Search NASA⌕ Search

SEARCH · Search NASA

Results for “Model Coupling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Seasonal representation of extreme precipitation indices over the United States in CMIP6 present-day simulations

Realistically representing the present-day characteristics of extreme precipitation has been a challenge for global climate models, which is due in part to deficiencies in model resolution and physics, but is also due to a lack of consistency in gridded observations. In this study, we use three observation datasets, including gridded rain gauge and satellite data, to assess historical simulations from sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We separately evaluate summer and winter precipitation over the United States (US) with a comprehensive set of extreme precipitation indices, including an assessment of precipitation frequency, intensity and spatial structure. The observations exhibit significant differences in their estimates of area-average intensity distributions and spatial patterns of the mean and extremes of precipitation over the US. In general, the CMIP6 multi-model mean performs better than most individual models at capturing daily precipitation distributions and extreme precipitation indices, particularly in comparison to gauge-based data. Also, the representation of the extreme precipitation indices by the CMIP6 models is better in the summer than winter. Although the ‘standard’ horizontal-resolution can vary significantly across CMIP6 models, from ~0.7° to ~2.8°, we find that resolution is not a good indicator of model performance. Overall, our results highlight common biases in CMIP6 models and demonstrate that no single model is consistently the most reliable across all indices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Modification and validation of an analytical source model for external beam radiotherapy Monte Carlo dose calculations

Purpose: A dose calculation tool, which combines the accuracy of the dose planning method (DPM) Monte Carlo code and the versatility of a practical analytical multisource model, which was previously reported has been improved and validated for the Varian 6 and 10 MV linear accelerators (linacs). The calculation tool can be used to calculate doses in advanced clinical application studies. One shortcoming of current clinical trials that report dose from patient plans is the lack of a standardized dose calculation methodology. Because commercial treatment planning systems (TPSs) have their own dose calculation algorithms and the clinical trial participant who uses these systems is responsible for commissioning the beam model, variation exists in the reported calculated dose distributions. Today’s modern linac is manufactured to tight specifications so that variability within a linac model is quite low. The expectation is that a single dose calculation tool for a specific linac model can be used to accurately recalculate dose from patient plans that have been submitted to the clinical trial community from any institution. The calculation tool would provide for a more meaningful outcome analysis. Methods: The analytical source model was described by a primary point source, a secondary extra-focal source, and a contaminant electron source. Off-axis energy softening and fluence effects were also included. The additions of hyperbolic functions have been incorporated into the model to correct for the changes in output and in electron contamination with field size. A multileaf collimator (MLC) model is included to facilitate phantom and patient dose calculations. An offset to the MLC leaf positions was used to correct for the rudimentary assumed primary point source. Results: Dose calculations of the depth dose and profiles for field sizes 4 × 4 to 40 × 40 cm agree with measurement within 2% of the maximum dose or 2 mm distance to agreement (DTA) for 95% of the data points tested. The model was capable of predicting the depth of the maximum dose within 1 mm. Anthropomorphic phantom benchmark testing of modulated and patterned MLCs treatment plans showed agreement to measurement within 3% in target regions using thermoluminescent dosimeters (TLD). Using radiochromic film normalized to TLD, a gamma criteria of 3% of maximum dose and 2 mm DTA was applied with a pass rate of least 85% in the high dose, high gradient, and low dose regions. Finally, recalculations of patient plans using DPM showed good agreement relative to a commercial TPS when comparing dose volume histograms and 2D dose distributions. Conclusions: A unique analytical source model coupled to the dose planning method Monte Carlo dose calculation code has been modified and validated using basic beam data and anthropomorphic phantom measurement. While this tool can be applied in general use for a particular linac model, specifically it was developed to provide a singular methodology to independently assess treatment plan dose distributions from those clinical institutions participating in National Cancer Institute trials.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

DOE/UCAR Cooperative Agreement for the Regional and Global Climate Modeling Program

This progress report describes research supported by Cooperative Agreement between the University Corporation for Atmospheric Research (UCAR) and the U.S. Department of Energy (DOE) Biological and Environmental Research (BER). The purpose of the Cooperative Agreement is to provide support for an integrated research program to address key science questions relevant to the DOE Regional and Global Climate Modeling Program as part of the U.S. Global Change Research Program (USGCRP). The Cooperative Agreement funds four tasks that address key aspects of climate science, and this report provides a summary of activities for each task during the past year. As part of the Cooperative Agreement supported science, the Climate Change Prediction (CCP) group at the National Center for Atmospheric Research (NCAR), provides leadership to the Community Earth System Model (CESM) Climate Variability and Change Working Group (CVCWG) through co-chairs Gerald Meehl and Susan Bates (with DOE-funded co-chair Peter Gleckler, along with Clara Deser and Shang-Ping Xie). Over the past year CCP has played a leadership role in performing climate change simulations and analyses with the Community Climate System Model version 4 (CCSM4) and CESM version 1. These simulations and analyses made strong contributions to the federal USGCRP in several high priority areas. We have also been involved in planning for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) which started in 2015. Simulations from CMIP6 will form the basis for ongoing U.S. National Assessment activities, and the IPCC AR6.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Variance and Predictability of Precipitation at Seasonal-to-Interannual Timescales

