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All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Velocity Characteristics of Evaporated Plasma using Hinode/EIS

This paper presents a detailed study of chromospheric evaporation using the EUV Imaging Spectrometer (EIS) onboard Hinode in conjunction with HXR observat,ions from RHESSI. The advanced capabilities of EIS were used to measure Doppler shifts in 15 emission lines covering the temperature range T=0.05-16 MK during the impulsive phase of a C-class flare on 2007 December 14. Blueshifts indicative of the evaporated material were observed in six emission lines from Fe XIV-XXIV (2-16 MK). Upflow velocity was found to scale with temperature as v(sub up) (kilometers per second) approximately equal to 5-17 T (MK). Although the hottest emission lines, Fe XXIII and Fe XXIV, exhibited upflows of greater than 200 kilometers per second, their line profiles were found to be dominated by a stationary component in stark contrast to the predictions of the standard flare model. Emission from O VI-Fe XIII lines (0.5-1.5 MK) was found to be redshifted by v(sub down) (kilometers per second) approximately equal to 60-17 T (MK) and was interpreted as the downward-moving 'plug' characteristic of explosive evaporation. These downflows occur at temperatures significantly higher than previously expected. Both upflows and downflows were spatially and temporally correlated with HXR emission observed by RHESSI that provided the properties of the electron beam deemed to be the driver of the evaporation. The energy contained in the electron beam was found to be greater than or equal to 10(sup 11) ergs per square centimeter per second consistent with the value required to drive explosive chromospheric evaporation from hydrodynamic simulations.

Milligan, Ryan O.↗
NASA NTRS2011

Towards Improved Snow Water Equivalent Estimation via GRACE Assimilation

Passive microwave (e.g. AMSR-E) and visible spectrum (e.g. MODIS) measurements of snow states have been used in conjunction with land surface models to better characterize snow pack states, most notably snow water equivalent (SWE). However, both types of measurements have limitations. AMSR-E, for example, suffers a loss of information in deep/wet snow packs. Similarly, MODIS suffers a loss of temporal correlation information beyond the initial accumulation and final ablation phases of the snow season. Gravimetric measurements, on the other hand, do not suffer from these limitations. In this study, gravimetric measurements from the Gravity Recovery and Climate Experiment (GRACE) mission are used in a land surface model data assimilation (DA) framework to better characterize SWE in the Mackenzie River basin located in northern Canada. Comparisons are made against independent, ground-based SWE observations, state-of-the-art modeled SWE estimates, and independent, ground-based river discharge observations. Preliminary results suggest improved SWE estimates, including improved timing of the subsequent ablation and runoff of the snow pack. Additionally, use of the DA procedure can add vertical and horizontal resolution to the coarse-scale GRACE measurements as well as effectively downscale the measurements in time. Such findings offer the potential for better understanding of the hydrologic cycle in snow-dominated basins located in remote regions of the globe where ground-based observation collection if difficult, if not impossible. This information could ultimately lead to improved freshwater resource management in communities dependent on snow melt as well as a reduction in the uncertainty of river discharge into the Arctic Ocean.

Forman, Bart↗
NASA NTRS2011

Precision Continuum Receivers for Astrophysical Applications

Cryogenically cooled HEMT (High Electron Mobility Transistor) amplifiers find widespread use in radioastronomy receivers. In recent years, these devices have also been commonly employed in broadband receivers for precision measurements of the Cosmic Microwave Background (CMB) radiation. In this setting, the combination of ultra-low-noise and low-spectral-resolution observations reinforce the importance achieving suitable control over the device environment to achieve fundamentally limited receiver performance. The influence of the intrinsic amplifier stability at low frequencies on data quality (e.g., achievable noise and residual temporal correlations), observational and calibration strategies, as well as architectural mitigation approaches in this setting will be discussed. The implications of device level 1/f fluctuations reported in the literature on system performance will be reviewed.

