scholarly journals Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1

2021 ◽  
Author(s):  
Santos J. González-Rojí ◽  
Martina Messmer ◽  
Christoph C. Raible ◽  
Thomas F. Stocker
Keyword(s):  
Observational Data ◽  
Amazon Basin ◽  
Cumulus Parameterization ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Microphysics Scheme ◽  
The Andes ◽  
Station Data ◽  
Pattern Correlation ◽  
Simulated Precipitation

Abstract. The performance of the Weather Research and Forecasting (WRF) model version 3.8.1 at convection-permitting scale is evaluated by means of several sensitivity simulations over southern Peru down to a grid resolution of 1 km, whereby the main focus is on the domain with 5 km horizontal resolution. Different configurations of microphysics, cumulus, longwave radiation and planetary boundary layer schemes are tested. For the year 2008, the simulated precipitation amounts and patterns are compared to gridded observational data sets and weather station data gathered from Peru, Bolivia and Brazil. The temporal correlation of simulated monthly precipitation sums against in-situ and gridded observational data show that the most challenging regions for WRF are the slopes along both sides of the Andes, i.e., elevations between 1000 and 3000 m above sea level. The pattern correlation analysis between simulated precipitation and station data suggests that all tested WRF setups perform rather poorly along the northeastern slopes of the Andes during the entire year. In the southwestern region of the domain the performance of all setups is better except for the driest period (May–September). The results of the pattern correlation to the gridded observational data sets show that all setups perform reasonably well except along both slopes during the dry season. The precipitation patterns reveal that the typical setup used over Europe is too dry throughout the entire year, and that the experiment with the combination of the single-moment 6-class microphysics scheme and the Grell–Freitas cumulus parameterization in the domains with resolutions larger than 5 km, suitable for East Africa, does not perfectly apply to other equatorial regions such as the Amazon basin in southeastern Peru. The experiment with the Stony–Brook University microphysics scheme and the Grell-Freitas cumulus parameterization tends to overestimate precipitation over the northeastern slopes of the Andes, but allows to enforce a positive feedback between the soil moisture, air temperature, relative humidity, mid-level cloud cover and finally, also precipitation. Hence, this setup is the one providing the most accurate results over the Peruvian Amazon, and particularly over the department of Madre de Dios, which is a region of interest because it is considered the biodiversity hotspot of Peru. The robustness of this particular parameterization option is backed up by similar results obtained during wet climate conditions observed in 2012.

scholarly journals Sensitivity of precipitation and temperature over the Mount Kenya area to physics parameterization options in a high-resolution model simulation performed with WRFV3.8.1

2021 ◽  
Vol 14 (5) ◽  
pp. 2691-2711
Author(s):  
Martina Messmer ◽  
Santos J. González-Rojí ◽  
Christoph C. Raible ◽  
Thomas F. Stocker
Keyword(s):  
Observational Data ◽  
Temporal Correlation ◽  
Cumulus Parameterization ◽  
Weather Station ◽  
Data Set ◽  
Precipitation Patterns ◽  
Cumulus Scheme ◽  
Station Data ◽  
Weather Station Data ◽  
Simulated Precipitation

Abstract. Several sensitivity experiments with the Weather Research and Forecasting (WRF) model version 3.8.1 have been performed to find the optimal parameterization setup for precipitation amounts and patterns around Mount Kenya at a convection-permitting scale of 1 km. Hereby, the focus is on the cumulus scheme, with tests of the Kain–Fritsch, the Grell–Freitas, and no cumulus parameterizations. In addition, two longwave radiation schemes and two planetary boundary layer parameterizations are evaluated, and different nesting ratios and numbers of nests are tested. The precipitation amounts and patterns are compared against a large amount of weather station data and three gridded observational data sets. The temporal correlation of monthly precipitation sums show that fewer nests lead to a more constrained simulation, and hence the correlation is higher. The pattern correlation with weather station data confirms this result, but when comparing it to the most recent gridded observational data set the difference between the number of nests and nesting ratios is marginal. The precipitation patterns further reveal that using the Grell–Freitas cumulus parameterization in the domains with resolutions >5 km provides the best results when it comes to precipitation patterns and amounts. If no cumulus parameterization is used in any of the domains, the temporal correlation between gridded and in situ observations and simulated precipitation is especially poor with more nests. Moreover, even if the patterns are captured reasonably well, a clear overestimation in the precipitation amounts is simulated around Mount Kenya when using no cumulus scheme in all domains. The experiment with the Grell–Freitas cumulus parameterization in the domains with resolutions >5 km also provides reasonable results for 2 m temperature with respect to gridded observational and weather station data.