A series of atmospheric general circulation model (AGCM) simulations, spanning a total of several thousand years, is used to assess the impact of land-surface and ocean boundary conditions on the seasonal-to-interannual variability and predictability of precipitation in a coupled modeling system. In the first half of the analysis, which focuses on precipitation variance, we show that the contributions of ocean, atmosphere, and land processes to this variance can be characterized, to first order, with a simple linear model. This allows a clean separation of the contributions, from which we find: (1) land and ocean processes have essentially different domains of influence, i.e., the amplification of precipitation variance by land-atmosphere feedback is most important outside of the regions (mainly in the tropics) that are most affected by sea surface temperatures; and (2) the strength of land-atmosphere feedback in a given region is largely controlled by the relative availability of energy and water there. In the second half of the analysis, the potential for seasonal-to-interannual predictability of precipitation is quantified under the assumption that all relevant surface boundary conditions (in the ocean and on land) are known perfectly into the future. We find that the chaotic nature of the atmospheric circulation imposes fundamental limits on predictability in many extratropical regions. Associated with this result is an indication that soil moisture initialization or assimilation in a seasonal-to-interannual forecasting system would be beneficial mainly in transition zones between dry and humid regions.

Koster, Randal D.↗
DOE OSTI2019

The transient response of atmospheric and oceanic heat transports to anthropogenic warming

Model projections of the near-future response to anthropogenic warming show compensation between meridional heat transports by the atmosphere (AHT) and ocean (OHT) that are largely symmetric about the equator1,2,3, the causes of which remain unclear. Here, using both the Coupled Model Intercomparison Project Phase 5 archive and Community Climate System Model version 4 simulations forced with Representative Concentration Pathway 8.5 to 2600, we show that this transient compensation—specifically during the initial stage of warming—is caused by combined changes in both atmospheric and oceanic circulations. In particular, it is caused by a southward OHT associated with a weakened Atlantic Meridional Overturning Circulation, a northward apparent OHT associated with an ocean heat storage maximum around the Southern Ocean, and a symmetric coupled response of the Hadley and Subtropical cells in the Indo-Pacific basin. It is further shown that the true advective OHT differs from the flux-inferred OHT in the initial warming due to the inhomogeneous responses of ocean heat storage. These results provide new insights to further our understanding of future heat transport responses, and thereby global climatic processes such as the redistribution of ocean heat.

He, Chengfei↗
DOE OSTI2014

Causes and Implications of Persistent Atmospheric Carbon Dioxide Biases in Earth System Models

The strength of feedbacks between a changing climate and future CO2 concentrations are uncertain and difficult to predict using Earth System Models (ESMs). We analyzed emission-driven simulations--in which atmospheric CO2 levels were computed prognostically--for historical (1850-2005) and future periods (RCP 8.5 for 2006-2100) produced by 15 ESMs for the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Comparison of ESM prognostic atmospheric CO2 over the historical period with observations indicated that ESMs, on average, had a small positive bias in predictions of contemporary atmospheric CO2. Weak ocean carbon uptake in many ESMs contributed to this bias, based on comparisons with observations of ocean and atmospheric anthropogenic carbon inventories. We found a significant linear relationship between contemporary atmospheric CO2 biases and future CO2 levels for the multi-model ensemble. We used this relationship to create a contemporary CO2 tuned model (CCTM) estimate of the atmospheric CO2 trajectory for the 21st century. The CCTM yielded CO2 estimates of 600 {plus minus} 14 ppm at 2060 and 947 {plus minus} 35 ppm at 2100, which were 21 ppm and 32 ppm below the multi-model mean during these two time periods. Using this emergent constraint approach, the likely ranges of future atmospheric CO2, CO2-induced radiative forcing, and CO2-induced temperature increases for the RCP 8.5 scenario were considerably narrowed compared to estimates from the full ESM ensemble. Our analysis provided evidence that much of the model-to-model variation in projected CO2 during the 21st century was tied to biases that existed during the observational era, and that model differences in the representation of concentration-carbon feedbacks and other slowly changing carbon cycle processes appear to be the primary driver of this variability. By improving models to more closely match the long-term time series of CO2 from Mauna Loa, our analysis suggests uncertainties in future climate projections can be reduced.