Wollack, Edward J.↗
NASA NTRS2011

Validation and Simulation of Ares I Scale Model Acoustic Test - 2 - Simulations at 5 Foot Elevation for Evaluation of Launch Mount Effects

The Ares I Scale Model Acoustics Test (ASMAT) is a series of live-fire tests of scaled rocket motors meant to simulate the conditions of the Ares I launch configuration. These tests have provided a well documented set of high fidelity measurements useful for validation including data taken over a range of test conditions and containing phenomena like Ignition Over-Pressure and water suppression of acoustics. Expanding from initial simulations of the ASMAT setup in a held down configuration, simulations have been performed using the Loci/CHEM computational fluid dynamics software for ASMAT tests of the vehicle at 5 ft. elevation (100 ft. real vehicle elevation) with worst case drift in the direction of the launch tower. These tests have been performed without water suppression and have compared the acoustic emissions for launch structures with and without launch mounts. In addition, simulation results have also been compared to acoustic and imagery data collected from similar live-fire tests to assess the accuracy of the simulations. Simulations have shown a marked change in the pattern of emissions after removal of the launch mount with a reduction in the overall acoustic environment experienced by the vehicle and the formation of highly directed acoustic waves moving across the platform deck. Comparisons of simulation results to live-fire test data showed good amplitude and temporal correlation and imagery comparisons over the visible and infrared wavelengths showed qualitative capture of all plume and pressure wave evolution features.

Strutzenberg, Louise L.↗
NASA NTRS2012

Comparative Study of Aerosol and Cloud Detected by CALIPSO and OMI

The Ozone Monitoring Instrument (OMI) on the Aura Satellite detects the presence of desert dust and smoke particles (also known as aerosols) in terms of a parameter known as the UV Aerosol Index (UV AI). The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission measures the vertical distribution of aerosols and clouds. Aerosols and clouds play important roles in the atmosphere and climate system. Accurately detecting their presence, altitude, and properties using satellite radiance measurements is a very important task. This paper presents a comparative analysis of the CALIPSO Version 2 Vertical Feature Mask (VFM) product with the (OMI) UV Aerosol Index (UV AI) and reflectivity datasets for a full year of 2007. The comparison is done at regional and global scales. Based on CALIPSO arid OMI observations, the vertical and horizontal extent of clouds and aerosols are determined and the effects of aerosol type selection, load, cloud fraction on aerosol identification are discussed. It was found that the spatial-temporal correlation found between CALIPSO and OMI observations, is strongly dependent on aerosol types and cloud contamination. CALIPSO is more sensitivity to cloud and often misidentifies desert dust aerosols as cloud, while some small scale aerosol layers as well as some pollution aerosols are unidentified by OMI UV AI. Large differences in aerosol distribution patterns between CALIPSO and OMI are observed, especially for the smoke and pollution aerosol dominated areas. In addition, the results found a significant correlation between CALIPSO lidar 1064 nm backscatter and the OMI UV AI over the study regions.

Chen, Zhong↗
NASA NTRS2013

Interactive Ozone and Methane Chemistry in GISS-E2 Historical and Future Climate Simulations

The new generation GISS climate model includes fully interactive chemistry related to ozone in historical and future simulations, and interactive methane in future simulations. Evaluation of ozone, its tropospheric precursors, and methane shows that the model captures much of the largescale spatial structure seen in recent observations. While the model is much improved compared with the previous chemistry-climate model, especially for ozone seasonality in the stratosphere, there is still slightly too rapid stratospheric circulation, too little stratosphere-to-troposphere ozone flux in the Southern Hemisphere and an Antarctic ozone hole that is too large and persists too long. Quantitative metrics of spatial and temporal correlations with satellite datasets as well as spatial autocorrelation to examine transport and mixing are presented to document improvements in model skill and provide a benchmark for future evaluations. The difference in radiative forcing (RF) calculated using modeled tropospheric ozone versus tropospheric ozone observed by TES is only 0.016W/sq. m. Historical 20th Century simulations show a steady increase in whole atmosphere ozone RF through 1970 after which there is a decrease through 2000 due to stratospheric ozone depletion. Ozone forcing increases throughout the 21st century under RCP8.5 owing to a projected recovery of stratospheric ozone depletion and increases in methane, but decreases under RCP4.5 and 2.6 due to reductions in emissions of other ozone precursors. RF from methane is 0.05 to 0.18W/ sq. m higher in our model calculations than in the RCP RF estimates. The surface temperature response to ozone through 1970 follows the increase in forcing due to tropospheric ozone. After that time, surface temperatures decrease as ozone RF declines due to stratospheric depletion. The stratospheric ozone depletion also induces substantial changes in surface winds and the Southern Ocean circulation, which may play a role in a slightly stronger response per unit forcing during later decades. Tropical precipitation shifts south during boreal summer from 1850 to 1970, but then shifts northward from 1970 to 2000, following upper tropospheric temperature gradients more strongly than those at the surface.

