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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.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Residual Tumor After Neoadjuvant Chemoradiation Outside the Radiation Therapy Target Volume: A New Prognostic Factor for Survival in Esophageal Cancer

Purpose/Objective(s): The aim of this study was to analyze the accuracy of gross tumor volume (GTV) delineation and clinical target volume (CTV) margins for neoadjuvant chemoradiation therapy (neo-CRT) in esophageal carcinoma at pathologic examination and to determine the impact on survival. Methods and Materials: The study population consisted of 63 esophageal cancer patients treated with neo-CRT. GTV and CTV borders were demarcated in situ during surgery on the esophagus, using anatomical reference points to provide accurate information regarding tumor location at pathologic evaluation. To identify prognostic factors for disease-free survival (DFS) and overall survival (OS), a Cox regression analysis was performed. Results: After resection, macroscopic residual tumor was found outside the GTV in 7 patients (11%). Microscopic residual tumor was located outside the CTV in 9 patients (14%). The median follow-up was 15.6 months. With multivariate analysis, only microscopic tumor outside the CTV (hazard ratio [HR], 4.96; 95% confidence interval [CI], 1.03-15.36), and perineural growth (HR, 5.77; 95% CI, 1.27-26.13) were identified as independent prognostic factors for OS. The 1-year OS was 20% for patients with tumor outside the CTV and 86% for those without (P<.01). For DFS, microscopic tumor outside the CTV (HR, 5.92; 95% CI, 1.89-18.54) and ypN+ (HR, 3.36; 95% CI, 1.33-8.48) were identified as independent adverse prognostic factors. The 1-year DFS was 23% versus 77% for patients with or without tumor outside the CTV (P<.01). Conclusions: Microscopic tumor outside the CTV is associated with markedly worse OS after neo-CRT. This may either stress the importance of accurate tumor delineation or reflect aggressive tumor behavior requiring new adjuvant treatment modalities.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer

Purpose: To determine whether pretreatment CT texture features can improve patient risk stratification beyond conventional prognostic factors (CPFs) in stage III non-small cell lung cancer (NSCLC). Methods and Materials: We retrospectively reviewed 91 cases with stage III NSCLC treated with definitive chemoradiation therapy. All patients underwent pretreatment diagnostic contrast enhanced computed tomography (CE-CT) followed by 4-dimensional CT (4D-CT) for treatment simulation. We used the average-CT and expiratory (T50-CT) images from the 4D-CT along with the CE-CT for texture extraction. Histogram, gradient, co-occurrence, gray tone difference, and filtration-based techniques were used for texture feature extraction. Penalized Cox regression implementing cross-validation was used for covariate selection and modeling. Models incorporating texture features from the 33 image types and CPFs were compared to those with models incorporating CPFs alone for overall survival (OS), local-regional control (LRC), and freedom from distant metastases (FFDM). Predictive Kaplan-Meier curves were generated using leave-one-out cross-validation. Patients were stratified based on whether their predicted outcome was above or below the median. Reproducibility of texture features was evaluated using test-retest scans from independent patients and quantified using concordance correlation coefficients (CCC). We compared models incorporating the reproducibility seen on test-retest scans to our original models and determined the classification reproducibility. Results: Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone for OS (P=.046), LRC (P=.01), and FFDM (P=.005). The average CCCs were 0.89, 0.91, and 0.67 for texture features extracted from the average-CT, T50-CT, and CE-CT, respectively. Incorporating reproducibility within our models yielded 80.4% (±3.7% SD), 78.3% (±4.0% SD), and 78.8% (±3.9% SD) classification reproducibility in terms of OS, LRC, and FFDM, respectively. Conclusions: Pretreatment tumor texture may provide prognostic information beyond that obtained from CPFs. Models incorporating feature reproducibility achieved classification rates of ∼80%. External validation would be required to establish texture as a prognostic factor.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Radiation Therapy Overcomes Adverse Prognostic Role of Cyclooxygenase-2 Expression on Reed-Sternberg Cells in Early Hodgkin Lymphoma

