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Robert Baker
Robert Baker

Forecast


The Economic and Revenue Forecast Council must approve the official, optimistic and pessimistic forecasts by an affirmative vote of at least four members. If the council is unable to approve a forecast before a required date the supervisor will submit the forecast without approval and the forecast shall have the same effect as if approved by the council.




forecast



To promote state government financial stability by producing an accurate forecast of economic activity and General Fund revenue for the legislature and the governor to be used as the basis of the state budget.


In mid-March 2023, the previous negative sea surface temperature anomalies in the central-eastern equatorial Pacific weakened further, and the basin is now in an ENSO-neutral state (as of 15 March 2023, the last observed value in the NINO3.4 region was 0.1 C). Key oceanic and atmospheric variables are now consistent with ENSO-neutral conditions. CPC issued a Final La Niña Advisory in March 2023, signaling the end of the event. Most models in the IRI ENSO prediction plume forecast SSTs in the ENSO-neutral state during Apr-Jun, and May-Jul, 2023. The likelihood of El Niño remains low during Apr-Jun (21%), increasing to 49% in May-Jul, and then becomes the dominant category from Jun-Aug onward with probabilities in the 60-67%. ENSO-neutral is the next most-likely category, with probabilities remaining in the range of 31-35%.


A purely objective ENSO probability forecast, based on regression, using as input the model predictions from the plume of dynamical and statistical forecasts shown in the ENSO Predictions Plume. Each of the forecasts is weighted equally. It is updated near or just after the middle of the month, using forecasts from the plume models that are run in the first half of the month. It does not use any human interpretation or judgment. This is updated on the third Thursday of the month.


The official CPC ENSO probability forecast, based on a consensus of CPC and IRI forecasters. It is updated during the first half of the month, in association with the official CPC ENSO Diagnostic Discussion. It is based on observational and predictive information from early in the month and from the previous month. It uses human judgment in addition to model output, while the forecast shown in the Model-Based Probabilistic ENSO Forecast relies solely on model output. This is updated on the second Thursday of every month.


The following graph and table show forecasts made by dynamical and statistical models for SST in the Nino 3.4 regionfor nine overlapping 3-month periods. Note that the expected skills of the models, based on historical performance, arenot equal to one another. The skills also generally decrease as the lead time increases. Thirdly, forecasts made at sometimes of the year generally have higher skill than forecasts made at other times of the year--namely, they are better whenmade between June and December than when they are made between February and May. Differences among the forecasts of themodels reflect both differences in model design, and actual uncertainty in the forecast of the possible future SST scenario.


The following plots show the model forecasts issued not only from the current month (as in the plot above),but also from the 21 months previous to this month. The observations are also shown up to the most recently completed3-month period. The plots allow comparison of plumes from the previous start times, or examination of the forecastbehavior of a given model over time. The first plot shows forecasts for dynamical models, the second for statisticalmodels, and the third for all models. For less difficult readability, forecasts are shown to a maximum of only the firstfive lead times. Below the third plot, we provide a mechanism for highlighting the forecasts of one model at a time againsta background of more lightly colored lines for all other models.


Only models producing forecasts on a monthly basis are included. This means that some models whose forecasts appear in the Experimental Long-Lead Forecast Bulletin (produced by COLA) do not appear in the table.


Once an IRI ENSO probability forecast has been published, the results stand even if a model reports an error and changes their data. When this happens we will update the plume with the model's correct values even though our forecast hasn't changed. What this means is that our forecast is always the same, but the underlying data may be different from what we based our forecast on.


The SST anomaly forecasts are for the 3-month periods shown, and are for the Nino 3.4 region (120-170W, 5N-5S). Often, the anomalies are provided directly in a graph or a table by the respective forecasting centers for the Nino 3.4 region. In some cases, however, they are given for 1-month periods, for 3-month periods that skip some of the periods in the above table, and/or only for a region (or regions) other than Nino 3.4. In these cases, the following means are used to obtain the needed anomalies for the table:


The anomalies shown are those with respect to the base period used to define the normals, which vary among the groups producing model forecasts. They have not been adjusted to anomalies with respect to a common base period. Discrepancies among the climatological SST resulting from differing base periods may be as high as a quarter of a degree C in the worst cases. Forecasters are encouraged to use the standard 1971-2000 period as the base period, or a period not very different from it.


