arXiv subjects

William R. Boos

Publications and source records attributed to William R. Boos.

7 recordsLinked to original sources

arXiv2026

Designing probabilistic AI monsoon forecasts to inform agricultural decision-making

Hundreds of millions of farmers make high-stakes decisions under uncertainty about future weather. Forecasts can inform these decisions, but available choices and their risks and benefits vary between farmers. We introduce a decision-theory framework for designing useful forecasts in settings where the forecaster cannot prescribe optimal actions because farmers' circumstances are heterogeneous. We apply this framework to the case of seasonal onset of monsoon rains, a key date for planting decisions and agricultural investments in many tropical countries. We develop a system for tailoring forecasts to the requirements of this framework by blending systematically benchmarked artificial intelligence (AI) weather prediction models with a new "evolving farmer expectations" statistical model. This statistical model applies Bayesian inference to historical observations to predict time-varying probabilities of first-occurrence events throughout a season. The blended system yields more skillful Indian monsoon forecasts at longer lead times than its components or any multi-model average. In 2025, this system was deployed operationally in a government-led program that delivered subseasonal monsoon onset forecasts to 38 million Indian farmers, skillfully predicting that year's early-summer anomalous dry period. This decision-theory framework and blending system offer a pathway for developing climate adaptation tools for large vulnerable populations around the world.

cs.LG↗

arXiv2026

Decision-oriented benchmarking to transform AI weather forecast access: Application to the Indian monsoon

Artificial intelligence weather prediction (AIWP) models now often outperform traditional physics-based models on common metrics while requiring orders-of-magnitude less computing resources and time. Open-access AIWP models thus hold promise as transformational tools for helping low- and middle-income populations make decisions in the face of high-impact weather shocks. Yet, current approaches to evaluating AIWP models focus mainly on aggregated meteorological metrics without considering local stakeholders' needs in decision-oriented, operational frameworks. Here, we introduce such a framework that connects meteorology, AI, and social sciences. As an example, we apply it to the 150-year-old problem of Indian monsoon forecasting, focusing on benefits to rain-fed agriculture, which is highly susceptible to climate change. AIWP models skillfully predict an agriculturally relevant onset index at regional scales weeks in advance when evaluated out-of-sample using deterministic and probabilistic metrics. This framework informed a government-led effort in 2025 to send 38 million Indian farmers AI-based monsoon onset forecasts, which captured an unusual weeks-long pause in monsoon progression. This decision-oriented benchmarking framework provides a key component of a blueprint for harnessing the power of AIWP models to help large vulnerable populations adapt to weather shocks in the face of climate variability and change.

cs.LG↗

arXiv2025

Constraining Atmospheric River Uncertainty Using Instantaneous Poleward Latent Heat Transport

Atmospheric rivers (ARs) are extreme weather events that play a crucial role in the global hydrological cycle. As a key mechanism of latent heat transport (LHT), they help maintain energy balance in the climate system. While an AR is characterized by a long, narrow corridor of water vapor associated with a low-level jet stream, there is no unambiguous definition of an AR grounded in geophysical fluid dynamics. AR identification is currently performed by a variety of threshold-based algorithms, which has introduced uncertainty in the estimated contribution of ARs to LHT. We calculate the instantaneous eddy LHT from moist, poleward anomalies. Based on the dynamics of the large-scale atmospheric circulation, this quantity is a physics-based upper bound that constrains AR projections from the variety of detection algorithms. We quantify the contribution of ARs to transient eddies, stationary eddies, and transient-stationary eddy interactions, and we show the relative contributions of ARs vs. other processes, such as dry, equatorward transport. We use this upper bound as a reference to quantify ARs' frequency, intensity, and temporal variability. In the historical climate, the AR reference transport is ~2.21 PW at the latitude of peak transport in Northern Hemisphere winter, with a temporal standard deviation of approximately 0.47 PW. In a future climate projection, at this latitude, AR-induced LHT will increase by 0.5 PW and the corresponding temporal variability will increase by 0.14 PW. The future change in the AR reference transport is larger than the changes associated with other types of atmospheric anomalies.

physics.ao-ph↗

arXiv2022

Wrangler for the Emergency Events Database: A Tool for Geocoding and Analysis of a Global Disaster Dataset

There is an increasing need for precise location information on historical disasters, such as mass casualty events caused by weather or earthquakes, but existing disaster datasets often do not provide geographic coordinates of past events. Here we describe a new tool, the Wrangler for the Emergency Events Database (WEED), that associates latitude and longitude coordinates with entries in the widely used Emergency Events Database (EM-DAT). WEED takes as input records from EM-DAT, and geocodes the list of cities, states, and other location types associated with a given disaster using the R language with the GeoNames web service. Error processing is performed, and users are given the ability to customize the logic used in geocoding; the open-source nature of the tool also allows more general customization or extension by users. This tool provides researchers the ability to easily prepare EM-DAT data for analysis with geophysical, hydrological, and other geospatial variables.

