◽

pp. 793-819 ◽

Cited By ~ 12

Author(s):

Joseph A. Santanello Jr. ◽

Patricia Lawston ◽

Sujay Kumar ◽

Eli Dennis

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

Initial Conditions ◽

Land Surface Model ◽

Surface Model ◽

Coupled Models ◽

Wide Range ◽

Soil Moisture Initialization

Abstract The role of soil moisture in NWP has gained more attention in recent years, as studies have demonstrated impacts of land surface states on ambient weather from diurnal to seasonal scales. However, soil moisture initialization approaches in coupled models remain quite diverse in terms of their complexity and observational roots, while assessment using bulk forecast statistics can be simplistic and misleading. In this study, a suite of soil moisture initialization approaches is used to generate short-term coupled forecasts over the U.S. Southern Great Plains using NASA’s Land Information System (LIS) and NASA Unified WRF (NU-WRF) modeling systems. This includes a wide range of currently used initialization approaches, including soil moisture derived from “off the shelf” products such as atmospheric models and land data assimilation systems, high-resolution land surface model spinups, and satellite-based soil moisture products from SMAP. Results indicate that the spread across initialization approaches can be quite large in terms of soil moisture conditions and spatial resolution, and that SMAP performs well in terms of heterogeneity and temporal dynamics when compared against high-resolution land surface model and in situ soil moisture estimates. Case studies are analyzed using the local land–atmosphere coupling (LoCo) framework that relies on integrated assessment of soil moisture, surface flux, boundary layer, and ambient weather, with results highlighting the critical role of inherent model background biases. In addition, simultaneous assessment of land versus atmospheric initial conditions in an integrated, process-level fashion can help address the question of whether improvements in traditional NWP verification statistics are achieved for the right reasons.

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Impacts of High-Resolution Land Surface Initialization on Regional Sensible Weather Forecasts from the WRF Model

Journal of Hydrometeorology ◽

10.1175/2008jhm990.1 ◽

2008 ◽

Vol 9 (6) ◽

pp. 1249-1266 ◽

Cited By ~ 49

Author(s):

Jonathan L. Case ◽

William L. Crosson ◽

Sujay V. Kumar ◽

William M. Lapenta ◽

Christa D. Peters-Lidard

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Land Surface ◽

Initial Conditions ◽

Surface Model ◽

Surface Data ◽

Initial Soil ◽

Eta Model ◽

Data Assimilation System ◽

Assimilation System

Abstract This manuscript presents an assessment of daily regional simulations of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model initialized with high-resolution land surface data from the NASA Land Information System (LIS) software versus a control WRF configuration that uses land surface data from the National Centers for Environmental Prediction (NCEP) Eta Model. The goal of this study is to investigate the potential benefits of using the LIS software to improve land surface initialization for regional NWP. Fifty-eight individual nested simulations were integrated for 24 h for both the control and experimental (LISWRF) configurations during May 2004 over Florida and the surrounding areas: 29 initialized at 0000 UTC and 29 initialized at 1200 UTC. The land surface initial conditions for the LISWRF runs came from an offline integration of the Noah land surface model (LSM) within LIS for two years prior to the beginning of the month-long study on an identical grid domain to the subsequent WRF simulations. Atmospheric variables used to force the offline Noah LSM integration were provided by the North American Land Data Assimilation System and Global Data Assimilation System gridded analyses. The LISWRF soil states were generally cooler and drier than the NCEP Eta Model soil states during May 2004. Comparisons between the control and LISWRF runs for one event suggested that the LIS land surface initial conditions led to an improvement in the timing and evolution of a sea-breeze circulation over portions of northwestern Florida. Surface verification statistics for the entire month indicated that the LISWRF runs produced a more enhanced and accurate diurnal range in 2-m temperatures compared to the control as a result of the overall drier initial soil states, which resulted from a reduction in the nocturnal warm bias in conjunction with a reduction in the daytime cold bias. Daytime LISWRF 2-m dewpoints were correspondingly drier than the control dewpoints, again a manifestation of the drier initial soil states in LISWRF. The positive results of the LISWRF experiments help to illustrate the importance of initializing regional NWP models with high-quality land surface data generated at the same grid resolution.

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Sensitivity of Simulated Sea Breezes to Initial Conditions in Complex Coastal Regions

Monthly Weather Review ◽

10.1175/mwr-d-15-0306.1 ◽

2016 ◽

Vol 144 (4) ◽

pp. 1299-1320 ◽

Cited By ~ 10

Author(s):

Kelly Lombardo ◽

Eric Sinsky ◽

Yan Jia ◽

Michael M. Whitney ◽

James Edson

Keyword(s):