Purpose: To analyze the role of radiation therapy (RT) on the adverse prognostic influence of cyclooxygenase-2 (COX-2) expression on Reed-Sternberg (RS) cells, in the setting of early Hodgkin lymphoma (HL) treated with ABVD (adriamycin, vinblastine, bleomycin, dacarbazine). Methods and Materials: In the present study we retrospectively investigated the prognostic value of COX-2 expression in a large (n=143), uniformly treated early HL population from the Spanish Network of HL using tissue microarrays. Univariate and multivariate analyses were done, including the most recognized clinical variables and the potential role of administration of adjuvant RT. Results: Median age was 31 years; the expression of COX-2 defined a subgroup with significantly worse prognosis. Considering COX-2{sup +} patients, those who received RT had significantly better 5-year progression-free survival (PFS) (80% vs 54% if no RT; P=.008). In contrast, COX-2{sup −} patients only had a modest, nonsignificant benefit from RT in terms of 5-year PFS (90% vs 79%; P=.13). When we compared the outcome of patients receiving RT considering the expression of COX-2 on RS cells, we found a nonsignificant 10% difference in terms of PFS between COX-2{sup +} and COX-2{sup −} patients (P=.09), whereas the difference between the 2 groups was important (25%) in patients not receiving RT (P=.04). Conclusions: Cyclooxygenase-2 RS cell expression is an adverse independent prognostic factor in early HL. Radiation therapy overcomes the worse prognosis associated with COX-2 expression on RS cells, acting in a chemotherapy-independent way. Cyclooxygenase-2 RS cell expression may be useful for determining patient candidates with early HL to receive consolidation with RT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Metabolic Tumor Volume as a Prognostic Imaging-Based Biomarker for Head-and-Neck Cancer: Pilot Results From Radiation Therapy Oncology Group Protocol 0522

Purpose: To evaluate candidate fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) imaging biomarkers for head-and-neck chemoradiotherapy outcomes in the cooperative group trial setting. Methods and Materials: Radiation Therapy Oncology Group (RTOG) protocol 0522 patients consenting to a secondary FDG-PET/CT substudy were serially imaged at baseline and 8 weeks after radiation. Maximum standardized uptake value (SUVmax), SUV peak (mean SUV within a 1-cm sphere centered on SUVmax), and metabolic tumor volume (MTV) using 40% of SUVmax as threshold were obtained from primary tumor and involved nodes. Results: Of 940 patients entered onto RTOG 0522, 74 were analyzable for this substudy. Neither high baseline SUVmax nor SUVpeak from primary or nodal disease were associated with poor treatment outcomes. However, primary tumor MTV above the cohort median was associated with worse local-regional control (hazard ratio 4.01, 95% confidence interval 1.28-12.52, P=.02) and progression-free survival (hazard ratio 2.34, 95% confidence interval 1.02-5.37, P=.05). Although MTV and T stage seemed to correlate (mean MTV 6.4, 13.2, and 26.8 for T2, T3, and T4 tumors, respectively), MTV remained a strong independent prognostic factor for progression-free survival in bivariate analysis that included T stage. Primary MTV remained prognostic in p16-associated oropharyngeal cancer cases, although sample size was limited. Conclusion: High baseline primary tumor MTV was associated with worse treatment outcomes in this limited patient subset of RTOG 0522. Additional confirmatory work will be required to validate primary tumor MTV as a prognostic imaging biomarker for patient stratification in future trials.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Predicting Overall Survival After Stereotactic Ablative Radiation Therapy in Early-Stage Lung Cancer: Development and External Validation of the Amsterdam Prognostic Model