Hoffman, Forrest M↗
DOE OSTI2013

Consistency of simulated and observed regional changes in temperature, sea level pressure and precipitation

There is increasing pressure from stakeholders for highly localised climate change projections. A comprehensive assessment of climate model performance at the grid box scale in simulating recent change, however, is not available at present. Therefore, we compare observed changes in near-surface temperature, sea level pressure (SLP) and precipitation with simulations available from the Coupled Model Intercomparison Projects 3 and 5 (CMIP3 and CMIP5). In both multi-model datasets we find coherent areas of inconsistency between observed and simulated local trends per degree global warming in both temperature and SLP in the majority of models. Localised projections should thus take into account the possibility of regional biases shared across models. In contrast, simulated changes in precipitation are not significantly different from observations due to low signal-to-noise ratio of local precipitation changes. Therefore, recent regional rainfall change is likely not providing useful constraints for future projections as of yet. Comparing the two most recent sets of internationally coordinated climate model experiments, we find no indication of improvement in the models’ ability to reproduce local trends in temperature, SLP and precipitation.

Environmental Sciences & Ecology↗
DOE OSTI2024

Estimating Uncertainty in Simulated ENSO Statistics

Abstract Large ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling‐based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre‐industrial control or historical runs. This suggests that pre‐industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Analysis and Optimization of a Novel Hexagonal Waveguide Concentrator for Solar Thermal Applications

This paper analyzes a novel, cost-effective planar waveguide solar concentrator design that is inspired by cellular hexagonal structures in nature with the benefits of facile installation and low operation and maintenance cost. A coupled thermal and optical analysis of solar irradiation through an ideal hexagonal waveguide concentrator integrated with a linear receiver is presented, along with a cost analysis methodology, to establish the upper limit of performance. The techno-economic model, coupled with numerical optimization, is used to determine designs that maximized power density and minimized the cost of heat in the temperature range of 100–250 °C, which constitutes more than half of the industrial process heat demand. Depending on the incident solar irradiation and the application temperature, the cost of heat for the optimal design configuration ranged between 0.1–0.27 $/W and 0.075–0.18 $/W for waveguide made of ZK7 glass and polycarbonate, respectively. A techno-economic analysis showed the potential of the technology to achieve cost as low as 80 $/m2 and 61 $/m2 for waveguide made of ZK7 glass and polycarbonate material, respectively, which is less than half the cost of state-of-the-art parabolic trough concentrators. Overall, the hexagonal waveguide solar concentrator technology shows immense potential for decarbonizing the industrial process heat and thermal desalination sectors.

14 SOLAR ENERGY↗
NASA NTRS2018

Climate Shifts Within Major Agricultural Seasons for +1.5 and +2.0 °C Worlds: HAPPI Projections and AgMIP Modeling Scenarios

This study compares climate changes in major agricultural regions and current agricultural seasons associated with global warming of +1.5 or +2.0 °C above pre-industrial conditions. It describes the generation of climate scenarios for agricultural modeling applications conducted as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments. Climate scenarios from the Half a degree Additional warming, Projections, Prognosis and Impacts project (HAPPI) are largely consistent with transient scenarios extracted from RCP4.5 simulations of the Coupled Model Intercomparison Project phase 5 (CMIP5). Focusing on food and agricultural systems and top-producing breadbaskets in particular, we distinguish maize, rice, wheat, and soy season changes from global annual mean climate changes. Many agricultural regions warm at a rate that is faster than the global mean surface temperature (including oceans) but slower than the mean land surface temperature, leading to regional warming that exceeds 0.5 °C between the +1.5 and +2.0 °C Worlds. Agricultural growing seasons warm at a pace slightly behind the annual temperature trends in most regions, while precipitation increases slightly ahead of the annual rate. Rice cultivation regions show reduced warming as they are concentrated where monsoon rainfall is projected to intensify, although projections are influenced by Asian aerosol loading in climate mitigation scenarios. Compared to CMIP5, HAPPI slightly underestimates the CO2 concentration that corresponds to the +1.5 °C World but overestimates the CO2 concentration for the +2.0 °C World, which means that HAPPI scenarios may also lead to an overestimate in the beneficial effects of CO2 on crops in the +2.0 °C World. HAPPI enables detailed analysis of the shifting distribution of extreme growing season temperatures and precipitation, highlighting widespread increases in extreme heat seasons and heightened skewness toward hot seasons in the tropics. Shifts in the probability of extreme drought seasons generally tracked median precipitation changes; however, some regions skewed toward drought conditions even where median precipitation changes were small. Together, these findings highlight unique seasonal and agricultural region changes in the +1.5 °C and +2.0 °C worlds for adaptation planning in these climate stabilization targets.