scholarly journals Sensitivity of precipitation and temperature over Mount Kenya area to physics parameterization options in a high-resolution model simulation performed with WRFV3.8.1

2020 ◽  
Author(s):  
Martina Messmer ◽  
Santos J. González-Rojí ◽  
Christoph C. Raible ◽  
Thomas F. Stocker
Keyword(s):  
Observational Data ◽  
Temporal Correlation ◽  
Cumulus Parameterization ◽  
Wave Radiation ◽  
Weather Station ◽  
Precipitation Patterns ◽  
Cumulus Scheme ◽  
Station Data ◽  
Weather Station Data ◽  
Simulated Precipitation

Abstract. Several sensitivity experiments with the Weather Research and Forecasting (WRF) model version 3.8.1 have been performed to find the optimal parameterization setup for precipitation amounts and patterns around Mount Kenya at a convection-permitting scale of 1 km. Hereby, the focus is on the cumulus scheme, with tests of the Kain-Fritsch, the Grell-Freitas and no cumulus parameterization for the parent and all nested domains. Besides, two long wave radiation schemes and two planetary boundary layer parameterizations are evaluated. Additionally, different nesting ratios and numbers of nests are tested. The precipitation amounts and patterns are compared against a large number of weather station data and three gridded observational data sets. The temporal correlation of monthly precipitation sums show that fewer nests lead to a more constrained simulation and hence, the correlation is higher. The pattern correlation with weather station data confirms this result, but when comparing it to the most recent gridded observational data set the difference between the number of nests and nesting ratios are marginal. The precipitation patterns further reveal that the Grell-Freitas cumulus parameterization provides the best results, when it comes to precipitation patterns and amounts. If no cumulus parameterization is used, the temporal correlation between gridded and in-situ observations and simulated precipitation is especially poor with more nests. Moreover, even if the patterns are captured quite well, a clear overestimation in the precipitation amounts is observed around Mount Kenya when using no cumulus scheme at all. The Grell-Freitas cumulus parameterization also provides reasonable results for 2-metre temperature with respect to gridded observational and weather station data.

scholarly journals Role of atmospheric horizontal resolution in simulating tropical and subtropical South American precipitation in HadGEM3-GC31

2020 ◽  
Author(s):  
Paul-Arthur Monerie ◽  
Amulya Chevuturi ◽  
Peter Cook ◽  
Nick Klingaman ◽  
Christopher E. Holloway
Keyword(s):  
South America ◽  
Amazon Basin ◽  
Climate Model ◽  
Spatial Scales ◽  
Moisture Flux ◽  
Horizontal Resolution ◽  
South American ◽  
North East ◽  
The Andes ◽  
Simulated Precipitation

Abstract. We assess the effect of increasing horizontal resolution on simulated precipitation over South America in a climate model. We use atmosphere-only simulations, performed with HadGEM3-GC31 at three horizontal resolutions: N96 (~ 130 km, 1.88° × 1.25°), N216 (~ 60 km, 0.83° × 0.56°), and N512 (~25 km, 0.35° × 0.23°). We show that all simulations have systematic biases in annual mean and seasonal mean precipitation over South America (e.g. too wet over the Amazon and too dry in northeast). Increasing horizontal resolution improves simulated precipitation over the Andes and north-east Brazil. Over the Andes, improvements from horizontal resolution continue to ~ 25 km, while over north-east Brazil, there are no improvements beyond ~ 60 km resolution. These changes are primarily related to changes in atmospheric dynamics and moisture flux convergence. Over the Amazon basin, precipitation variability increases at higher resolution. We show that some spatial and temporal features of daily South American precipitation are improved at high resolution, including the intensity spectra of rainfall. Spatial scales of daily precipitation features are also better simulated, suggesting that higher resolution may improve the representation of South American mesoscale convective systems.

scholarly journals Role of atmospheric horizontal resolution in simulating tropical and subtropical South American precipitation in HadGEM3-GC31

2020 ◽  
Vol 13 (10) ◽  
pp. 4749-4771
Author(s):  
Paul-Arthur Monerie ◽  
Amulya Chevuturi ◽  
Peter Cook ◽  
Nicholas P. Klingaman ◽  
Christopher E. Holloway
Keyword(s):  
South America ◽  
Amazon Basin ◽  
Climate Model ◽  
Spatial Scales ◽  
Moisture Flux ◽  
Horizontal Resolution ◽  
South American ◽  
Northeast Brazil ◽  
The Andes ◽  
Simulated Precipitation