Ozone↗
NASA NTRS2010

High-Resolution Subtropical Summer Precipitation Derived from Dynamical Downscaling of the NCEP-DOE Reanalysis: How Much Small-Scale Information Is Added by a Regional Model?

This study assesses the regional-scale summer precipitation produced by the dynamical downscaling of analyzed large-scale fields. The main goal of this study is to investigate how much the regional model adds smaller scale precipitation information that the large-scale fields do not resolve. The modeling region for this study covers the southeastern United States (Florida, Georgia, Alabama, South Carolina, and North Carolina) where the summer climate is subtropical in nature, with a heavy influence of regional-scale convection. The coarse resolution (2.5deg latitude/longitude) large-scale atmospheric variables from the National Center for Environmental Prediction (NCEP)/DOE reanalysis (R2) are downscaled using the NCEP Environmental Climate Prediction Center regional spectral model (RSM) to produce precipitation at 20 km resolution for 16 summer seasons (19902005). The RSM produces realistic details in the regional summer precipitation at 20 km resolution. Compared to R2, the RSM-produced monthly precipitation shows better agreement with observations. There is a reduced wet bias and a more realistic spatial pattern of the precipitation climatology compared with the interpolated R2 values. The root mean square errors of the monthly R2 precipitation are reduced over 93 (1,697) of all the grid points in the five states (1,821). The temporal correlation also improves over 92 (1,675) of all grid points such that the domain-averaged correlation increases from 0.38 (R2) to 0.55 (RSM). The RSM accurately reproduces the first two observed eigenmodes, compared with the R2 product for which the second mode is not properly reproduced. The spatial patterns for wet versus dry summer years are also successfully simulated in RSM. For shorter time scales, the RSM resolves heavy rainfall events and their frequency better than R2. Correlation and categorical classification (above/near/below average) for the monthly frequency of heavy precipitation days is also significantly improved by the RSM.

NARR↗
NASA NTRS2014

Fatigue in U.S. Astronauts Onboard the International Space Station: Environmental Factors, Operational Impacts, and Implementation of Countermeasures

Since 2000, US astronauts have been supporting missions up to a six month duration on the International Space Station (ISS). Crewmembers have experienced fatigue for reasons similar to military deployments. Astronauts experience psychological stressors such as heavy workloads, extended duty periods, circadian misalignment, inadequate/ineffective sleep, and loss of the environmental cues of a gravity environment. Complicating the psychological stressors are environmental factors; distracting background noise, unexpected and variable mission schedules, unfavorable thermal control, elevated CO2 levels, and an unusual sleep environment with schedules that impinge on pre‐sleep periods. Physiological contributors to poor sleep and fatigue include a cephalad fluid shift and back pain. Restful sleep is further challenged due to a lack of gravity‐related proprioceptive cues and need for restraints. The term "space fog" has been used by astronauts to describe a phenomenon of forgetfulness, slowed reaction time and transient confusion while trying to complete tasks. There is a distinct temporal correlation with arrival on the Space Station and the onset of slowed cognitive skills and a spontaneous resolution that may take up to 6 weeks. The Genesis of this phenomenon may be chronic fatigue secondary to transitioning from a planar environment to a 360deg microgravity perspective. Recently, countermeasures to improve sleep duration and quality in astronauts on the ISS have been instituted with moderate degrees of success as measured by self‐reaction time (psychomotor vigilance task testing), actigraphy, and subjective reports. Judicious use of stimulants and hypnotics, light therapy, controlled sleep periods and sleep shifting and reducing ambient CO2 levels are a few of the most promising countermeasures being used in space to improve sleep and reduce fatigue.