Alexander C. Ruane↗
DOE OSTI2013

A continuous hydrogen desorption model from zirconium hydride and subsequent metal in Vacuum

Predictions of a one-dimensional, moving boundary model coupled with a kinetic description of hydrogen desorption from a two-phase region of {delta}-ZrH{sub 1.6{+-}}n and {alpha}-Zr have been compared with the hydrogen flux obtained in a thermal desorption spectroscopy experiment. The model accurately reproduces the major features of the hydrogen desorption flux on the sample surface after executing a parameter optimization. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2018

Numerical Investigation of Desulfurization Kinetics in Gas-Stirred Ladles by a Quick Modeling Analysis Approach

A quick modeling analysis approach for predicting the slag-steel reaction and desulfurization kinetics in argon gas-stirred ladles has been developed in this study. The model consists of two uncoupled components: (i) a computational fluid dynamics (CFD) model for predicting the fluid flow and the characteristics of slag-steel interface, and (ii) a multicomponent reaction kinetics model for calculating the desulfurization evolution. The steel-slag interfacial area and mass transfer coefficients predicted by the CFD simulation are used as the processing data for the reaction model. Since the desulfurization predictions are uncoupled from the CFD simulation, the computational time of this uncoupled predictive approach is decreased by at least 100 times for each case study when compared with the CFD-reaction kinetics fully coupled model. The uncoupled modeling approach was validated by comparing the evolution of steel and slag compositions with the experimentally measured data during ladle metallurgical furnace (LMF) processing at Nucor Steel Tuscaloosa, Inc. Then, the validated approach was applied to investigate the effects of the initial steel and slag compositions, as well as different types of additions during the refining process on the desulfurization efficiency. The results revealed that the sulfur distribution ratio and the desulfurization reaction can be promoted by making Al and CaO additions during the refining process. It was also shown that by increasing the initial Al content in liquid steel, both Al oxidation and desulfurization rates rapidly increase. In addition, it was found that the variation of the initial Si content in steel has no significant influence on the desulfurization rate. Lastly, if the initial CaO content in slag is increased or the initial Al{sub 2}O{sub 3} content is decreased in the fluid-slag compositional range, the desulfurization rate can be improved significantly during the LMF process.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Heavy and light hadron production and D -hadron correlation in relativistic heavy-ion collisions

We establish a linear Boltzmann transport (LBT) model coupled to hydrodynamical background to study hard parton evolution in heavy-ion collisions. Both elastic and inelastic scatterings are included in our calculations; and heavy and light flavor partons are treated on the same footing. Within this LBT model, we provide good descriptions of heavy and light hadron suppression and anisotropic flow in heavy-ion collisions. Angular correlation functions between heavy and light flavor hadrons are studied for the first time and shown able to quantify not only the amount of heavy quark energy loss, but also how the parton energy is re-distributed in parton showers.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

A transient site balance model for atomic layer etching

We present a transient site balance model of plasma-assisted atomic layer etching of silicon (Si) with alternating exposure to chlorine gas (Cl 2 ) and argon ions (Ar + ). Molecular dynamics (MD) simulation results are used to provide parameters for the model. The model couples the dynamics of a top monolayer surface region ('top layer') and a perfectly mixed subsurface region ('mixed layer'). The differential equations describing the rates of change of the Cl coverage in the two layers are transient mass balances. Model predictions include Cl coverages and rates of etching of various species from the surface as a function of Cl 2 or Ar + fluence. The simplified phenomenological model reproduces the MD simulation results well over a range of conditions. Comparing model predictions directly to experimental optical emission spectroscopy data, as reported in a previous paper, provides further evidence of the accuracy of the model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Multifrequency Models of Black Hole Photon Rings from Low-luminosity Accretion Disks