Purpose: A prognostic model for 5-year overall survival (OS), consisting of recursive partitioning analysis (RPA) and a nomogram, was developed for patients with early-stage non-small cell lung cancer (ES-NSCLC) treated with stereotactic ablative radiation therapy (SABR). Methods and Materials: A primary dataset of 703 ES-NSCLC SABR patients was randomly divided into a training (67%) and an internal validation (33%) dataset. In the former group, 21 unique parameters consisting of patient, treatment, and tumor factors were entered into an RPA model to predict OS. Univariate and multivariate models were constructed for RPA-selected factors to evaluate their relationship with OS. A nomogram for OS was constructed based on factors significant in multivariate modeling and validated with calibration plots. Both the RPA and the nomogram were externally validated in independent surgical (n=193) and SABR (n=543) datasets. Results: RPA identified 2 distinct risk classes based on tumor diameter, age, World Health Organization performance status (PS) and Charlson comorbidity index. This RPA had moderate discrimination in SABR datasets (c-index range: 0.52-0.60) but was of limited value in the surgical validation cohort. The nomogram predicting OS included smoking history in addition to RPA-identified factors. In contrast to RPA, validation of the nomogram performed well in internal validation (r{sup 2}=0.97) and external SABR (r{sup 2}=0.79) and surgical cohorts (r{sup 2}=0.91). Conclusions: The Amsterdam prognostic model is the first externally validated prognostication tool for OS in ES-NSCLC treated with SABR available to individualize patient decision making. The nomogram retained strong performance across surgical and SABR external validation datasets. RPA performance was poor in surgical patients, suggesting that 2 different distinct patient populations are being treated with these 2 effective modalities.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Prognostic Relevance of HPV Infection and p16 Overexpression in Squamous Cell Anal Cancer

Purpose: Human papillomavirus (HPV) DNA and p16 status have both been reported as prognostic factors in anal cancer, but the prognostic relevance of combined detection and particularly HPV−/p16+ and HPV+/p16− signatures is unknown. We evaluated combined HPV DNA and p16 status as a prognostic factor of treatment response in anal cancer. Methods: 106 patients treated with radiochemotherapy (RCT+5-FU/MMC) with available paraffin-embedded tumor tissue specimens were evaluated regarding local control (LC) and overall survival (OS) at 5 years. In addition to HPV DNA/p16 status, the influence of age, gender, previous surgery, initial recurrence, T stage, N status, and tumor localization was analyzed. Results: 63 patients were HPV+/p16+, 9 were HPV+/p16−, 11 were HPV−/p16+, and 23 were HPV−/p16−. In univariate analysis, LC was significantly better in patients with T1/2 stage, female gender, and HPV/p16 status. HPV+/p16+ was associated with significantly better LC (88.1%; 95% confidence interval [CI]: 78.89-97.31) compared with HPV−/p16+ (63.6%; 95% CI: 35.18-92.02; P=.021) and especially HPV−/p16− (55.8%; 95% CI: 33.46-78.14; P=.002) but not with HPV+/p16− (77.8%; 95% CI: 50.56-105.04; P=.270). OS was influenced by T stage and LC. HPV+/p16+ patients showed a trend toward better OS compared with HPV−/p16− patients (HPV+/p16+: 81.1%; 95% CI: 70.12-92.08 vs HPV−/p16−: 68.8%; 95%CI: 47.44-90.16; P=.138). On multivariate analysis, T3/4 stage and HPV/p16 status (HPV−/p16+, HPV−/p16− vs HPV+/p16+) predicted poorer LC (T3/4: 50.3% vs T1/2: 86.6%, hazard ratio [HR] 0.22; 95% CI: 0.09-0.53; P<.001; HPV+/p16+ vs HPV−/p16+: HR 4.73; 95% CI: 1.33-16.82; P=.016, and HPV+/p16+ vs HPV−/p16−: HR 6.40; 95% CI: 2.23-18.35; P<.001), whereas local relapse dramatically influenced OS. Conclusion: Our data suggest that HPV/p16 signature determines prognosis. HPV+/p16+ patients had the best prognosis, and HPV−/p16+ and HPV−/p16− patients showed the worst outcome and therefore require therapy optimization, particularly given that LC is the most important factor for OS.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Delineating transcriptional networks of prognostic gene signatures refines treatment recommendations for lymph node-negative breast cancer patients