Scheuring, R. A.↗
NASA NTRS2014

Global Distribution and Variability of Surface Skin and Surface Air Temperatures as Depicted in the AIRS Version-6 Data Set

In this presentation, we will briefly describe the significant improvements made in the AIRS Version-6 retrieval algorithm, especially as to how they affect retrieved surface skin and surface air temperatures. The global distribution of seasonal 1:30 AM and 1:30 PM local time 12 year climatologies of Ts,a will be presented for the first time. We will also present the spatial distribution of short term 12 year anomaly trends of Ts,a at 1:30 AM and 1:30 PM, as well as the spatial distribution of temporal correlations of Ts,a with the El Nino Index. It will be shown that there are significant differences between the behavior of 1:30 AM and 1:30 PM Ts,a anomalies in some arid land areas.

Retrieval algorithm↗
NASA NTRS2012

Evolution of Altimetry Calibration and Future Challenges

Over the past 20 years, altimetry calibration has evolved from an engineering-oriented exercise to a multidisciplinary endeavor driving the state of the art. This evolution has been spurred by the developing promise of altimetry to capture the large-scale, but small-amplitude, changes of the ocean surface containing the expression of climate change. The scope of altimeter calibration/validation programs has expanded commensurately. Early efforts focused on determining a constant range bias and verifying basic compliance of the data products with mission requirements. Contemporary investigations capture, with increasing accuracies, the spatial and temporal characteristics of errors in all elements of the measurement system. Dedicated calibration sites still provide the fundamental service of estimating absolute bias, but also enable long-term monitoring of the sea-surface height and constituent measurements. The use of a network of island and coastal tide gauges has provided the best perspective on the measurement stability, and revealed temporal variations of altimeter measurement system drift. The cross-calibration between successive missions provided fundamentally new information on the performance of altimetry systems. Spatially and temporally correlated errors pose challenges for future missions, underscoring the importance of cross-calibration of new measurements against the established record.

calibration↗
NASA NTRS2011

Space-Time Data Fusion

Space-time Data Fusion (STDF) is a methodology for combing heterogeneous remote sensing data to optimally estimate the true values of a geophysical field of interest, and obtain uncertainties for those estimates. The input data sets may have different observing characteristics including different footprints, spatial resolutions and fields of view, orbit cycles, biases, and noise characteristics. Despite these differences all observed data can be linked to the underlying field, and therefore the each other, by a statistical model. Differences in footprints and other geometric characteristics are accounted for by parameterizing pixel-level remote sensing observations as spatial integrals of true field values lying within pixel boundaries, plus measurement error. Both spatial and temporal correlations in the true field and in the observations are estimated and incorporated through the use of a space-time random effects (STRE) model. Once the models parameters are estimated, we use it to derive expressions for optimal (minimum mean squared error and unbiased) estimates of the true field at any arbitrary location of interest, computed from the observations. Standard errors of these estimates are also produced, allowing confidence intervals to be constructed. The procedure is carried out on a fine spatial grid to approximate a continuous field. We demonstrate STDF by applying it to the problem of estimating CO2 concentration in the lower-atmosphere using data from the Atmospheric Infrared Sounder (AIRS) and the Japanese Greenhouse Gasses Observing Satellite (GOSAT) over one year for the continental US.

Greenhouse Gases Observing Satellite (GOSAT)↗
NASA NTRS2013

Observations of Global and Regional Ionospheric Irregularities and Scintillation Using GNSS Tracking Networks

The rate of TEC index (ROTI) is a measurement that characterizes ionospheric irregularities. It can be obtained from standard GNSS dual-frequency phase data collected using a geodetic type of GNSS receiver. By processing GPS data from ground-based networks of International GNSS Service and Continuously Operating Reference Station (CORS), ROTI maps have been produced to observe global and regional scintillation activities. A major mid-latitude scintillation event in the contiguous United States is reported here that was captured in ROTI maps produced using CORS GPS data collected during a space weather storm. The analyses conducted in this work and previously by another group indicate that ROTI is a good occurrence indicator of both amplitude and phase scintillations of GPS L-band signals, even though the magnitudes of ROTI, S4, and sigma(sub phi) can be different. For example, our analysis indicates that prominent ROTI and the L1 phase scintillation (sigma(sub phi)) are well correlated temporally in the polar region while L1 amplitude scintillation rarely occurs. The differences are partially attributed to physics processes in different latitude regions, such as high-speed plasma convection in the polar region that can suppress the amplitude scintillation. An analysis of the impact of ionospheric scintillation on precise positioning, which requires use of dual-frequency phase data, is also conducted. The results indicate that significant (more than an order of magnitude) positioning errors can occur under phase scintillation conditions.

