scholarly journals A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 1: Offline forward and adjoint transport models

2017 ◽  
Vol 10 (3) ◽  
pp. 1157-1174 ◽  
Author(s):  
Yosuke Niwa ◽  
Hirofumi Tomita ◽  
Masaki Satoh ◽  
Ryoichi Imasu ◽  
Yousuke Sawa ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Meteorological Data ◽  
Forward Model ◽  
High Temporal Resolution ◽  
Tracer Transport ◽  
Adjoint Model ◽  
Transport Models ◽  
Flux Limiter ◽  
Adjoint Transport ◽  
Continuous Adjoint

Abstract. A four-dimensional variational (4D-Var) method is a popular algorithm for inverting atmospheric greenhouse gas (GHG) measurements. In order to meet the computationally intense 4D-Var iterative calculation, offline forward and adjoint transport models are developed based on the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). By introducing flexibility into the temporal resolution of the input meteorological data, the forward model developed in this study is not only computationally efficient, it is also found to nearly match the transport performance of the online model. In a transport simulation of atmospheric carbon dioxide (CO2), the data-thinning error (error resulting from reduction in the time resolution of the meteorological data used to drive the offline transport model) is minimized by employing high temporal resolution data of the vertical diffusion coefficient; with a low 6-hourly temporal resolution, significant concentration biases near the surface are introduced. The new adjoint model can be run in discrete or continuous adjoint mode for the advection process. The discrete adjoint is characterized by perfect adjoint relationship with the forward model that switches off the flux limiter, while the continuous adjoint is characterized by an imperfect but reasonable adjoint relationship with its corresponding forward model. In the latter case, both the forward and adjoint models use the flux limiter to ensure the monotonicity of tracer concentrations and sensitivities. Trajectory analysis for high CO2 concentration events are performed to test adjoint sensitivities. We also demonstrate the potential usefulness of our adjoint model for diagnosing tracer transport. Both the offline forward and adjoint models have computational efficiency about 10 times higher than the online model. A description of our new 4D-Var system that includes an optimization method, along with its application in an atmospheric CO2 inversion and the effects of using either the discrete or continuous adjoint method, is presented in an accompanying paper Niwa et al.(2016).

scholarly journals A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0): 1. Off-line forward and adjoint transport models

2016 ◽  
Author(s):  
Yosuke Niwa ◽  
Hirofumi Tomita ◽  
Masaki Satoh ◽  
Ryoichi Imasu ◽  
Yousuke Sawa ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Meteorological Data ◽  
Forward Model ◽  
Adjoint Model ◽  
Transport Models ◽  
Line Model ◽  
Flux Limiter ◽  
On Line ◽  
Adjoint Transport ◽  
Continuous Adjoint

Abstract. A 4-dimensional variational (4D-Var) method is a popular algorithm for inverting atmospheric greenhouse gas (GHG) measurements. In order to meet the computationally intense 4D-Var iterative calculation, off-line forward and adjoint transport models are developed based on the Nonhydorstatic ICosahedral Atmospheric Model (NICAM). By introducing flexibility into the temporal resolution of the input meteorological data, the forward model developed in this study is not only computationally efficient but is also found to nearly match the transport performance of the on-line model. In a transport simulation of atmospheric carbon dioxide (CO2), the data-thinning error (error resulting from reduction in the time resolution of the meteorological data used to drive the off-line transport model) is minimized by employing high temporal resolution data of the vertical diffusion coefficient; with a lower temporal resolution, significant concentration biases near the surface are introduced. The new adjoint model can be run in discrete or continuous adjoint mode for the advection process. The discrete adjoint is characterized by perfect adjoint relationship with the forward model that switches off the flux limiter, while the continuous adjoint is characterized by imperfect but reasonable adjoint relationship with its corresponding forward model. In the latter case, both the forward and adjoint models use the flux limiter to ensure the monotonicity of tracer concentrations and sensitivities. Trajectory analysis for high-CO2 concentration events are performed to test adjoint sensitivities; we also demonstrate the potential usefulness of our adjoint model for diagnosing tracer transport. Both the off-line forward and adjoint models have computational efficiency about ten times more than that of the on-line model. A description of our new 4D-Var system that includes an optimization method, along with its application in an atmospheric CO2 inversion and the effects of using either the discrete or continuous adjoint method, is presented in an accompanying paper (Niwa et al., 2016).

