scholarly journals The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes

2011 ◽  
Vol 4 (3) ◽  
pp. 677-699 ◽  
Author(s):  
M. J. Best ◽  
M. Pryor ◽  
D. B. Clark ◽  
G. G. Rooney ◽  
R .L. H. Essery ◽  
...  
Keyword(s):  
Land Surface ◽  
Weather Forecasting ◽  
Land Surface Model ◽  
Circulation Model ◽  
Model Description ◽  
Surface Model ◽  
Global Circulation Model ◽  
Surface Exchange ◽  
Model Driven ◽  
New Community

Abstract. This manuscript describes the energy and water components of a new community land surface model called the Joint UK Land Environment Simulator (JULES). This is developed from the Met Office Surface Exchange Scheme (MOSES). It can be used as a stand alone land surface model driven by observed forcing data, or coupled to an atmospheric global circulation model. The JULES model has been coupled to the Met Office Unified Model (UM) and as such provides a unique opportunity for the research community to contribute their research to improve both world-leading operational weather forecasting and climate change prediction systems. In addition JULES, and its forerunner MOSES, have been the basis for a number of very high-profile papers concerning the land-surface and climate over the last decade. JULES has a modular structure aligned to physical processes, providing the basis for a flexible modelling platform.

scholarly journals The Joint UK Land Environment Simulator (JULES), Model description – Part 1: Energy and water fluxes

2011 ◽  
Vol 4 (1) ◽  
pp. 595-640 ◽  
Author(s):  
M. J. Best ◽  
M. Pryor ◽  
D. B. Clark ◽  
G. G. Rooney ◽  
R. L. H. Essery ◽  
...  
Keyword(s):  
Land Surface ◽  
Weather Forecasting ◽  
Land Surface Model ◽  
Circulation Model ◽  
Model Description ◽  
Surface Model ◽  
Global Circulation Model ◽  
Surface Exchange ◽  
Model Driven ◽  
New Community

Abstract. This manuscript describes the energy and water components of a new community land surface model called the Joint UK Land Environment Simulator (JULES). This is developed from the Met Office Surface Exchange Scheme (MOSES). It can be used as a stand alone land surface model driven by observed forcing data, or coupled to an atmospheric global circulation model. The JULES model has been coupled to the Met Office Unified Model (UM) and as such provides an opportunity for the research community to contribute their research into world-leading operational weather forecasting and climate change prediction systems. JULES has a modular structure aligned to physical processes, providing the basis for a flexible modelling platform.

Development of stochastically perturbed parameterization scheme for the Noah Land Surface Model with the optimized random forcing parameters using the micro-genetic algorithm

2021 ◽  
Author(s):  
Sujeong Lim ◽  
Claudio Cassardo ◽  
Seon Ki Park
Keyword(s):  
Genetic Algorithm ◽  
Land Surface ◽  
Weather Forecasting ◽  
Land Surface Model ◽  
Surface Model ◽  
Ensemble Spread ◽  
Near Surface ◽  
Random Forcing ◽  
Micro Genetic Algorithm ◽  
Stochastically Perturbed

<p>The ensemble data assimilation system is beneficial to represent the initial uncertainties and flow-dependent background error covariance (BEC). In particular, the inevitable model uncertainties can be expressed by ensemble spread, that is the standard deviation of ensemble BEC. However, the ensemble spread generally suffers from under-estimated problems. To alleviate this problem, recent studies employed stochastic perturbation schemes to increases the ensemble spreads by adding the random forcing in the model tendencies (i.e., physical or dynamical tendencies) or parameterization schemes (i.e., PBL, convective scheme, etc.). In this study, we focus on the near-surface uncertainties which are affected by the interactions between the land and atmosphere process. The land surface model (LSM) provides various fluxes as the lower boundary condition to the atmosphere, influencing the accuracy of hourly-to-seasonal scale weather forecasting, but the surface uncertainties were not much addressed yet. In this study, we developed the stochastically perturbed parameterization (SPP) scheme for the Noah LSM. The Weather Research and Forecasting (WRF) ensemble system is used for regional weather forecasting over East Asia, especially over the Korean Peninsula. As a testbed experiment with the newly-developed Noah LSM-SPP system, we first perturbed the soil temperature — a crucial variable for the near-surface forecasts by affecting sensible heat fluxes, land surface skin temperature and surface air temperature, and hence lower-tropospheric temperature. Here, the random forcing used in perturbation is made by the tuning parameters for amplitude, length scale, and time scales: they are commonly determined empirically by trial and error. In order to find optimal tuning parameter values, we applied a global optimization algorithm — the micro-genetic algorithm (micro-GA) — to achieve the smallest root-mean-squared errors. Our results indicate that optimization of the random forcing parameters contributes to an increase in the ensemble spread and a decrease in the ensemble mean errors in the near-surface and lower-troposphere uncertainties. Further experiments will be conducted by including soil moisture in the testbed.</p>

Preliminary Results of Simulation of SOCRATES period with Global System for Atmospheric Modeling

2020 ◽  
Author(s):  
Marat Khairoutdinov ◽  
Christopher Bretherton
Keyword(s):  
Land Surface ◽  
Weather Forecasting ◽  
Storm Track ◽  
Land Surface Model ◽  
Cloud Microphysics ◽  
Horizontal Resolution ◽  
Atmospheric Modeling ◽  
Surface Model ◽  
Grid Spacing ◽  
Reanalysis Dataset

