Search arXiv⌕ Search

arXiv · physics/0604022

A Qualitative Description of Boundary Layer Wind Speed Records

Abstract

The complexity of the atmosphere endows it with the property of turbulence by virtue of which, wind speed variations in the atmospheric boundary layer (ABL) exhibit highly irregular fluctuations that persist over a wide range of temporal and spatial scales. Despite the large and significant body of work on microscale turbulence, understanding the statistics of atmospheric wind speed variations has proved to be elusive and challenging. Knowledge about the nature of wind speed at ABL has far reaching impact on several fields of research such as meteorology, hydrology, agriculture, pollutant dispersion, and more importantly wind energy generation. In the present study, temporal wind speed records from twenty eight stations distributed through out the state of North Dakota (ND, USA), ($\sim$ 70,000 square-miles) and spanning a period of nearly eight years are analyzed. We show that these records exhibit a characteristic broad multifractal spectrum irrespective of the geographical location and topography. The rapid progression of air masses with distinct qualitative characteristics originating from Polar regions, Gulf of Mexico and Northern Pacific account for irregular changes in the local weather system in ND. We hypothesize that one of the primary reasons for the observed multifractal structure could be the irregular recurrence and confluence of these three air masses.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rajesh G. Kavasseri †~, Radhakrishnan Nagarajan. 2006-04-04. A Qualitative Description of Boundary Layer Wind Speed Records. https://arxiv.org/abs/physics/0604022

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A Land-Sea Contrast Pattern in Surface Temperature and Atmospheric Circulation Trends in Recent Decades

We argue land-sea contrast is an important driver of spatial patterns of observed surface temperature and atmospheric circulation trends, including the negative Inter-Decadal Pacific Oscillation (IPO)-like tendency across the Pacific basin. Observed and modeled trends display an overall decline in sea level pressure over the faster-warming land, with a spatial pattern that resembles the seasonal cycle and modeled response to land heating. Coupled historical forcing climate model simulations underestimate the land-sea warming ratio. It is only in the early response of abrupt CO2 quadrupling climate model simulations that climate models are able to recreate the observed land-sea warming ratio, in which case a negative IPO-like sea level pressure trend and surface warming pattern over the Pacific comparable to observed trends are seen. We propose that some discrepancies between observed trends and those predicted by climate models are explained by the underestimation of the land-sea warming ratio by climate models.

physics.ao-ph↗

Distinguishing Initial-Condition and Model Error Using Machine-Learning Weather Models

Differentiable weather models allow atmospheric initial conditions to be directly optimized to shadow a known trajectory, which we take from a reanalysis dataset. Here, we extend this optimization to include the forecast model, GraphCast, using low-rank adaptation (LoRA). We compare model optimization with initial-condition (IC) optimization, and then joint optimization, where both are tuned simultaneously. When compared against a control forecast initialized with reanalysis data, LoRA and IC optimization individually produce similar improvements. We find similar results when IC and LoRA are jointly optimized across a spectrum of solutions where a tuning coefficient smoothly shifts the weight of the joint optimization from all-LoRA to all-IC; forecast improvement is not unique to the relative contributions of model and IC corrections. To better discriminate the source of error, we introduce a second model, Pangu-Weather, as a supervisor that gates IC updates during joint optimization. This gated optimization is the same as before, except that IC updates at each epoch are accepted only when they improve the forecasts of both models; otherwise, only the LoRA update is accepted. Results show that this cross-model constraint substantially improves agreement between the two models in their response to the same optimized ICs, narrowing the gap in mean forecast error reduction from 64% to 10%. Moreover, when used together, the LoRA and IC corrections from the gated optimization yield GraphCast forecast improvements similar to those from the ungated optimization. These results imply that model error that is encoded in the ungated ICs is shifted to the LoRA corrections via gating, yielding IC corrections that may provide better analysis estimates.

physics.ao-ph↗

Estimating ensemble-mean anomaly correlations and the ratio of predictable components using an unbiased estimate of the predictable signal variance

The correlation ($r$) between ensemble mean forecasts and observations is a frequently-used metric of forecast quality in the weather and climate prediction communities. However, uncorrected estimates of $r$ are biased towards zero at finite ensemble size due to the influence of unpredictable noise that contaminates the sample ensemble mean. This note summarises established ensemble-size adjustment methods and characterises the behaviour of $r_{\infty}$, a correlation estimator that is constructed from an unbiased and ensemble-size-independent estimate of the signal variance. The only requirement is that forecast members are exchangeable with one another such that they can be interpreted as independent draws from a case-specific predictive distribution. The estimator $r_{\infty}$ does not require forecasts and observations to be statistically exchangeable, only that the unpredictable component of each member is uncorrelated with the observed truth, and thus works for both reliable and unreliable forecasts. In some situations, $r_{\infty}$ may be undefined (i.e. the estimated signal variance is negative) or fall outside the admissible range $-1 \leq r_{\infty} \leq 1$ when the predictable signal is small relative to the sampling uncertainty. These inadmissible values can be interpreted as evidence that the true predictable signal cannot be resolved with the available ensemble size and number of cases. The same unbiased signal variance can also be used to estimate the correlation between the predictable signal and individual members to yield an ensemble-size-invariant estimator of the ratio of predictable components (RPC). In contrast, variance-based RPC estimates, including forms that correct only the model predictable component, are biased low by a factor that depends only on the ensemble size and model signal-to-noise ratio.

physics.ao-ph↗