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

scholarly journals Development of a variational flux inversion system (INVICAT v1.0) within the TOMCAT chemical transport model

2013 ◽  
Vol 6 (4) ◽  
pp. 7117-7159 ◽  
Author(s):  
C. Wilson ◽  
M. P. Chipperfield ◽  
M. Gloor ◽  
F. Chevallier
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Chemical Transport ◽  
Forward Model ◽  
Chemical Transport Model ◽  
Adjoint Model ◽  
Inverse Models ◽  
Adjoint Transport ◽  
Inverse Transport ◽  
Atmospheric Trace Gas

Abstract. We present a new variational inverse transport model, named INVICAT (v1.0), which is based upon the global chemical transport model TOMCAT, and a new corresponding adjoint transport model, ATOMCAT. The adjoint model is constructed through manually derived discrete adjoint algorithms, and includes subroutines governing advection, convection and boundary layer mixing. We present extensive testing of the adjoint and inverse models, and also thoroughly assess the accuracy of the TOMCAT forward model's representation of atmospheric transport through comparison with observations of the atmospheric trace gas SF6. The forward model is shown to perform well in comparison with these observations, capturing the latitudinal gradient and seasonal cycle of SF6 to within acceptable tolerances. The adjoint model is shown, through numerical identity tests and novel transport reciprocity tests, to be extremely accurate in comparison with the forward model, with no error shown at the level of accuracy possible with our machines. The potential for the variational system as a tool for inverse modelling is investigated through an idealised test using simulated observations, and the system demonstrates an ability to retrieve known fluxes from a perturbed state accurately. Using basic off-line chemistry schemes, the inverse model is ready and available to perform inversions of trace gases with relatively simple chemical interactions, including CH4, CO2 and CO.

scholarly journals Vertical mixing in atmospheric tracer transport models: error characterization and propagation

2008 ◽  
Vol 8 (3) ◽  
pp. 591-602 ◽  
Author(s):  
C. Gerbig ◽  
S. Körner ◽  
J. C. Lin
Keyword(s):  
Meteorological Data ◽  
Large Fraction ◽  
Vertical Mixing ◽  
Sampling Strategy ◽  
Mixing Ratio ◽  
Random Errors ◽  
Tracer Transport ◽  
Transport Models ◽  
Error Characterization ◽  
Pseudo Data

Abstract. Imperfect representation of vertical mixing near the surface in atmospheric transport models leads to uncertainties in modelled tracer mixing ratios. When using the atmosphere as an integrator to derive surface-atmosphere exchange from mixing ratio observations made in the atmospheric boundary layer, this uncertainty has to be quantified and taken into account. A comparison between radiosonde-derived mixing heights and mixing heights derived from ECMWF meteorological data during May–June 2005 in Europe revealed random discrepancies of about 40% for the daytime with insignificant bias errors, and much larger values approaching 100% for nocturnal mixing layers with bias errors also exceeding 50%. The Stochastic Time Inverted Lagrangian Transport (STILT) model was used to propagate this uncertainty into CO2 mixing ratio uncertainties, accounting for spatial and temporal error covariance. Average values of 3 ppm were found for the 2 month period, indicating that this represents a large fraction of the overall uncertainty. A pseudo data experiment shows that the error propagation with STILT avoids biases in flux retrievals when applied in inversions. The results indicate that flux inversions employing transport models based on current generation meteorological products have misrepresented an important part of the model error structure likely leading to biases in the estimated mean and uncertainties. We strongly recommend including the solution presented in this work: better, higher resolution atmospheric models, a proper description of correlated random errors, and a modification of the overall sampling strategy.