Predictions of ENSO are probabilistic. The ensemble mean prediction is only a best single guess. On either side of that prediction, there is a substantial uncertainty distribution, or error tolerance. The second plot (Figure 2) shows the estimated probability distribution of the predictions, showing a set of percentiles within that distribution for each lead time. The distribution is modeled as a normal (Gaussian) distribution, so that the overall mean forecast represents the center, or 50 percentile, in the distribution. The overall mean is formed using equal weighting among all models. On either side, other percentile values are shown symmetrically, ranging from 1 to 99 and including some intermediate percentiles (5 and 95, 15 and 85, and 25 and 75). The plot enables a user to estimate the probability of the Niño3.4 SST anomaly to be greater or less than some critical value, or within some interval. If, for example, the 85 percentile falls at 1.8 C above average, the probability of the SST exceeding 1.8 C can be estimated at 15%. Probabilities for exceeding or not exceeding values not exactly on percentile line can be roughly interpolated by eye. The overall width of the probability distribution is derived from the historical skill of the hindcasts of the models, from 1982 to present, for the specific forecast start time and lead time. This method of defining the probability distribution represents one of two general approaches, the other approach being a direct counting of ensemble members within each of the percentile bands. This second approach assumes that the ensemble spreads of the models are true representations of the uncertainty. Individual model spreads have often been found to be somwehate narrower than they should be, although in multi-model ensembles this tendency has been shown to be milder or even eliminated.


This website is owned and maintained by the non-profit arm of the Sierra Avalanche Center. Some of the content is updated by the USDA avalanche forecasters including the forecasts and some observational data. The USDA is not responsible for any advertising, fund-raising events/information, or sponsorship information, or other content not related to the forecasts and the data pertaining to the forecasts.


Description: The global economy is projected to grow 6.0 percent in 2021 and 4.9 percent in 2022.The 2021 global forecast is unchanged from the April 2021 WEO, but with offsetting revisions. Prospects for emerging market and developing economies have been marked down for 2021, especially for Emerging Asia. By contrast, the forecast for advanced economies is revised up. These revisions reflect pandemic developments and changes in policy support. The 0.5 percentage-point upgrade for 2022 derives largely from the forecast upgrade for advanced economies, particularly the United States, reflecting the anticipated legislation of additional fiscal support in the second half of 2021 and improved health metrics more broadly across the group.


Amazon Forecast is a fully managed service that uses statistical and machine learning algorithms to deliver highly accurate time-series forecasts. Based on the same technology used for time-series forecasting at Amazon.com, Forecast provides state-of-the-art algorithms to predict future time-series data based on historical data, and requires no machine learning experience.


Time-series forecasting is useful in multiple fields, including retail, finance, logistics, and healthcare. You can also use Forecast to predict domain-specific metrics for your inventory, workforce, web traffic, server capacity, and finances.


You can use the APIs, AWS Command Line Interface (AWS CLI), Python Software Development Kit (SDK), and Amazon Forecast console to import time series datasets, train predictors, and generate forecasts.


With Amazon Forecast, you pay only for what you use. There are no minimum fees and no upfront commitments. The costs of Amazon Forecast depend on the number generated forecasts, data storage, and training hours.


The AWS Free Tier allows you a monthly limit of up to 10,000 time series forecasts, up to 10GB of storage, and up to 10 hours of training time. The Amazon Forecast free tier is valid for the first two months of usage.


The Land DA System is an offline version of the Noah Multi-Physics (Noah-MP) land surface model (LSM) used in the UFS via the Common Community Physics Package (CCPP), and it is currently being tested for operational use in GFSv17 and RRFS v2. Its data assimilation framework uses the Joint Effort for Data assimilation Integration (JEDI) software stack, which includes the Object-Oriented Prediction System (OOPS) for the data assimilation algorithm, the Interface for Observation Data Access (IODA) for observation formatting and processing, and the Unified Forward Operator (UFO) for comparing model forecasts and observations. 041b061a72


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