Images of black holes encode both astrophysical and gravitational properties. Detecting highly lensed features in images can differentiate between these two effects. We present an accretion disk emission model coupled to the Adaptive Analytical Ray Tracing (AART) code that allows a fast parameter space exploration of black hole photon ring images produced from synchrotron emission from 10 to 670 GHz. As an application, we systematically study several disk models and compute their total flux density, average radii, and optical depth. The model parameters are chosen around fiducial values calibrated to general relativistic magnetohydrodynamic (GRMHD) simulations and observations of M87*. For the parameter space studied, we characterize the transition between optically thin and thick regimes and the frequency at which the first photon ring is observable. Our results highlight the need for careful definitions of photon ring radius in the image domain, as in certain models the highly lensed photon ring is dimmer than the direct emission at certain angles. We find that at low frequencies the ring radii are set by the electron temperature, while at higher frequencies the magnetic field strength plays a more significant role, demonstrating how multifrequency analysis can also be used to infer plasma parameters. Lastly, we show how our implementation can qualitatively reproduce multifrequency black hole images from GRMHD simulations when adding time variability to our disk model through Gaussian random fields. This approach provides a new method for simulating observations from the Event Horizon Telescope and the proposed Black Hole Explorer space mission.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Two Leading ENSO Modes and El Niño Types in the Zebiak–Cane Model

Modern instrumental records reveal that El Niño events differ in their spatial patterns and temporal evolutions. Attempts have been made to categorize them roughly into two main types: eastern Pacific (EP; or cold tongue) and central Pacific (CP; or warm pool) El Niño events. In this study, a modified version of the Zebiak–Cane (MZC) coupled model is used to examine the dynamics of these two types of El Niño events. Linear eigenanalysis of the model is conducted to show that there are two leading El Niño–Southern Oscillation (ENSO) modes with their SST patterns resembling those of two types of El Niño. Thus, they are referred to as the EP and CP ENSO modes. These two modes are sensitive to changes in the mean states. The heat budget analyses demonstrate that the EP (CP) mode is dominated by thermocline (zonal advective) feedback. Therefore, the weak (strong) mean wind stress and deep (shallow) mean thermocline prefer the EP (CP) ENSO mode because of the relative dominance of thermocline (zonal advective) feedback under such a mean state. Consistent with the linear stability analysis, the occurrence ratio of CP/EP El Niño events in the nonlinear simulations generally increases toward the regime where the linear CP ENSO mode has relatively higher growth rate. These analyses suggest that the coexistence of two leading ENSO modes is responsible for two types of El Niño simulated in the MZC model. This model result may provide a plausible scenario for the observed ENSO diversity.

Meteorology & Atmospheric Sciences↗
NASA NTRSDate not supplied

Improving Simulation of Irrigation in a Global Land Surface Model

Studies have shown that irrigated agriculture has substantial effects on local to regional hydrologic and atmospheric processes. These effects include increased evapotranspiration and near surface humidity, decreased air and ground temperature, augmented downwind precipitation, enhanced recharge, depletion of source water stocks, and overall changes to the regional water budget. However, developers of global offline and coupled modeling systems have been slow to incorporate irrigation. This presentation will describe progress towards simulating irrigation worldwide in the Land Information System (LIS) and Global Land Data Assimilation System (GLDAS). We have developed and refined the ability to simulate sprinkler irrigation, furrow irrigation, paddy irrigation, and drip irrigation. We have reconciled multiple global and national irrigation, land cover, and crop type databases in an effort to (1) distinguish irrigated and rainfed agriculture, (2) simulate the proper distribution of crops (as sub-grid tiles) in each model grid pixel, and (3) apply the most likely irrigation method in each tile based on the crop type and country or state level information on the relative usage of sprinkler, flood, and drip irrigation. Using agricultural water withdrawal data, as available, for calibration and evaluation, we show that the implemented approach is promising but that much fine tuning remains to be done. In particular, irrigation model parameters that produce good results in one location often produce less satisfying results in others.

Matthew Rodell↗
DOE OSTI2019

Alaska Daily Extreme Precipitation Processes in a Subset of CMIP5 Global Climate Models

We analyze physical processes leading to daily wintertime (December, January, and February) extreme precipitation events in Alaska between 1986 and 2005. This is done by applying self-organizing maps to environmental conditions corresponding to National Centers for Environmental Information precipitation, using the European Centre for Medium-Range Weather Forecasts reanalysis (ERA-Interim) and Coupled Model Intercomparison Project 5 (CMIP5) global climate selected Global climate model (GCM; selected GCMs). We focus on widespread extreme events, defined as the top 0.1% of daily precipitation occurring on at least six grid boxes on the same day. The self-organizing maps methodology allows identifying large-scale circulations conducive to extreme events. This methodology identifies distinctive circulation patterns conducive to producing extreme events with a trough west of Alaska leading to south or southwest flow across the state. Extreme events occur along the windward (southern) side of the Alaska Range due to uplift by the mountains in the ERA-Interim and in all models. In the National Centers for Environmental Information observations, precipitation rates are greater than in any of the selected GCMs. Simulated extreme precipitation decreases as model resolution decreases, and our study suggests that the smoothness of model topography is a reason for the scaling between model precipitation rate and model resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