ionospheric measurements↗
NASA NTRS2016

Validation of AIRS V6 Surface Temperature over Greenland with GCN and NOAA Stations

This work compares the temporal and spatial characteristics of the AIRSAMSU (Atmospheric Infrared Sounder Advanced Microwave Sounding Unit A) Version 6 and MODIS (Moderate resolution Imaging Spectroradiometer) Collection 5 derived surface temperatures over Greenland. To estimate uncertainties in space-based surface temperature measurements, we re-projected the MODIS Ice Surface Temperature (IST) to 0.5 by 0.5 degree spatial resolution. We also re-gridded AIRS Skin Temperature (Ts) into the same grid but classified with different cloud conditions and surface types. These co-located data sets make intercomparison between the two instruments relatively straightforward. Using this approach, the spatial comparison between the monthly mean AIRS Ts and MODIS IST is in good agreement with RMS 2K for May 2012. This approach also allows the detection of any long-term calibration drift and the careful examination of calibration consistency in the MODIS and AIRS temperature data record. The temporal correlations between temperature data are also compared with those from in-situ measurements from GC-Net (GCN) and NOAA stations. The coherent time series of surface temperature evident in the correlation between AIRS Ts and GCN temperatures suggest that at monthly time scales both observations capture the same climate signal over Greenland. It is also suggested that AIRS surface air temperature (Ta) can be used to estimate the boundary layer inversion.

AIRS↗
NASA NTRS2017

Data Assimilation to Extract Soil Moisture Information From SMAP Observations

Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, they can be used to reduce the need for localized bias correction techniques typically implemented in data assimilation (DA) systems that tend to remove some of the independent information provided by satellite observations. Here, we use a statistical neural network (NN) algorithm to retrieve SMAP (Soil Moisture Active Passive) surface soil moisture estimates in the climatology of the NASA Catchment land surface model. Assimilating these estimates without additional bias correction is found to significantly reduce the model error and increase the temporal correlation against SMAP CalVal in situ observations over the contiguous United States. A comparison with assimilation experiments using traditional bias correction techniques shows that the NN approach better retains the independent information provided by the SMAP observations and thus leads to larger model skill improvements during the assimilation. A comparison with the SMAP Level 4 product shows that the NN approach is able to provide comparable skill improvements and thus represents a viable assimilation approach.

D↗
NASA NTRS2018

Improved Hydrology over Peatlands in a Global Land Modeling System

Peatlands of the Northern Hemisphere represent an important carbon pool that mainly accumulated since the last ice age under permanently wet conditions in specific geological and climatic settings. The carbon balance of peatlands is closely coupled to water table dynamics. Consequently, the future carbon balance over peatlands is strongly dependent on how hydrology in peatlands will react to changing boundary conditions, e.g. due to climate change or regional water level drawdown of connected aquifers or streams. Global land surface modeling over organic-rich regions can provide valuable global-scale insights on where and how peatlands are in transition due to changing boundary conditions. However, the current global land surface models are not able to reproduce typical hydrological dynamics in peatlands well. We implemented specific structural and parametric changes to account for key hydrological characteristics of peatlands into NASA's GEOS-5 Catchment Land Surface Model (CLSM, Koster et al. 2000). The main modifications pertain to the modeling of partial inundation, and the definition of peatland-specific runoff and evapotranspiration schemes. We ran a set of simulations on a high performance cluster using different CLSM configurations and validated the results with a newly compiled global in-situ dataset of water table depths in peatlands. The results demonstrate that an update of soil hydraulic properties for peat soils alone does not improve the performance of CLSM over peatlands. However, structural model changes for peatlands are able to improve the skill metrics for water table depth. The validation results for the water table depth indicate a reduction of the bias from 2.5 to 0.2 m, and an improvement of the temporal correlation coefficient from 0.5 to 0.65, and from 0.4 to 0.55 for the anomalies. Our validation data set includes both bogs (rain-fed) and fens (ground and/or surface water influence) and reveals that the metrics improved less for fens. In addition, a comparison of evapotranspiration and soil moisture estimates over peatlands will be presented, albeit only with limited ground-based validation data. We will discuss strengths and weaknesses of the new model by focusing on time series of specific validation sites.