scholarly journals High temporal resolution hydrometeorological data collected in the tropical Cordillera Blanca, Peru (2004–2020)

2021 ◽  
Author(s):  
Emilio I. Mateo ◽  
Bryan G. Mark ◽  
Robert Å. Hellström ◽  
Michel Baraer ◽  
Jeffrey M. McKenzie ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Meteorological Data ◽  
Elevation Gradient ◽  
High Temporal Resolution ◽  
High Mountain ◽  
Cordillera Blanca ◽  
Local Manifestation ◽  
Daily Discharge ◽  
Weather Stations ◽  
Meteorological Observations

Abstract. This article provides a comprehensive hydrometeorological dataset collected over the past two decades throughout the Cordillera Blanca, Peru. The data recording sites, located in the upper portion of the Rio Santa valley, also known as the Callejon de Huaylas, span an elevation range of 3738–4750 m a.s.l. As many historical hydrological stations measuring daily discharge across the region became defunct after their installation in the 1950s, there was a need for new stations to be installed and an opportunity to increase the temporal resolution of the streamflow observations. Through inter-institutional collaboration the hydrometeorological network described in this paper was deployed with goals to evaluate how progressive glacier mass loss was impacting stream hydrology, and to better understand the local manifestation of climate change over diurnal to seasonal and interannual time scales. The four automatic weather stations supply detailed meteorological observations, and are situated in a variety of mountain landscapes, with one on a high-mountain pass, another next to a glacial lake, and two in glacially carved valleys. Four additional temperature and relative humidity loggers complement the weather stations within the Llanganuco valley by providing these data across an elevation gradient. The six streamflow gauges are located in tributaries to the Rio Santa and collect high temporal resolution runoff data. The datasets presented here are available freely from https://doi.org/10.4211/hs.059794371790407abd749576df8fd121 (Mateo et al., 2021). Combined, the hydrological and meteorological data collected throughout the Cordillera Blanca enable detailed research of atmospheric and hydrological processes in tropical high-mountain terrain.

Localised human thermal discomfort assessment using high temporal resolution meteorological data: A case of University of Zimbabwe

2019 ◽  
Vol 110 ◽  
pp. 138-148 ◽  
Author(s):  
Terence Darlington Mushore ◽  
Brandon Chimuti ◽  
Juliet Gwenzi ◽  
Moven Manjowe ◽  
Collen Mutasa ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Thermal Discomfort ◽  
University Of Zimbabwe

scholarly journals Development of a variational flux inversion system (INVICAT v1.0) using the TOMCAT chemical transport model

2014 ◽  
Vol 7 (5) ◽  
pp. 2485-2500 ◽  
Author(s):  
C. Wilson ◽  
M. P. Chipperfield ◽  
M. Gloor ◽  
F. Chevallier
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Chemical Transport ◽  
Forward Model ◽  
Chemical Transport Model ◽  
Adjoint Model ◽  
Inverse Models ◽  
Adjoint Transport ◽  
Inverse Transport ◽  
Atmospheric Trace Gas

Abstract. We present a new variational inverse transport model, named INVICAT (v1.0), which is based on the global chemical transport model TOMCAT, and a new corresponding adjoint transport model, ATOMCAT. The adjoint model is constructed through manually derived discrete adjoint algorithms, and includes subroutines governing advection, convection and boundary layer mixing, all of which are linear in the TOMCAT model. We present extensive testing of the adjoint and inverse models, and also thoroughly assess the accuracy of the TOMCAT forward model's representation of atmospheric transport through comparison with observations of the atmospheric trace gas SF6. The forward model is shown to perform well in comparison with these observations, capturing the latitudinal gradient and seasonal cycle of SF6 to within acceptable tolerances. The adjoint model is shown, through numerical identity tests and novel transport reciprocity tests, to be extremely accurate in comparison with the forward model, with no error shown at the level of accuracy possible with our machines. The potential for the variational system as a tool for inverse modelling is investigated through an idealised test using simulated observations, and the system demonstrates an ability to retrieve known fluxes from a perturbed state accurately. Using basic off-line chemistry schemes, the inverse model is ready and available to perform inversions of trace gases with relatively simple chemical interactions, including CH4, CO2 and CO.