The majority of women diagnosed with lymph node-negative breast cancer are unnecessarily treated with damaging chemotherapeutics after surgical resection. This highlights the importance of understanding and more accurately predicting patient prognosis. We define the transcriptional networks regulating well-established prognostic gene expression signatures. We find that the same set of transcriptional regulators consistently lie upstream of both ‘prognosis’ and ‘proliferation’ gene signatures, suggesting that a central transcriptional network underpins a shared phenotype within these signatures. Strikingly, the master transcriptional regulators within this network predict recurrence risk for lymph node-negative breast cancer better than currently used multigene prognostic assays, particularly in estrogen receptor-positive patients. Simultaneous examination of p16 INK4A expression, which predicts tumours that have bypassed cellular senescence, revealed that intermediate levels of p16 INK4A correlate with an intact pRB pathway and improved survival. A combination of these master transcriptional regulators and p16 INK4A , termed the OncoMasTR score, stratifies tumours based on their proliferative and senescence capacity, facilitating a clearer delineation of lymph node-negative breast cancer patients at high risk of recurrence, and thus requiring chemotherapy. Furthermore, OncoMasTR accurately classifies over 60% of patients as ‘low risk’, an improvement on existing prognostic assays, which has the potential to reduce overtreatment in early-stage patients. Taken together, the present study provides new insights into the transcriptional regulation of cellular proliferation in breast cancer and provides an opportunity to enhance and streamline methods of predicting breast cancer prognosis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Online Monitoring and Prognostics for Passive Components in Nuclear Power Plants

This paper describes research towards developing prognostics technologies for light-water nuclear power reactors components. The focus of this paper is on passive components (those that do not need to change state or move to perform their function), though the technologies are applicable to other classes of components as well. A prototypic failure mechanism (high-cycle fatigue) is used to focus the efforts and provide context for the development effort. A Bayesian framework is proposed for the prognostics of remaining useful life, and applied to simulated data sets representing nondestructive measurements of high-cycle fatigue damage. Initial results of prognostics, based on simulated data sets are presented.

Ramuhalli, Pradeep↗
DOE OSTI2015

Prognostic significance of FAM83D gene expression across human cancer types

The family with sequence similarity 83, member D (FAM83D) gene has been proposed as a new prognostic marker for breast cancer. In this work, we further evaluate the prognostic significance of FAM83D expression in different breast cancer subtypes using a meta-analysis. Patients with higher FAM83D mRNA levels have significantly decreased overall and metastatic relapse-free survival, particularly in the group of patients with ER-positive, or luminal subtype tumors. We also assessed FAM83D alterations and its prognostic significance across 22 human cancer types using The Cancer Genome Atlas (TCGA). FAM83D is frequently gained in the majority of human cancer types, resulting in the elevated expression of FAM83D. Higher levels of FAM83D mRNA expression are significantly associated with decreased overall survival in several cancer types. Finally, we demonstrate that TP53 mutation in human cancers is coupled to a significant increase in the expression of FAM83D, and that a higher level of FAM83D expression is positively correlated with an increase in genome instability in many cancer types. These results identify FAM83D as a potential novel oncogene across multiple human cancer types.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Diagnostics, Prognostics, and Optimization for Lithium-Ion Battery Systems

Health management of lithium-ion battery systems presents a host of challenges due to their complex physics, large numbers of components, and a wide variety of degradation behaviors across different battery types. Dr. Paul Gasper will present on research from the Electrochemical Energy Storage Group on Lithium-ion battery diagnostics, prognostics, and optimization. Diagnostics research, including state-estimation via machine-learning from electrochemical impedance spectroscopy and DC pulses as well as continuous state-estimation via Kalman filters, will highlight the ongoing challenges for accurately measuring the state of batteries without performing time-consuming characterization tests. NLR's industry-recognized battery prognostics work, which predicts real-world battery degradation by identifying degradation rate models from accelerated aging data using statistical modeling and machine-learning, will be used to demonstrate the critical impact of battery controls, thermal management, and operating strategy on durability and lifetime. Finally, the use of prognostic models for financial or lifetime optimization will be discussed.