Bechtold, M.↗
NASA NTRS2017

Scale Size-Dependent Characteristics of the Nightside Aurora

We have determined the spatiotemporal characteristics of the magnetosphere-ionosphere (M-I) coupling using auroral imaging. Observations at fixed positions for an extended period of time are provided by a ground-based all-sky imager measuring the 557.7 nanometer auroral emissions. We report on a single event of nightside aurora (at approximately 22 magnetic local time) preceding a substorm onset. To determine the spatiotemporal characteristics, we perform an innovative analysis of an all-sky imager movie (19 minutes duration, images at 3.31 hertz) that combines a two-dimensional spatial fast Fourier transform with a temporal correlation. We find a scale size-dependent variability where the largest scale sizes are stable on timescales of minutes while the small scale sizes are more variable. When comparing two smaller time intervals of different types of auroral displays, we find a variation in their characteristics. The characteristics averaged over the event are in remarkable agreement with the spatiotemporal characteristics of the nightside field-aligned currents during moderately disturbed times. Thus, two different electrodynamical parameters of the M-I coupling show similar behavior. This gives independent support to the claim of a system behavior that uses repeatable solutions to transfer energy and momentum from the magnetosphere to the ionosphere.

electrodynamical↗
NASA NTRS2014

Evaluation of Temporal Spacing Errors Associated with Interval Management Algorithms

This paper seeks to characterize the temporal spacing errors resulting from the use of Interval Management (IM) algorithms. The focus of the current paper is IM concepts and algorithms that realize a specified temporal spacing between a Target aircraft and an Ownship aircraft at the runway threshold. The paper presents an IM algorithm consisting of the following four modules: (i) Target-Landing-Time Estimation Module, (ii) Ownship-Landing-Time Estimation Module, (iii) Ownship Speed Command Computation Module, and (iv) Ownship Thrust Command Computation Module. The overall guidance module is evaluated on a simulation that models aircraft point-mass dynamics, bank-angle auto-pilot dynamics, pitch-axis auto-pilot dynamics, and engine lag dynamics. The simulation environment also consists of actual atmospheric forecasts and realistic spatio-temporally correlated wind uncertainty models. Results obtained from single case simulation as well as Monte-Carlo simulations are presented in the paper. The modeled scenario consisted of an A320 Target equipped with “Lateral Navigation”/“Vertical Navigation” (LNAV/VNAV) capabilities followed by an A320 Ownship equipped with the IM algorithm. Both aircraft fly the BIGSUR route to SFO airport using a RAP-13 1-hr wind forecast. 500 Monte-Carlo simulations were conducted with realistic wind uncertainty models. The IM algorithm for this case is seen to have a 90% probability landing time error range of 5.9 seconds, compared to the no-IM solution, which has a 90% probability landing time error range of 33.4 seconds.

Bai, Xiaoli↗
NASA NTRS2020

An Observation‐Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model

An empirical model calibration approach is presented that aims to approximate missing biosphere processes in a global land surface model without the need for substantial model structural changes. The strategy is implemented here using the NASA Catchment‐CN land surface model and Moderate Resolution Imaging Spectroradiometer (MODIS) observations of the fraction of absorbed photosynthetically active radiation (FPAR). Existing plant functional types (PFTs) of the Catchment‐CN model are divided into three subtypes, based on the bias between the model‐simulated and MODIS‐observed FPAR. Separate sets of vegetation parameters for each subtype are then calibrated at a small number of grid cells with homogeneous, single‐PFT land cover, using MODIS FPAR reference observations from 2003 to 2009. The effectiveness of the empirical approach at improving the realism of modeled vegetation dynamics is investigated with two global model simulations for the period 2010–2016, one using the newly calibrated parameter values and the other using the original values. Globally, the calibrated parameters reduce the root mean square error (RMSE) of the modeled FPAR with respect to MODIS by 0.029 (∼10%) on average. In some regions, substantially larger RMSE reductions are achieved. RMSE reductions are primarily driven by model bias reductions, with neutral effects on the temporal correlation skill. While the empirical approach is suitable for achieving consistent model improvements, it is shown to be sensitive to the characteristics of the model error, specifically a dominance of the bias component in the case of Catchment‐CN. Ultimately, more fundamental model structural changes may be required to achieve better improvements.

J. Kolassa↗