Abstract. We assess the effect of increasing horizontal resolution on simulated precipitation over South America in a climate model. We use atmosphere-only simulations, performed with HadGEM3-GC31 at three horizontal resolutions: N96 (∼130 km; 1.88∘×1.25∘), N216 (∼60 km; 0.83∘×0.56∘), and N512 (∼25 km; 0.35∘×0.23∘). We show that all simulations have systematic biases in annual mean and seasonal mean precipitation over South America (e.g. too wet over the Amazon and too dry in the northeast). Increasing horizontal resolution improves simulated precipitation over the Andes and northeast Brazil. Over the Andes, improvements from horizontal resolution continue to ∼25 km, while over northeast Brazil, there are no improvements beyond ∼60 km resolution. These changes are primarily related to changes in atmospheric dynamics and moisture flux convergence. Over the Amazon Basin, precipitation variability increases at higher resolution. We show that some spatial and temporal features of daily South American precipitation are improved at high resolution, including the intensity spectra of rainfall. Spatial scales of daily precipitation features are also better simulated, suggesting that higher resolution may improve the representation of South American mesoscale convective systems.

Evaluating alternative data sets for ecological niche models of birds in the Andes

Ecography ◽  
2004 ◽  
Vol 27 (3) ◽  
pp. 350-360 ◽  
Author(s):  
Juan L. Parra ◽  
Catherine C. Graham ◽  
Juan F. Freile
Keyword(s):  
Ecological Niche ◽  
Data Sets ◽  
Ecological Niche Models ◽  
The Andes ◽  
Niche Models

scholarly journals Consistency of nonlinear interacting ghost dark energy with recent observations

2017 ◽  
Vol 26 (11) ◽  
pp. 1750124 ◽  
Author(s):  
E. Ebrahimi ◽  
H. Golchin ◽  
A. Mehrabi ◽  
S. M. S. Movahed
Keyword(s):  
Dark Energy ◽  
Observational Data ◽  
Nonlinear Interaction ◽  
Acoustic Oscillation ◽  
Joint Analysis ◽  
Data Sets ◽  
Dark Energy Density ◽  
Ghost Dark Energy ◽  
Interaction Terms ◽  
Best Fit

In this paper, we investigate ghost dark energy model in the presence of nonlinear interaction between dark energy and dark matter. We also extend the analysis to the so-called generalized ghost dark energy (GGDE) which [Formula: see text]. The model contains three free parameters as [Formula: see text] and [Formula: see text] (the coupling coefficient of interactions). We propose three kinds of nonlinear interaction terms and discuss the behavior of equation of state, deceleration and dark energy density parameters of the model. We also find the squared sound speed and search for signs of stability of the model. To compare the interacting GGDE model with observational data sets, we use more recent observational outcomes, namely SNIa from JLA catalog, Hubble parameter, baryonic acoustic oscillation and the most relevant CMB parameters including, the position of acoustic peaks, shift parameters and redshift to recombination. For GGDE with the first nonlinear interaction, the joint analysis indicates that [Formula: see text], [Formula: see text] and [Formula: see text] at 1 optimal variance error. For the second interaction, the best fit values at [Formula: see text] confidence are [Formula: see text], [Formula: see text] and [Formula: see text]. According to combination of all observational data sets considered in this paper, the best fit values for third nonlinearly interacting model are [Formula: see text], [Formula: see text] and [Formula: see text] at [Formula: see text] confidence interval. Finally, we found that the presence of interaction is compatible in mentioned models via current observational datasets.

scholarly journals Validation of the HIRHAM-Simulated Indian Summer Monsoon Circulation

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Stefan Polanski ◽  
Annette Rinke ◽  
Klaus Dethloff
Keyword(s):  
Summer Monsoon ◽  
Climate Model ◽  
Regional Climate ◽  
Horizontal Resolution ◽  
Monsoon Circulation ◽  
Data Sets ◽  
Data Set ◽  
Topographic Features ◽  
Temperature And Precipitation ◽  
Simulated Precipitation

The regional climate model HIRHAM has been applied over the Asian continent to simulate the Indian monsoon circulation under present-day conditions. The model is driven at the lateral and lower boundaries by European reanalysis (ERA40) data for the period from 1958 to 2001. Simulations with a horizontal resolution of 50 km are carried out to analyze the regional monsoon patterns. The focus in this paper is on the validation of the long-term summer monsoon climatology and its variability concerning circulation, temperature, and precipitation. Additionally, the monsoonal behavior in simulations for wet and dry years has been investigated and compared against several observational data sets. The results successfully reproduce the observations due to a realistic reproduction of topographic features. The simulated precipitation shows a better agreement with a high-resolution gridded precipitation data set over the central land areas of India and in the higher elevated Tibetan and Himalayan regions than ERA40.

