ALBERT

Description: Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems, and human communities, worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean-land-atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "Big Data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically-based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.

Description: Describing the nature and variability of Indian monsoon rainfall extremes is a topic of much debate in the current literature. We suggest the use of a generalized linear mixed model (GLMM), specifically, the logit-normal mixed model, to describe the underlying structure of this complex climatic event. Several GLMM algorithms are described and simulations are performed to vet these algorithms before applying them to the Indian precipitation data procured from the National Climatic Data Center. The logit-normal model was applied with fixed covariates of latitude, longitude, elevation, daily minimum and maximum temperatures with a random intercept by weather station. In general, the estimation methods concurred in their suggestion of a relationship between the El Niño Southern Oscillation (ENSO) and extreme rainfall variability estimates. This work provides a valuable starting point for extending GLMM to incorporate the intricate dependencies in extreme climate events.

Description: Exponential family statistical distributions, including the well-known Normal, Binomial, Poisson, and exponential distributions, are overwhelmingly used in data analysis. In the presence of covariates, an exponential family distributional assumption for the response random variables results in a generalized linear model. However, it is rarely ensured that the parameters of the assumed distributions are stable through the entire duration of data collection process. A failure of stability leads to nonsmoothness and nonlinearity in the physical processes that drive the data under. In this paper, we propose testing for stability of parameters of exponential family distributions and generalized linear models. A rejection of the hypothesis of stable parameters leads to change detection. We derive the related likelihood ratio test statistic. We compare the performance of this test statistic to the popular Normal distributional assumption dependent cumulative sum (Gaussian-CUSUM) statistic in change detection problems. We study Atlantic tropical storms using the techniques developed here, to understand whether the nature of these tropical storms has remained stable over the last few decades.

Description: Exponential family statistical distributions, including the well-known normal, binomial, Poisson, and exponential distributions, are overwhelmingly used in data analysis. In the presence of covariates, an exponential family distributional assumption for the response random variables results in a generalized linear model. However, it is rarely ensured that the parameters of the assumed distributions are stable through the entire duration of the data collection process. A failure of stability leads to nonsmoothness and nonlinearity in the physical processes that result in the data. In this paper, we propose testing for stability of parameters of exponential family distributions and generalized linear models. A rejection of the hypothesis of stable parameters leads to change detection. We derive the related likelihood ratio test statistic. We compare the performance of this test statistic to the popular normal distributional assumption dependent cumulative sum (Gaussian CUSUM) statistic in change detection problems. We study Atlantic tropical storms using the techniques developed here, so to understand whether the nature of these tropical storms has remained stable over the last few decades.

Description: Describing the nature and variability of Indian monsoon precipitation is a topic of much debate in the current literature. We suggest the use of a generalized linear mixed model (GLMM), specifically, the logit-normal mixed model, to describe the underlying structure of this complex climatic event. Four GLMM algorithms are described and simulations are performed to vet these algorithms before applying them to the Indian precipitation data. The logit-normal model was applied to light, moderate, and extreme rainfall. Findings indicated that physical constructs were preserved by the models, and random effects were significant in many cases. We also found GLMM estimation methods were sensitive to tuning parameters and assumptions and therefore, recommend use of multiple methods in applications. This work provides a novel use of GLMM and promotes its addition to the gamut of tools for analysis in studying climate phenomena.

Description: Multistation observations of ionosphere scintillation at VHF (250 MHz) and GNSS L1 frequency from three locations – (i) Bokkhali (BOK) (geographic 21.6° N, 88.2° E, dip 31.48°, (ii) Raja Peary Mohan College Centre (RPMC) (geographic 22.66° N, 88.4° E, dip 33.5°) and (iii) Krishnath College Centre (KNC), Berhampore (geographic 24.1° N, 88.3° E, dip 35.9°) – at ~ 1° latitudinal separations near the northern crest of the equatorial ionization anomaly (EIA) of the Indian longitude sector are investigated in conjunction with total electron content (TEC) data and available ionosonde data near the magnetic equator to study fine structure in spatial and temporal variability patterns of scintillation occurrences. The observations are carried out in the autumnal equinoctial months of a high solar activity year (2011). In spite of smaller latitudinal/spatial separation among the observing stations, conspicuous differences are reflected in the onset time, duration, fade rate and fade depth of VHF scintillations as well as in spectral features. Scintillations are mostly associated with depletion in TEC around the anomaly crest and occurrence of ESF near the magnetic equator at an earlier time. Not only the strength of EIA, but also the locations of observing stations with respect to the post-sunset resurgence peak of EIA seem to play dominant role in dictating the severity of scintillation activity. A secondary enhancement in diurnal TEC in the post-sunset period seems to accentuate the irregularity activities near the anomaly crest, and a threshold value of the same may fruitfully be utilized for the prediction of scintillation around the locations. An idea regarding latitudinal extent of scintillation is developed by considering observations at L1 frequency from the GPS and GLONASS constellation of satellites. A critical value of h'F near the magnetic equator for the occurrence of simultaneous scintillation at the three centres is suggested. The observations are discussed considering electrodynamical aspect of equatorial irregularities.