New England ◽

Land Surface ◽

Mesoscale Model ◽

Initial Conditions ◽

Sea Breeze ◽

Horizontal Resolution ◽

Sea Surface ◽

Initial Surface ◽

Sea Breezes ◽

Mesoscale Simulations

Abstract Mesoscale simulations of sea breezes are sensitive to the analysis product used to initialize the simulations, primarily due to the representation of the coastline and the coastal sea surface temperatures (SSTs) in the analyses. The use of spatially coarse initial conditions, relative to the horizontal resolution of the mesoscale model grid, can introduce errors in the representation of coastal SSTs, in part due to the incorrect designation of the land surface. As a result, portions of the coastal ocean are initialized with land surface temperature values and vice versa. The diurnal variation of the sea surface is typically smaller than over land on meso- and synoptic-scale time scales. Therefore, it is common practice to retain a temporally static SST in numerical simulations, causing initial SST errors to persist through the duration of the simulation. These SST errors influence horizontal coastal temperature and humidity gradients and thereby the development of the sea-breeze circulations. The authors developed a technique to modify the initial surface conditions created from a reanalysis product [North American Regional Reanalysis (NARR)] for simulations of two sea-breeze events over the New England coast to more accurately represent the finescale structure of the coastline and the spatial representation of the coastal land surface and SST. Using this technique, the coastal SST (2-m temperature) RMSE is reduced from as much as 25°–1°C (7°–1°C), contributing to a more accurate propagation of the sea-breeze front. Techniques described in this work may be important for mesoscale simulations and forecasts of other coastal phenomena.

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The Use of Similarity Concepts to Represent Subgrid Variability in Land Surface Models: Case Study in a Snowmelt-Dominated Watershed

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-038.1 ◽

2014 ◽

Vol 15 (5) ◽

pp. 1717-1738 ◽

Cited By ~ 21

Author(s):

Andrew J. Newman ◽

Martyn P. Clark ◽

Adam Winstral ◽

Danny Marks ◽

Mark Seyfried

Keyword(s):

High Resolution ◽

Land Surface ◽

Mesoscale Model ◽

A Priori ◽

Land Surface Models ◽

Creek Watershed ◽

Surface Models ◽

Reynolds Creek ◽

Better Than

Abstract This paper develops a multivariate mosaic subgrid approach to represent subgrid variability in land surface models (LSMs). The k-means clustering is used to take an arbitrary number of input descriptors and objectively determine areas of similarity within a catchment or mesoscale model grid box. Two different classifications of hydrologic similarity are compared: an a priori classification, where clusters are based solely on known physiographic information, and an a posteriori classification, where clusters are defined based on high-resolution LSM simulations. Simulations from these clustering approaches are compared to high-resolution gridded simulations, as well as to three common mosaic approaches used in LSMs: the “lumped” approach (no subgrid variability), disaggregation by elevation bands, and disaggregation by vegetation types in two subcatchments. All watershed disaggregation methods are incorporated in the Noah Multi-Physics (Noah-MP) LSM and applied to snowmelt-dominated subcatchments within the Reynolds Creek watershed in Idaho. Results demonstrate that the a priori clustering method is able to capture the aggregate impact of finescale spatial variability with O(10) simulation points, which is practical for implementation into an LSM scheme for coupled predictions on continental–global scales. The multivariate a priori approach better represents snow cover and depth variability than the univariate mosaic approaches, critical in snowmelt-dominated areas. Catchment-averaged energy fluxes are generally within 10%–15% for the high-resolution and a priori simulations, while displaying more subgrid variability than the univariate mosaic methods. Examination of observed and simulated streamflow time series shows that the a priori method generally reproduces hydrograph characteristics better than the simple disaggregation approaches.

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Developing an operational high-resolution hydrometeorological system in a Mediterranean region: predictability analysis of two case studies

10.5194/egusphere-egu2020-14140 ◽

2020 ◽

Author(s):

Luca Furnari ◽

Alfonso Senatore ◽

Linus Magnusson ◽

Giuseppe Mendicino

Keyword(s):

High Resolution ◽

Weather Forecasting ◽

Mesoscale Model ◽

Initial Conditions ◽

Horizontal Resolution ◽

Lead Times ◽

Synoptic Conditions ◽

Hydrological Impact ◽

Forecasting System ◽

Modelling System

Given the expected increase in the frequency and intensity of severe weather events due to global warming, improving weather forecasting capability in terms of both spatial resolution and lead times is a key factor for reducing extreme events impact. The climate of the Calabrian peninsula (southern Italy) is dominated by the interactions of the air masses with the surrounding Mediterranean Sea and strongly influenced by its complex steep orography, which often amplifies precipitation amounts worsening ground effects. With the aim of investigating the capability of a state-of-the-art modelling chain to deliver accurate forecasts for civil protection purposes in the Calabria Region, an experimental high-resolution hydrometeorological modelling system has been developed recently at the Department of Environmental Engineering of the University of Calabria, providing forecasts up to the hydrological impact. The system is based on the Advanced Research WRF (ARW) mesoscale model in its version 3.9.1, with two one-way nested domains, the innermost having 2-km resolution. The boundary and initial conditions are provided operationally by the Global Forecasting System (GFS) in its high-resolution version and, for back-analysis purposes, by the European Centre for Medium-range Weather Forecasts&#8217; Integrated Forecasting System (IFS). Finally, to simulate the hydrological impact of the atmospheric forcing, the WRF-Hydro 5.0 modelling system in a one-way mode with a horizontal resolution of 200 m is linked to the system and applied on all the main river networks of the region.The accuracy and efficiency of the system have been tested with two events occurred in Autumn 2019. Though the synoptic conditions showed some significant differences, both the events affected mainly the central part of the region, causing about 230 mm and 200 mm of rainfall in 72 hours, on the 11-13 November 2019 and on the 24-26 November 2019, respectively. The analysis focused particularly on the predictability of the events, evaluating the forecast accuracy by considering lead times from one week early.Preliminary results highlight the ability to forecasts the events well in advance, proved by the comparison of the simulated rainfall with the ground-based observations and the reproduction of the main hydrological signals in the basins affected by the events.