cs.DB↗

arXiv2019

Aquaplanet Models on Eccentric Orbits: Effects of Rotation Rate on Observables

Rotation and orbital eccentricity both strongly influence planetary climate. Eccentricities can often be measured for exoplanets, but rotation rates are currently difficult or impossible to constrain. Here we examine how the combined effects of rotation and eccentricity on observed emission from ocean-rich terrestrial planets can be used to infer their rotation rates in circumstances where their eccentricities are known. We employ an Earth climate model with no land and a slab ocean, and consider two eccentricities ($e=0.3$ and 0.6) and two rotation rates: a fast Earth-like period of 24 hours, and a slower pseudo-synchronous period that generalizes spin synchronization for eccentric orbits. We adopt bandpasses of the Mid-Infrared Instrument on the James Webb Space Telescope as a template for future photometry. At $e=0.3$ the rotation rates can be distinguished if the planet transits near periastron, because slow rotation produces a strong day-night contrast and thus an emission minimum during periastron. However, light curves behave similarly if the planet is eclipsed near periastron, as well as for either viewing geometry at $e=0.6$. Rotation rates can nevertheless be distinguished using ratios of emission in different bands, one in the water vapor window with another in a region of strong water absorption. These ratios vary over an orbit by $\lesssim\!0.1$ dex for Earth-like rotation, but by 0.3--0.5 dex for pseudo-synchronous rotation because of large day-night contrast in upper-tropospheric water. For planets with condensible atmospheric constituents in eccentric orbits, rotation regimes might thus be distinguished with infrared observations for a range of viewing geometries.

astro-ph.EP↗

arXiv2017

Weakening and Shifting of the Saharan Shallow Meridional Circulation During Wet Years of the West African Monsoon

The correlation between increased Sahel rainfall and reduced Saharan surface pressure is well established in observations and global climate models, and has been used to imply that increased Sahel rainfall is caused by a stronger shallow meridional circulation (SMC) over the Sahara. This study uses two atmospheric reanalyses to examine interannual variability of Sahel rainfall and the Saharan SMC, which consists of northward near-surface flow across the Sahel into the Sahara and southward flow near 700 hPa out of the Sahara. During wet Sahel years, the Saharan SMC shifts poleward, producing a drop in low-level geopotential and surface pressure over the Sahara. Statistically removing the effect of the poleward shift from the low-level geopotential eliminates significant correlations between this geopotential and Sahel precipitation. As the Saharan SMC shifts poleward, its mid-tropospheric divergent outflow decreases, indicating a weakening of its overturning mass flux. The poleward shift and weakening of the Saharan SMC during wet Sahel years is reproduced in an idealized model of West Africa; a wide range of imposed sea surface temperature and land surface albedo perturbations in this model produce a much larger range of SMC variations that nevertheless have similar quantitative associations with Sahel rainfall as in the reanalyses. These results disprove the idea that enhanced Sahel rainfall is caused by strengthening of the Saharan SMC. Instead, these results are consistent with the hypothesis that the a stronger SMC inhibits Sahel rainfall, perhaps by advecting mid-tropospheric warm and dry air into the precipitation maximum.

physics.ao-ph↗

arXiv2016

Improving Energy-Based Estimates of Monsoon Location in the Presence of Proximal Deserts

Two theoretical frameworks have been widely used to understand the response of monsoons to local and remote forcings: the vertically integrated atmospheric energy budget and convective quasi-equilibrium (CQE). Existing forms of these frameworks neglect some of the complexities of monsoons, such as the shallow meridional circulations that advect dry air from adjacent deserts into the middle and lower troposphere of monsoon regions. Here the fidelity of energy budget and CQE theories for monsoon location is assessed in a three-dimensional beta-plane model with boundary conditions representative of an off-equatorial continent with a tropical grassland and an adjacent subtropical desert. Energy budget theories show mixed success for various SST and land surface albedo forcings, with the ITCZ being collocated with the energy flux equator but a non-monotonic relationship existing between ITCZ latitude and cross-equatorial energy transport. Accounting for the off-equatorial position of the unperturbed energy flux equator is shown to be important when a linearization of meridional energy transports is used to quantitatively diagnose ITCZ location. CQE theories that diagnose ITCZ location based on the subcloud moist static energy maximum are shown to have large biases; accounting for convective entrainment of dry air by using a lower-tropospheric mean moist static energy provides a more correct diagnosis of ITCZ location. Finally, it is shown that although ITCZ shifts can be diagnosed by modified CQE and energy budget frameworks, neither can be used in a quantitatively prognostic capacity due to unpredictable feedbacks that are often larger than the imposed forcing.

physics.ao-ph↗