25 ENERGY STORAGE↗
NASA NTRS1993

Development and analysis of prognostic equations for mesoscale kinetic energy and mesoscale (subgrid scale) fluxes for large-scale atmospheric models

Generated by landscape discontinuities (e.g., sea breezes) mesoscale circulation processes are not represented in large-scale atmospheric models (e.g., general circulation models), which have an inappropiate grid-scale resolution. With the assumption that atmospheric variables can be separated into large scale, mesoscale, and turbulent scale, a set of prognostic equations applicable in large-scale atmospheric models for momentum, temperature, moisture, and any other gaseous or aerosol material, which includes both mesoscale and turbulent fluxes is developed. Prognostic equations are also developed for these mesoscale fluxes, which indicate a closure problem and, therefore, require a parameterization. For this purpose, the mean mesoscale kinetic energy (MKE) per unit of mass is used, defined as E-tilde = 0.5 (the mean value of u'(sub i exp 2), where u'(sub i) represents the three Cartesian components of a mesoscale circulation (the angle bracket symbol is the grid-scale, horizontal averaging operator in the large-scale model, and a tilde indicates a corresponding large-scale mean value). A prognostic equation is developed for E-tilde, and an analysis of the different terms of this equation indicates that the mesoscale vertical heat flux, the mesoscale pressure correlation, and the interaction between turbulence and mesoscale perturbations are the major terms that affect the time tendency of E-tilde. A-state-of-the-art mesoscale atmospheric model is used to investigate the relationship between MKE, landscape discontinuities (as characterized by the spatial distribution of heat fluxes at the earth's surface), and mesoscale sensible and latent heat fluxes in the atmosphere. MKE is compared with turbulence kinetic energy to illustrate the importance of mesoscale processes as compared to turbulent processes. This analysis emphasizes the potential use of MKE to bridge between landscape discontinuities and mesoscale fluxes and, therefore, to parameterize mesoscale fluxes generated by such subgrid-scale landscape discontinuities in large-scale atmospheric models.

Avissar, Roni↗
NASA NTRS2009

Development of an On-board Failure Diagnostics and Prognostics System for Solid Rocket Booster

We develop a case breach model for the on-board fault diagnostics and prognostics system for subscale solid-rocket boosters (SRBs). The model development was motivated by recent ground firing tests, in which a deviation of measured time-traces from the predicted time-series was observed. A modified model takes into account the nozzle ablation, including the effect of roughness of the nozzle surface, the geometry of the fault, and erosion and burning of the walls of the hole in the metal case. The derived low-dimensional performance model (LDPM) of the fault can reproduce the observed time-series data very well. To verify the performance of the LDPM we build a FLUENT model of the case breach fault and demonstrate a good agreement between theoretical predictions based on the analytical solution of the model equations and the results of the FLUENT simulations. We then incorporate the derived LDPM into an inferential Bayesian framework and verify performance of the Bayesian algorithm for the diagnostics and prognostics of the case breach fault. It is shown that the obtained LDPM allows one to track parameters of the SRB during the flight in real time, to diagnose case breach fault, and to predict its values in the future. The application of the method to fault diagnostics and prognostics (FD&P) of other SRB faults modes is discussed.