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Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation

Remote Sensing ◽

10.3390/rs11050520 ◽

2019 ◽

Vol 11 (5) ◽

pp. 520 ◽

Cited By ~ 19

Author(s):

Clément Albergel ◽

Emanuel Dutra ◽

Bertrand Bonan ◽

Yongjun Zheng ◽

Simon Munier ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

Western Europe ◽

Initial Conditions ◽

Added Value ◽

Area Index ◽

Long Time ◽

Land Data Assimilation System ◽

The Impact

This study aims to assess the potential of the LDAS-Monde platform, a land data assimilation system developed by Météo-France, to monitor the impact on vegetation state of the 2018 summer heatwave over Western Europe. The LDAS-Monde is driven by ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite-derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). The study of long time series of satellite derived CGLS LAI (2000–2018) and SSM (2008–2018) highlights marked negative anomalies for July 2018 affecting large areas of northwestern Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the domain of interest have never been observed in the LAI product over this 19-year period. LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25° × 0.25° (January 2008 to October 2018) and 0.10° × 0.10° (April 2016 to December 2018). Both configurations of LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5- and IFS HRES-driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g., associated to a better representation of the land cover, topography) and using HRES forcing still enhances the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedback in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model-only initial conditions.

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Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

Remote Sensing ◽

10.3390/rs13010113 ◽

2020 ◽

Vol 13 (1) ◽

pp. 113

Author(s):

Antonio-Juan Collados-Lara ◽

Steven R. Fassnacht ◽

Eulogio Pardo-Igúzquiza ◽

David Pulido-Velazquez

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Air Temperature ◽

Land Surface ◽

National Park ◽

Rocky Mountain ◽

Rocky Mountain National Park ◽

Temperature Fields ◽

Lapse Rate ◽

Validation Experiment

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

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High-Resolution Monitoring and Assessment of Evapotranspiration and Gross Primary Production Using Remote Sensing in a Typical Arid Region

Land ◽

10.3390/land10040396 ◽

2021 ◽

Vol 10 (4) ◽

pp. 396

Author(s):

Junxia Yan ◽

Yanfei Ma ◽

Dongyun Zhang ◽

Zechen Li ◽

Weike Zhang ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Land Surface ◽

Arid Region ◽

Management Practices ◽

Gross Primary Production ◽

Irrigation District ◽

Heihe River ◽

Freshwater Resource ◽

Oasis Area

Land surface evapotranspiration (ET) and gross primary productivity (GPP) are critical components in terrestrial ecosystems with water and carbon cycles. Large-scale, high-resolution, and accurately quantified ET and GPP values are important fundamental data for freshwater resource management and help in understanding terrestrial carbon and water cycles in an arid region. In this study, the revised surface energy balance system (SEBS) model and MOD17 GPP algorithm were used to estimate daily ET and GPP at 100 m resolution based on multi-source satellite remote sensing data to obtain surface biophysical parameters and meteorological forcing data as input variables for the model in the midstream oasis area of the Heihe River Basin (HRB) from 2010 to 2016. Then, we further calculated the ecosystem water-use efficiency (WUE). We validated the daily ET, GPP, and WUE from ground observations at a crop oasis station and conducted spatial intercomparisons of monthly and annual ET, GPP, and WUE at the irrigation district and cropland oasis scales. The site-level evaluation results show that ET and GPP had better performance than WUE at the daily time scale. Specifically, the deviations in the daily ET, GPP, and WUE data compared with ground observations were small, with a root mean square error (RMSE) and mean absolute percent error (MAPE) of 0.75 mm/day and 26.59%, 1.13 gC/m2 and 36.62%, and 0.50 gC/kgH2O and 39.83%, respectively. The regional annual ET, GPP, and WUE varied from 300 to 700 mm, 200 to 650 gC/m2, and 0.5 to 1.0 gC/kgH2O, respectively, over the entire irrigation oasis area. It was found that annual ET and GPP were greater than 550 mm and 500 gC/m2, and annual oasis cropland WUE had strong invariability and was maintained at approximately 0.85 gC/kgH2O. The spatial intercomparisons from 2010 to 2016 revealed that ET had similar spatial patterns to GPP due to tightly coupled carbon and water fluxes. However, the WUE spatiotemporal patterns were slightly different from both ET and GPP, particularly in the early and late growing seasons for the oasis area. Our results demonstrate that spatial full coverage and reasonably fine spatiotemporal variation and variability could significantly improve our understanding of water-saving irrigation strategies and oasis agricultural water management practices in the face of water shortage issues.

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