A. Surjalal Sharma

Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10- to 30-d lead times, an interval that is generally considered to be a predictability gap. The temporal correlation of rainfall forecasts is improved by up to 0.28 in this time range. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point toward a future of combining dynamical and data-driven forecasts for Earth system prediction.

Whitepaper #072 in the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033. Main topics: space weather applications; basic research. Additional topics: space weather research/operations/research loop; other basic research; other space weather; system science.

Whitepaper #368 in the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033. Main topics: basic research. Additional topics: planetary magnetospheres; fundamental space plasma theory; space-based missions/projects.

Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode which determines the active and break phases of the monsoon, and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the rainfall portion corresponding to MISO.Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO using a novel method. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics, and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve significant improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10–30 day lead times, an interval that is generally considered as a predictability gap. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point towards a future of combining dynamical and data-driven forecasts for Earth system prediction.

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

The Advanced Baseline Imager (ABI) onboard GOES-16 provides high quality visible and near-infrared (VNIR) imagery data. In this paper, radiometric performance of the GOES-16 ABI multiple VNIR bands (B1, B2, B3, B5, and B6) are evaluated over the Sonoran Desert by comparing measurements with Suomi National Polar-Orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) equivalent bands M3, M5, M7, M10, and M11 and Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) equivalent bands b3, b1, b2, b6 and b7, respectively. Spectral Band Adjustment Factor (SBAF) derived from Hyperion data over Sonoran Desert is used. The large viewing angle at the Sonoran Desert by ABI (56.34º) and a lack of comprehensive BRDF model at such viewing geometry and angular dependence of atmospheric scattering are the main challenges for the comparison. To address this issue, a radiative transfer modeling-based (RTM) method to account for atmospheric effects is developed to facilitate the comparison. The time series trending and mean bias of reflectance ratio between ABI and VIIRS and MODIS measurements over the Sonoran Desert after SBAF and BRDF corrections are derived to evaluate the radiometric performance of ABI w.r.t. VIIRS and MODIS. The analysis shows that the radiometric biases of the first four VNIR channels of GOES-16 ABI are all within 6% in comparison to the matched channels of VIIRS or MODIS after applying the RTM correction. Only the reflectance consistency of the VIIRS M11 and MODIS b7 is ~0.09, which dues to the center wavelength difference and different H2O absorption effects. The analysis also detects ~6% drop in the radiometric bias of GOES-16 ABI 0.64 μm channel after April 23, 2019, which can be traced to the implementation of a correction of the ABI B2 calibration coefficient around this date.

This Letter presents an analysis of the sunward Poynting flux throughout magnetosheath and foreshock (directly measured by INTERBALL-1, CLUSTER-4 and DOUBLE STAR TC1) and its correlation and...

It is shown by using a combination of Vlasov and test particles simulations that the electron distribution function resulting from energization due to Upper Hybrid (UH) plasma turbulence depends critically on the closeness of the pump wave to the double resonance, defined as ω ≈ ω_UH ≈ nω_ce, where n is an integer. For pump frequencies, away from the double resonance, the electron distribution function is very close to Maxwellian, while as the pump frequency approaches the double resonance, it develops a high energy tail. The simulations show turbulence involving coupling between Lower Hybrid (LH) and UH waves, followed by excitation of Electron Bernstein (EB) modes. For the particular case of a pump with frequency between n = 3 and n = 4, the EB modes cover the range from the first to the 5th mode. The simulations show that when the injected wave frequency is between the 3rd and 4th harmonics of the electron cyclotron frequency, bulk electron heating occurs due to the interaction between the electrons and large amplitude EB waves, primarily on the first EB branch leading to an essentially thermal distribution. On the other hand, when the frequency is slightly above the 4th electron cyclotron harmonic, the resonant interaction is predominantly due to the UH branch and leads to a further acceleration of high-velocity electrons and a distribution function with a suprathermal tail of energetic electrons. The results are consistent with ionospheric experiments and relevant to the production of Artificial Ionospheric Plasma Layers.

Establishing the predictability of the climate system beyond the weather time scale of about 10 days is essential for extended range prediction. To overcome the limitation imposed by deterministic chaos on long‐range prediction, we exploit the near‐oscillatory behavior of monsoon intraseasonal oscillation (MISO) and other such phenomena in the tropical climate. These are nonlinear oscillations in the time range of 30–60 days. Based on the phase space reconstruction method of nonlinear dynamical systems theory, we have developed a prediction model using the time series of MISO. We demonstrate that the Indian monsoon intraseasonal oscillation can be predicted with more accuracy at extended range. The phase space reconstruction model performs better than the Climate Forecast System of the National Centers for Environmental Prediction in predicting the MISO. Our results show that intraseasonal variability can be modeled as a low‐dimensional dynamical system and demonstrate extended predictability of climate.