scholarly journals Climatology and time series of surface meteorology in Ny-Ålesund, Svalbard

2013 ◽  
Vol 5 (1) ◽  
pp. 155-163 ◽  
Author(s):  
M. Maturilli ◽  
A. Herber ◽  
G. König-Langlo
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
The Arctic ◽  
High Temporal Resolution ◽  
Mean Values ◽  
Time Period ◽  
Synoptic Observations ◽  
Arctic Change ◽  
Water Vapor Mixing Ratio

Abstract. A consistent meteorological dataset of the Arctic site Ny-Ålesund (11.9° E, 78.9° N) spanning the 18 yr-period 1 August 1993 to 31 July 2011 is presented. Instrumentation and data handling of temperature, humidity, wind and pressure measurements are described in detail. Monthly mean values are shown for all years to illustrate the interannual variability of the different parameters. Climatological mean values are given for temperature, humidity and pressure. From the climatological dataset, we also present the time series of annual mean temperature and humidity, revealing a temperature increase of +1.35 K per decade and an increase in water vapor mixing ratio of +0.22 g kg−1 per decade for the given time period, respectively. With the continuation of the presented measurements, the Ny-Ålesund high resolution time series will provide a reliable source to monitor Arctic change and retrieve trends in the future. The relevant data are provided in high temporal resolution as averages over 5 (1) min before (after) 14 July 1998, respectively, placed on the PANGAEA repository (doi:10.1594/PANGAEA.793046). While 6 hourly synoptic observations in Ny-Ålesund by the Norwegian Meteorological Institute reach back to 1974 (Førland et al., 2011), the meteorological data presented here cover a shorter time period, but their high temporal resolution will be of value for atmospheric process studies on shorter time scales.

scholarly journals Vertical mixing in atmospheric tracer transport models: error characterization and propagation

2007 ◽  
Vol 7 (5) ◽  
pp. 13121-13150 ◽  
Author(s):  
C. Gerbig ◽  
S. Körner ◽  
J. C. Lin
Keyword(s):  
Mixed Layer ◽  
Meteorological Data ◽  
Large Fraction ◽  
Vertical Mixing ◽  
Sampling Strategy ◽  
Mixing Ratio ◽  
Tracer Transport ◽  
Transport Models ◽  
Error Characterization ◽  
Pseudo Data

Abstract. Imperfect representation of vertical mixing near the surface in atmospheric transport models leads to uncertainties in modelled tracer mixing ratios. When using the atmosphere as an integrator to derive surface-atmosphere exchange from mixing ratio observations made in the atmospheric boundary layer, this uncertainty has to be quantified and taken into account. A comparison between radiosonde-derived mixed layer heights and mixed layer heights derived from ECMWF meteorological data during May–June 2005 in Europe revealed random discrepancies of about 40% for the daytime with insignificant bias errors, and much larger values approaching 100% for nocturnal mixed layers with bias errors also exceeding 50%. The Stochastic Time Inverted Lagrangian Transport (STILT) model was used to propagate this uncertainty into CO2 mixing ratio uncertainties, accounting for spatial and temporal error covariance. Average values of 3 ppm were found for the 2 month period, indicating that this represents a large fraction of the overall uncertainty. A pseudo data experiment shows that the error propagation with STILT avoids biases in flux retrievals when applied in inversions. The results indicate that transport models driven by current generation data assimilation for meteorological fields is by far not sufficient for inversions of continental mixing ratio data. As a solution we suggest the use of better, higher resolution atmospheric models, and a modification of the overall sampling strategy.

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