<p>The global version of the cloud-resolving System for Atmospheric Modeling (SAM) is used to simulate the global evolution of clouds and precipitation during the SOCRATES field campaign In Feb 2018 with particular focus on the Southern Ocean storm track region. The model has nonuniform horizontal resolution, which ranges from 4-km horizontal grid spacing over the Tropics up to 2-3 km isotropic grid-spacing over mid-latitudes. It includes a realistic topography and comprehensive land-surface model. The sea-surface temperature and sea ice are prescribed from observations. The results of two types of simulations are presented, weather-forecasting and observed-weather-nudged over 24-hour time scale; for the latter, hourly ERA5 reanalysis dataset is used. The cloud properties are compared to the SOCRATES observations. The sensitivity of the results to the choice of cloud microphysics, from simple single-moment to double-moment, is also discussed.</p>

scholarly journals Technical Note: Evaluation of the skill in monthly-to-seasonal soil moisture forecasting based on SMAP satellite observations over the southeastern US

2020 ◽  
Vol 24 (3) ◽  
pp. 1073-1079
Author(s):  
Amirhossein Mazrooei ◽  
Arumugam Sankarasubramanian ◽  
Venkat Lakshmi
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Weather Forecasting ◽  
Land Surface Model ◽  
Technical Note ◽  
Surface Model ◽  
Lead Times ◽  
Southeastern Us ◽  
Agricultural Planning ◽  
Model Initialization

Abstract. Providing accurate soil moisture (SM) conditions is a critical step in model initialization in weather forecasting, agricultural planning, and water resources management. This study develops monthly-to-seasonal (M2S) top layer SM forecasts by forcing 1- to 3-month-ahead precipitation forecasts with Noah3.2 Land Surface Model. The SM forecasts are developed over the southeastern US (SEUS), and the SM forecasting skill is evaluated in comparison with the remotely sensed SM observations collected by the Soil Moisture Active Passive (SMAP) satellite. Our results indicate potential in developing real-time SM forecasts. The retrospective 18-month (April 2015–September 2016) comparison between SM forecasts and the SMAP observations shows statistically significant correlations of 0.62, 0.57, and 0.58 over 1-, 2-, and 3-month lead times respectively.

scholarly journals Implementing northern peatlands in a global land surface model: description and evaluation in the ORCHIDEE high latitude version model (ORC-HL-PEAT)

2017 ◽  
Author(s):  
Chloé Largeron ◽  
Gerhard Krinner ◽  
Philippe Ciais ◽  
Claire Brutel-Vuilmet
Keyword(s):  
Land Surface ◽  
Peat Soil ◽  
Water Storage ◽  
Land Surface Model ◽  
Model Description ◽  
High Water Content ◽  
Surface Model ◽  
Northern Latitudes ◽  
Global Land ◽  
Northern Peatlands

Abstract. Widely present in boreal regions, peatlands contain large carbon stocks because of their hydrologic properties and high water content, making decomposition smaller than primary productivity. We have enhanced the global land surface model ORCHIDEE by introducing northern peatlands. These are considered as a new Plant Functional Types (PFT) in the model, with specific hydrological properties for peat soil. In this paper, we focus on the representation of the hydrology of northern peatlands and on the evaluation of the hydrological impact of this implementation. A prescribed map based on the inventory of Yu et al. (2010) defines peatlands as a fraction of grid cell represented as a PFT comparable to C3 grasses with adaptations to reproduce shallow roots and higher photosynthesis stress. The treatment of peatland hydrology differs from that of other vegetation types by the fact that runoff from other soil types is partially directed towards the peatlands (instead of directly to the river network). The evaluation of this implementation was carried out according to different spatial and temporal scales, from site evaluation to larger scales such as watershed scale and all northern latitudes scale. The simulated net ecosystem exchanges are in agreement with observations from 3 FLUXNET sites. Water table positions were generally close to observations, with some exceptions in winter. Compared to other soils, the simulated peat soil have a reduced seasonal variability of water storage. The seasonal cycle of inundated peatlands area is also compared to flooded area estimated from satellite observations. The model is able to represents more than 89.5 % of the flooded areas located in peatlands areas, where the modelled extent of inundated peatlands reaches 0.83 Mkm2. However, the extent of peatland in northern latitudes is small enough that is does not impact the terrestrial water storage at scale of latitudes over 45° N. Therefore, the inclusion of peatlands has a weak impact on the river discharge located in boreal regions.

scholarly journals Advancing land surface model development with satellite-based Earth observations

2017 ◽  
Vol 21 (5) ◽  
pp. 2483-2495 ◽  
Author(s):  
Rene Orth ◽  
Emanuel Dutra ◽  
Isabel F. Trigo ◽  
Gianpaolo Balsamo
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Weather Forecasting ◽  
Model Performance ◽  
Land Surface Model ◽  
Climate System ◽  
Surface Model ◽  
Earth Observations ◽  
The Impact

Abstract. The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts, we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability, and understanding of climate system feedbacks.

Sign in / Sign up

Export Citation Format

Share Document