Smelyanskiy, Vadim N.↗
NASA NTRS2009

Communication Optimizations for a Wireless Distributed Prognostic Framework

Distributed architecture for prognostics is an essential step in prognostic research in order to enable feasible real-time system health management. Communication overhead is an important design problem for such systems. In this paper we focus on communication issues faced in the distributed implementation of an important class of algorithms for prognostics - particle filters. In spite of being computation and memory intensive, particle filters lend well to distributed implementation except for one significant step - resampling. We propose new resampling scheme called parameterized resampling that attempts to reduce communication between collaborating nodes in a distributed wireless sensor network. Analysis and comparison with relevant resampling schemes is also presented. A battery health management system is used as a target application. A new resampling scheme for distributed implementation of particle filters has been discussed in this paper. Analysis and comparison of this new scheme with existing resampling schemes in the context for minimizing communication overhead have also been discussed. Our proposed new resampling scheme performs significantly better compared to other schemes by attempting to reduce both the communication message length as well as number total communication messages exchanged while not compromising prediction accuracy and precision. Future work will explore the effects of the new resampling scheme in the overall computational performance of the whole system as well as full implementation of the new schemes on the Sun SPOT devices. Exploring different network architectures for efficient communication is an importance future research direction as well.

Saha, Sankalita↗
NASA NTRS2011

Model Adaptation for Prognostics in a Particle Filtering Framework

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

Saha, Bhaskar↗
NASA NTRS2011

Towards Prognostics of Electrolytic Capacitors

A remaining useful life prediction algorithm and degradation model for electrolytic capacitors is presented. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management research. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. In particular, experimental results of an accelerated aging test under electrical stresses are presented. The capacitors used in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors.

Celaya, Jose R.↗
NASA NTRS2012

An Integrated Approach for Gear Health Prognostics

In this paper, an integrated approach for gear health prognostics using particle filters is presented. The presented method effectively addresses the issues in applying particle filters to gear health prognostics by integrating several new components into a particle filter: (1) data mining based techniques to effectively define the degradation state transition and measurement functions using a one-dimensional health index obtained by whitening transform; (2) an unbiased l-step ahead RUL estimator updated with measurement errors. The feasibility of the presented prognostics method is validated using data from a spiral bevel gear case study.

He, David↗
NASA NTRS2014

Application of Model-based Prognostics to a Pneumatic Valves Testbed

Pneumatic-actuated valves play an important role in many applications, including cryogenic propellant loading for space operations. Model-based prognostics emphasizes the importance of a model that describes the nominal and faulty behavior of a system, and how faulty behavior progresses in time, causing the end of useful life of the system. We describe the construction of a testbed consisting of a pneumatic valve that allows the injection of faulty behavior and controllable fault progression. The valve opens discretely, and is controlled through a solenoid valve. Controllable leaks of pneumatic gas in the testbed are introduced through proportional valves, allowing the testing and validation of prognostics algorithms for pneumatic valves. A new valve prognostics approach is developed that estimates fault progression and predicts remaining life based only on valve timing measurements. Simulation experiments demonstrate and validate the approach.

prediction↗
NASA NTRS2015

A Linearized Prognostic Cloud Scheme in NASAs Goddard Earth Observing System Data Assimilation Tools

A linearized prognostic cloud scheme has been developed to accompany the linearized convection scheme recently implemented in NASA's Goddard Earth Observing System data assimilation tools. The linearization, developed from the nonlinear cloud scheme, treats cloud variables prognostically so they are subject to linearized advection, diffusion, generation, and evaporation. Four linearized cloud variables are modeled, the ice and water phases of clouds generated by large-scale condensation and, separately, by detraining convection. For each species the scheme models their sources, sublimation, evaporation, and autoconversion. Large-scale, anvil and convective species of precipitation are modeled and evaporated. The cloud scheme exhibits linearity and realistic perturbation growth, except around the generation of clouds through large-scale condensation. Discontinuities and steep gradients are widely used here and severe problems occur in the calculation of cloud fraction. For data assimilation applications this poor behavior is controlled by replacing this part of the scheme with a perturbation model. For observation impacts, where efficiency is less of a concern, a filtering is developed that examines the Jacobian. The replacement scheme is only invoked if Jacobian elements or eigenvalues violate a series of tuned constants. The linearized prognostic cloud scheme is tested by comparing the linear and nonlinear perturbation trajectories for 6-, 12-, and 24-h forecast times. The tangent linear model performs well and perturbations of clouds are well captured for the lead times of interest.

Precipitation↗