scholarly journals Evaluating the effect of nutrient redistribution by animals on the phosphorus cycle of lowland Amazonia

2018 ◽  
Vol 15 (1) ◽  
pp. 279-295 ◽  
Author(s):  
Corina Buendía ◽  
Axel Kleidon ◽  
Stefano Manzoni ◽  
Björn Reu ◽  
Amilcare Porporato
Keyword(s):  
Amazon Basin ◽  
P Availability ◽  
Phosphorus Cycle ◽  
Terra Firme ◽  
The Andes ◽  
P Content ◽  
Spatial Redistribution ◽  
Soil Age ◽  
Seasonally Flooded ◽  
Lowland Amazonia

Abstract. Phosphorus (P) availability decreases with soil age and potentially limits the productivity of ecosystems growing on old and weathered soils. Despite growing on ancient soils, ecosystems of lowland Amazonia are highly productive and are among the most biodiverse on Earth. P eroded and weathered in the Andes is transported by the rivers and deposited in floodplains of the lowland Amazon basin creating hotspots of P fertility. We hypothesize that animals feeding on vegetation and detritus in these hotspots may redistribute P to P-depleted areas, thus contributing to dissipate the P gradient across the landscape. Using a mathematical model, we show that animal-driven spatial redistribution of P from rivers to land and from seasonally flooded to terra firme (upland) ecosystems may sustain the P cycle of Amazonian lowlands. Our results show how P imported to land by terrestrial piscivores in combination with spatial redistribution of herbivores and detritivores can significantly enhance the P content in terra firme ecosystems, thereby highlighting the importance of food webs for the biogeochemical cycling of Amazonia.

Sensible use of observational clinical data

2011 ◽  
Vol 22 (1) ◽  
pp. 7-13 ◽  
Author(s):  
J Marc Overhage ◽  
Lauren M Overhage
Keyword(s):  
Health Care ◽  
Clinical Trials ◽  
Medical Research ◽  
Observational Data ◽  
Clinical Data ◽  
Low Cost ◽  
Clinical Care ◽  
Data Sets ◽  
Large Size ◽  
Health Care Operations

Observational data sets offer many potential advantages for medical research including their low cost, large size and generalisability. Because they are collected for clinical care and health care operations purposes, observational data sets have some limitations that must be considered in order to perform useful analyses. Sensible use of observational data sets can yield valuable insights, particularly when clinical trials are impractical.

scholarly journals Precipitation Trends over the Indus Basin

Climate ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 116 ◽  
Author(s):  
Nir Y. Krakauer ◽  
Tarendra Lakhankar ◽  
Ghulam H. Dars
Keyword(s):  
Remote Sensing ◽  
Large Population ◽  
Tropical Rainfall Measuring Mission ◽  
Indus Basin ◽  
Data Sets ◽  
Precipitation Trend ◽  
Precipitation Data ◽  
Data Set ◽  
Station Data ◽  
Precipitation Trends

A large population relies on water input to the Indus basin, yet basinwide precipitation amounts and trends are not well quantified. Gridded precipitation data sets covering different time periods and based on either station observations, satellite remote sensing, or reanalysis were compared with available station observations and analyzed for basinwide precipitation trends. Compared to observations, some data sets tended to greatly underestimate precipitation, while others overestimate it. Additionally, the discrepancies between data set and station precipitation showed significant time trends in such cases, suggesting that the precipitation trends of those data sets were not consistent with station data. Among the data sets considered, the station-based Global Precipitation Climatology Centre (GPCC) gridded data set showed good agreement with observations in terms of mean amount, trend, and spatial and temporal pattern. GPCC had average precipitation of about 500 mm per year over the basin and an increase in mean precipitation of about 15% between 1891 and 2016. For the more recent past, since 1958 or 1979, no significant precipitation trend was seen. Among the remote sensing based data sets, the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) compared best to station observations and, though available for a shorter time period than station-based data sets such as GPCC, may be especially valuable for parts of the basin without station data. The reanalyses tended to have substantial biases in precipitation mean amount or trend relative to the station data. This assessment of precipitation data set quality and precipitation trends over the Indus basin may be helpful for water planning and management.

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